Abstract:

The interoperability of different assemblies is an essential factor affecting the popularization of wireless electric vehicle charging technology. Interoperability refers to the ability of the system to output rated power with specified efficiency when different ground and vehicle assemblies are paired. Aiming at the problems of incomplete evaluation criteria and low accuracy of evaluation results, this paper introduces the detuning factor and load factor representing the system resonance and load, respectively, and proposes an interoperability evaluation method based on two factors. Firstly, the limitations of the traditional evaluation method based on interface impedance are revealed. Secondly, the relationship between the detuning/load factors and system power, efficiency, and current limits are established. Then, according to the limits specified in national standards, the two factors’ interoperable region and its boundary functions are deduced, and the interoperability evaluation criterion based on the two factors is obtained. Experiments show that the proposed method can evaluate interoperability comprehensively from output power, system efficiency, and current limits. It not only solves the problem of incomplete evaluation criteria in the traditional method, but also improves the accuracy of interoperability evaluation results.

Abstract:

We proposed a smartphone positioning method by formulating the positioning problem as an HMM(hidden markov model,HMM) based on the proposed double-layer feature map consisting of visual and Wi-Fi features(vision-CSI map,V-CSI map) to solve the issue of low accuracy and poor stability in indoor environment. The V-CSI map is modeled by encoding CSI fingerprint features based on grid and visual features of sparse safety exits as well as association locations. The location problem based on the V-CSI feature map is solved as HMM problem in the method. First, the safety exit sign detection and visual feature matching are completed in the visual positioning phase, and the positioning results are employed to initialize and reinitialize the states of HMM. Subsequently, CSI fingerprint features are matched with that of the V-CSI map to complete the emission probability, and the state transition probability is computed by modeling motion constraint with Gaussian model. Finally, the optimal state is derived from the forward algorithm, and the position of the smartphone is readily determined from the weighted average of the closest states. In the experiment, the proposed method is verified in an office building of 6 000 square meters and an underground parking lot of 3 600 square meters respectively. Experimental results show that the average positioning error of the algorithm is about 1.0 m, and the time of a single positioning is about 170 ms in the two typical indoor scenes. Compared with only CSI positioning methods, the average positioning error of our proposed method is reduced by more than 56%. The outstanding performance of experimental results also illustrates that our proposed method can improve the accuracy and robustness of indoor positioning.

Abstract:

Traditional self-attention-based intrusion detection models have high time complexity in the calculation of attention values, and most intrusion detection models are oriented to static network environments. To address the above problems, we proposed an incremental intrusion detection model incorporating a sparse self-attention mechanism. First, we introduced a sparsity metric formula to reduce the time complexity, so as to alleviate the computational pressure of the model without affecting the detection performance of the model; Second, we constructed a dynamic example memory to alleviate the concept drift phenomenon of the model in incremental learning at the cost of a very small amount of memory space; Finally, we designed a category-balanced loss function, which is capable of enhancing the learning ability of the model for old-category samples without dynamically adjusting the model. Derivation and experiments prove that the sparse self-attention mechanism has lower time complexity and better classification effect. Compared with other schemes, the incremental learning mechanism shows a stronger ability to memorize old knowledge. The intrusion detection model has a better application prospect in the modern network environment.

Abstract:

The supporting and suspension parts of the catenary are the key infrastructure of the railway catenary. However, due to the long-term contact-induced vibration between pantograph and catenary, the components of the catenary are prone to various defects. Defect monitoring based on 4C images of catenary is the key to operation and maintenance tasks, which directly relates to the safety and reliability of railway transportation. Traditional manual inspection methods face challenges such as high labor intensity, low efficiency, and a high missed detection rate. Therefore, using image processing and artificial intelligence technology to detect defects automatically is a hot issue in this research field. The components of the catenary are diverse in type, and samples of each type of defect are scarce, making the existing deep learning methods that rely on a large number of training samples difficult to apply. To overcome this problem, we proposed a classification method, named defect detection based on variational autoencoder (DefVAE) for catenary. This method was based on the assumption that samples of the same class follow a Gaussian distribution in the feature space. It utilized the potential features from the output of a variational autoencoder (VAE) to determine the feature distribution of known defect samples and generated a large amount of defect data through resampling and decoding in the distribution space to compensate for the lack of samples. During the encoding phase, we incorporated auxiliary label information to increase the inter-class distribution distance in the latent feature space. During the defect classification phase, we adopted an image generation method assisted by sliding labels and combined the reconstruction error to improve the classification accuracy. The results of comparative and ablation experiments on open-source datasets and catenary 4C datasets show that DefVAE outperforms the baseline methods in most indicators on the open-source datasets and has high classification accuracy in the classification of catenary defects.

Abstract:

SAR can obtain 3D information of the target through multi-view observation. At present, multi-view SAR 3D reconstruction mainly assumes a side-looking trajectory. By constructing projective geometry equations, this type of method calculates the target offset between SAR images and derives the target height from the projective geometry. However, this type of method lacks a mathematical modeling process for the projection relationships and exhibits significant solving errors when the SAR trajectory includes squint and pitch angles. This paper analyzed the linear SAR trajectory, summarized the geometric relationship of the projection, and obtained the mathematical model of multi-view SAR projection. In the mathematical projection model, the relation matrix between pixel coordinates in the SAR imaging plane and target 3D space coordinates is called the essential matrix. The multi-view SAR mathematical model transforms the 3D reconstruction problem into a matrix inverse operation problem. The projection expression established by the essential matrix is transformed into homogeneous linear equations, and the singular value decomposition algorithm is used to solve the 3D coordinates of the target. Spaceborne SAR trajectory parameters were used for experimental simulation to verify the effectiveness of the proposed projection model and 3D reconstruction algorithm.

Abstract:

In order to solve the problem of limited linearity and frame rate in the ultra-large array infrared (IR) detector readout process, this paper proposed a high-speed and high-linearity readout method. The readout circuit noise characteristics were optimized by adopting an efficient correlated double sampling (CDS) method within pixels, and the CDS voltage was output to the column bus. By employing an alternating current (AC) enhancement technique, the parasitic capacitance of the column bus was rapidly settled, while an adaptive body-bias compensation method was applied at the column bus termination to eliminate the nonlinearity introduced by the pixel source follower. A comprehensive experimental verification was conducted in the readout circuit of an 8 192 × 8 192 array IR detector based on the 55 nm process at a low temperature of 110 K. The results show that in comparison with a traditional readout circuit, the output swing is increased from 2 V to 3.3 V, and the full-well capacity is increased from 4.3 Me- to 6 Me-. The row time is reduced from 20 μs to 2 μs, and the linearity is improved from 96.9% to 99.98%. The overall power consumption of the chip is 1.6 W, and single column power consumption of the readout optimization circuit is 33 μW in the accelerated readout mode and 16.5 μW in the nonlinear correction mode.

Abstract:

Heterogeneous graph neural networks have been extensively applied in data mining, information retrieval, and related domains. The metapath-based approach captures composite relationships in heterogeneous graphs by aggregating metapath neighborhood information. However, the selection of metapaths predominantly relies on prior knowledge, which may lead to the loss or overwriting of semantic information. Additionally, the use of attention mechanisms in feature aggregation incurs substantial computational overhead, and semantic confusion may arise as the network deepens or metapaths lengthen. To address these issues, a heterogeneous graph neural network that integrates multi-semantic view encoding is proposed. Firstly, all metapaths of fixed length are selected for the target node type, and subgraphs are constructed to extract corresponding semantic information. A lightweight mean aggregator is employed to obtain node representation under different metapath subgraphs, and specific relation encodings are learned for each type of metapath to combine with node representation. Subsequently, feature mapping is carried out and node features from different semantic views are fused to derive the final representation, which is applied to downstream tasks. Experiments conducted on five real-world datasets demonstrate that the proposed model more effectively captures semantic information in heterogeneous graphs, enhances node representation performance, and outperforms mainstream baseline models in node classification and link prediction tasks in most cases. The effectiveness of the model is further validated through ablation studies and parameter sensitivity analyses.

Abstract:

By removing weak and redundant learners, ensemble pruning can significantly enhance the efficacy of ensemble system-based facial expression recognition. However, existing methods primarily focus on either accepting or rejecting classifiers, which results in the retention of weak classifiers or the exclusion of pivotal ones when evaluation information is unreliable or incomplete. Additionally, relying on accuracy or diversity to evaluate the merits of the classifier is difficult to fully reflect the true performance of the classifier. Consequently, this paper proposed a three-way decision-based ensemble pruning algorithm (3WDEP) for facial expression recognition, which introduced a delayed acceptance strategy to address uncertainties in classifier assessment. Simultaneously, the concept of “predictive preference” was introduced, integrating the correlation measurement between prediction results and actual labels, as well as accuracy and recall metrics, so as to construct an ensemble pruning information system and comprehensively evaluate the classifier performance. The entropy weight method was used to determine the weight of the indicators, and combined with a three-way decision, the loss of classifiers under different decision options was considered to select the classifiers that contributed the most to the ensemble system for integration. Recall was utilized as both a benefit attribute and a cost attribute to optimize the ensemble pruning effect. Experimental results show that 3WDEP effectively improves facial expression recognition performance, and the accuracy improves by 3.32%, 9.39%, 1.26%, and 4.9% compared to the initial ensemble system on FER2013, JAFFE, CK+, and KDEF, respectively.

Abstract:

To address the problem of mismatch between upstream and downstream tasks exhibited by masked image modeling (MIM) methods in self-supervised representation learning, we proposed a novel pre-training model, called teacher-student complementary masked autoencoder, or in other words, the TSCAE model. The TSCAE model consists of two modules with complementary masked mechanisms, called teacher module and student module, respectively. The teacher module was designed as a Transformer-based structure to predict the masked region of an image (e.g., randomly masking 75% of the input image), while the student module employed a sole encoder to predict the remaining region of the same image (e.g., masking the remaining 25% of the input image). Meanwhile, to attain a richer visual representation from a large number of unlabeled data, the TSCAE model completed two kinds of upstream tasks, namely prediction and contrastive tasks. After that, the TSCAE model achieved the pre-training on COCO and Tiny-ImageNet datasets. The results demonstrate that across three public datasets including VOC and two private datasets, the proposed TSCAE model achieves better performance than the classical masked autoencoder (MAE) methods on downstream tasks such as image classification, object detection, and semantic segmentation. In particular, the TSCAE also alleviates the impact of the quality of the pre-training images on the visual representation learning encoder to a certain extent.

Abstract:

In response to the problems of low information utilization and poor anti-noise performance in existing recognition methods for radar emitter signals of complex systems, we proposed an ensemble deep neural network recognition method integrating multiple transform domain features of radar emitter signals. Firstly, based on the three transform domain methods of bispectrum estimation, ambiguity function (AF), and Hilbert-Huang transform (HHT), we processed the emitter signals, extracted, and transformed the signal’s rectangular integral bispectrum feature, AF orthogonal slice feature, and Hilbert marginal spectrum feature into two-dimensional feature images with stronger expressiveness and interpretability. Then, we constructed a fusion recognition model framework based on ResNet18 + multilayer perceptron (MLP), took multiple ResNet18 as base learners to perform primary recognition on the datasets of three transform domain features, and obtained feature vectors represented by probabilities. Finally, we conducted fusion learning on the feature vectors via the MLP and output the final signal category information. The experimental results show that the proposed method maintains an overall average recognition rate of above 99.23% for six classes of radar emitter signals at a signal-to-noise ratio (SNR) of 0 dB. Even in the low SNR environment of -4 dB, the recognition rate remains stable at above 96.54%. The results verify the effectiveness and better performance of the proposed method.

Abstract:

Dynamic cardiac magnetic resonance imaging (CMRI) is an important tool for noninvasive assessment of cardiovascular disease. In dynamic CMRI, a low-rank tensor recovery method is usually employed to explore the sparsity of dynamic magnetic resonance images; however, different modes along the tensor have different low-rank properties. The studies have found that the nonlocal self-similarity mode along the tensor can best improve the reconstruction quality of dynamic CMRI. Therefore, this paper proposes an optimal low-rank matrix recovery (OLRMR) model with matrix sparsity based on the nonlocal low-rank (NLR) method by treating each set of similar blocks extracted from a high-dimensional image as a matrix. The model uses the weighted Schatten p-norm as the rank proxy function and was solved using the alternating direction multiplier method (ADMM) and a fast soft-threshold iterative algorithm. Experimental results based on the cardiac dataset show that the OLRMR algorithm is more effective in improving the quality of the reconstructed image than the BCS, k-t SLR, and k-t LRTC algorithms and can better keep the detail of the image and the edge contour information intact. The experimental results also show that OLRMR improves the reconstruction speed by a factor of 2.6-3 over k-t LRTC.

Abstract:

To significantly enhance the response speed of spacecraft formation coordination and meet strict time-window requirements for formation missions, this paper develops a novel prescribed-time performance-guaranteed control framework. This framework effectively addresses three critical challenges prevalent in formation systems: limited perception range, actuator saturation constraints, and inter-agent collision avoidance requirements. First, by integrating error transformation technique with sliding mode control, a control Lyapunov function condition is constructed. This design not only ensures the system meets strict timing requirements for formation tasks, but also guarantees prescribed transient response characteristics and steady-state performance metrics. Second, through the establishment of high-order control barrier functions, precise regulation of relative distances between adjacent spacecraft is achieved, maintaining formation communication topology connectivity while effectively preventing collision risks. Furthermore, this study employs quadratic programming to solve for optimal control inputs, realizing multi-objective coordinated optimization of prescribed-time convergence, topology connectivity maintenance, and collision avoidance control under actuator saturation constraints. To validate the effectiveness of the proposed control framework, systematic performance verification is conducted through numerical simulations. The simulation results fully demonstrate the reliability and superiority of the proposed control scheme in satisfying all specified constraints.

Abstract:

Video re-localization aims to localize a moment that semantically corresponds to a given query video from an untrimmed reference video. This task not only meets the actual browsing needs of users but also plays an important role in various application scenarios. Since videos contain richer information compared to other data forms like images and text, accurately identifying the target moment in a long video and determining its temporal boundaries are significantly challenging. This paper regarded the video re-localization task as a sequential decision-making process and applied reinforcement learning to achieve efficient and accurate localization. Specifically, this paper proposed an agent-guided localization network (AGLN), which trained an agent to progressively refine temporal boundaries of the localized moment based on the learned policy, thereby finding the most relevant moment to the query video. Additionally, AGLN combined reinforcement learning with supervised learning in a multi-task learning framework, aiding the agent in more effectively exploring the environment and learning the optimal policy. Experimental results on the ActivityNet-VRL dataset demonstrate that AGLN outperforms existing methods in the video re-localization task. The average retrieval accuracy of AGLN is 25.9%, which is 0.2 percentage points higher than the current optimal method.

Abstract:

The existing all-symbol locally repairable codes with unequal availability have limited parameter values, and low code rates. In order to solve the problems above, this paper constructs a class of all-symbol locally repairable codes with unequal availability based on saturated orthogonal arrays, which achieves higher code rates. Specifically, the association matrix is generated based on the saturated orthogonal array, and the matrix transformations as well as the Kronecker product operations are performed on the association matrix to generate all-symbol locally repairable codes with high availability of information symbols and unequal availability of information bits. Theoretical analyses show that all-symbol locally repairable codes with high availability of information symbols have flexible parameter values and are optimal in dimension and code length at locality r=2. Compared with the existing all-symbol locally repairable codes, the constructed all-symbol locally repairable codes with unequal availability of information bits perform better in terms of code rate.

Abstract:

To enhance the microstructure and mechanical properties of laser cladded coatings, this study focuses on Ni60/WC coatings and proposes a resistance heating heat treatment (RHHT) process using pulsed direct current. First, RHHT experiments were carried out on Ni60/WC coatings for 1 h and 2 h, respectively, under the current density of 3.33 A/mm² .Subsequently, SEM and XRD were used to analyze the phase composition and microstructure transformation of coatings, and mechanical properties of the specimens before and after RHHT were tested. The results show that due to the selective heating effect, the electric current bypasses the hard phases within the coating, generating localized high temperatures which cause the secondary decomposition of WC. The Ti, Cr, and C atoms dissolved in γ-(Ni, Ti) diffuse under the influence of electric current, and the phase transformation in the coating proceeds in the direction of increasing electrical conductivity. Moreover,due to the electric current reducing the nucleation energy barrier in combination with rapid cooling, the nucleation rate was significantly increased and the grain sizes were reduced by approximately 99% which resulted in fine γ-(Ni, Ti) grains after the RHHT. By comparing the mechanical properties of the coatings before and after RHHT, it was found that the microhardness, fracture toughness and wear properties were effectively improved. The direct current, through its selective heating effect and athermal effects, circumvented the grain defects, and enhanced atomic diffusion ability, increased the nucleation rate, and refined the grains in the coating.

Abstract:

Existing rumor detection methods mainly rely on text semantic features and network propagation features, but the source tweets dominated by short texts can easily lead to insufficient semantic features, and the propagation tree used to extract propagation features can generate a large amount of data. To solve these problems, we proposed a rumor detection method, namely multi-view graph neural network, which integrated rich semantics and global propagation. This model used source texts to get structural semantic relationships, utilized external knowledge to extract potential semantic relationships, and got the global propagation relationship among users by source tweets and their response users. Finally, it automatically learned the feature weights of different views through the attention fusion mechanism, achieving adaptive information fusion and improving the accuracy of rumor detection. Besides, it adopted Word2Vec to supplement the content semantics of source tweets. Experimental results show that using source texts, external knowledge, and response users to construct graphs, respectively, can effectively capture rich semantic information and concise global propagation relationships. The model outperforms a series of baseline models on the public datasets Twitter15 and Twitter16, with the accuracy rates of 90.2% and 90.8%, respectively. The analysis results from the ablation experiment show that the proposed method can comprehensively capture rich semantic features of the source tweets and effectively obtain the global propagation relationship in a concise manner, so as to improve the accuracy of rumor detection.

Abstract:

Hyperspectral image (HSI) classification is a challenging task in the field of remote sensing, because the HSI has high spectral dimensionality and low spatial resolution, which makes it difficult to fully extract the spatial-spectral features of hyperspectral images in the classification task. Aiming at solving the problems of the existing convolutional neural network (CNN)-based HSI classification models, such as large parameter size, high computational cost and low classification accuracy, a lightweight network hyperspectral image classification model based on attention mechanism (AMLW-CNN) is proposed in this paper. In order to enhance feature extraction ability of the network, the spatial-spectral feature extraction module is designed based on two multiscale extraction modules. In addition, we use the residual structures to connect the convolutional layers of spatial feature extraction module and incorporate the attention mechanism to enhance the extraction of useful features. Furthermore, to reduce the number of model parameters, an asymmetric convolution and a depthwise separable convolution are introduced to replace the 3D and 2D convolution kernels, respectively. The experimental results show that classification accuracy of AMLW-CNN is better than that of the comparison algorithms, with lower computational complexity and higher robustness. The overall classification accuracies on the datasets of Indian Pines, Salinas and Pavia U has attain 98.5%,99.8% and 99.9%, respectively.

Abstract:

To reduce the risk of instability and wrinkling in the reducing and thickening extrusion process for aluminum alloy pull rod, and improve the production efficiency of the pull rod, a non-isothermal hot extrusion forming process was proposed, in which the fixture clamps the non-deformation zone of the tube blank, with only the extrusion die preheated and no cooling required for the tube blank. Firstly, the non-isothermal hot extrusion forming process of 2A12 aluminum alloy pull rod with diameter of  65 mm, thickness of 5 mm and target thickening ratio of 1.6 was analyzed based on finite element simulation, and then the effects of extrusion process parameters on forming quality were investigated. Secondly, under the optimized process parameters determined by simulation, the non-isothermal hot extrusion experiment of the pull rod was conducted, validating the effectiveness of the numerical simulation. The study demonstrated excellent agreement between simulation results and experimental data. The non-isothermal hot extrusion process eliminated bending deformation at the tube ends. Furthermore, the gradient-decreasing temperature distribution at the leading end of the tube along the extrusion direction helped maintain the material strength in the non-deformation zone, thereby reducing the risk of instability and wrinkling. Under the extrusion process parameters of friction coefficient of 0.05~0.3, extrusion speed of 1.6~7.6 mm/s and die temperature of 410~470 ℃, the friction coefficient exhibited a significant influence on the forming quality of the pull rod, while the extrusion speed and die temperature had relatively minor effects. However, none of these parameters showed a notable impact on the wall thickness uniformity in the thickened zone of the pull rod. As the friction coefficient decreased, the risk of wrinkling of the pull rod decreased, and the wall thickness of the thickened zone increased with a maximum increase of 56.5%. The findings of the study offer an innovative methodology for enhancing the extrusion forming quality of aluminum alloy pull rod.

Abstract:

The permafrost in northeastern China constitutes an important component of the Xing’an-(trans)Baikal permafrost, exhibiting characteristics of both high latitude and high altitude permafrost. The presence and dynamics of permafrost directly impact the ecological environment of the regional cold zone, water-carbon cycles, cold region engineering design, and operations. Currently, empirical and semi-empirical models based on thermal boundary conditions tend to overestimate the areal extent of permafrost and insufficiently consider environmental factors beyond temperature. To more accurately delineate the regional distribution of permafrost, this paper obtained the spatial variation characteristics of zonal and non-zonal factors influencing regional permafrost distribution through regional surveys and data integration and employed the boosted regression tree model for simulation and analysis. The results indicate that among the zonal factors, latitude, longitude, and altitude contribute 45.3%, 42.4%, and 12.3%, respectively. Among the non-zonal factors, temperature (including freezing and thawing indices), precipitation, soil-water conditions, snow cover, and vegetation contribute 46.4%, 18.9%, 13.1%, 12.5%, and 9.1%, respectively. This analysis clarifies the contributions of environmental factors to the development and dynamics of Xing’an-(trans)Baikal permafrost. Compared with a classification and regression decision tree, the boosted regression tree model achieves an accuracy of 0.91. This study provides data support and reference for regional permafrost research and related fields.

Abstract:

In the complex application environment of wireless sensor networks (WSN), in order to resist the selective forwarding attack and dishonest recommendation attack launched by malicious nodes and improve the safety performance of the network, this paper proposes a malicious node identification strategy based on artificial bee colony (ABC) considering reputation (CR-ABC) in WSN. By utilizing a fuzzy trust model (FTM) and integrating a fuzzy comprehensive evaluation mechanism, the paper calculates the comprehensive trust value of nodes based on three influencing factors: communication features, data attributes, and physical attributes to improve the detection accuracy of the reputation model. The paper introduces the suggested deviation function and the interaction index deviation function and uses the ABC algorithm to optimize the FTM, aiming to ensure that the system still maintains a higher identification rate and a lower misjudgment rate when there are too many malicious nodes. The simulation results show that the identification rate of CR-ABC for selective forwarding attacks can reach over 90%, and the misjudgment rate for normal nodes can be reduced to less than 6%. For dishonest recommendation attacks, even if the number of dishonest nodes reaches 50%, CR-ABC still maintains a high identification performance, which can effectively improve the security and reliability of WSN in complex environments.

Abstract:

The face image inpainting technology can generate a complete face image by repairing the occluded area of the face, which has important application value in fields such as criminal investigation and security protection. However, the inpainting results of the existing methods often exhibit artifacts such as fuzzy texture and distorted face structure. Therefore, based on the generative adversarial network (GAN) framework, this paper proposed an occluded face restoration network fusing edges and key points. Firstly, the proposed network used the structural forest edge restoration network to complete the structural forest edge map occluding the face image to obtain more description information of the face details. Then, it used the key point prediction network to locate 68 key points of the occluded face to obtain the topological structure information of the face image. Finally, it took the structural forest edge map and the key points of face obtained by the above two networks as prior information, restored the occluded face area by the face image inpainting network, and generated a complete face image. The experimental results on the CelebA-HQ dataset show that the face images restored by the proposed algorithm have finer texture details and more reasonable topological structures of faces. Under different occluded areas, the PSNR and SSIM of the proposed algorithm are higher than those of the comparison algorithm. Compared with that of GatedConv, EdgeConnect, and LaFIn algorithms, when the mask ratio is 50%, the PSNR of the proposed algorithm increases by 36.8%, 25.8%, and 29.3%, respectively, while the SSIM increases by 19.5%, 12.2%, and 12.2%.

Abstract:

With the advent of the era of big data, network structures are becoming more and more complex, and exploring the community structure of complex networks holds great significance in understanding their function and organization mechanisms. Many studies have been conducted for community detection, among which the label propagation algorithm (LPA) has a near linear time complexity and is applicable for large-scale complex networks. However, it has excessive randomness and relatively low accuracy. This paper proposed a collective influence-based label propagation algorithm (CILPA) for discovering overlapping community structures. CILPA introduced collective influence as a global indicator, redefined the node importance by integrating the node’s own information and global network information, and fixed node update order according to node importance to improve the algorithm’s stability. In the label propagation process, a label selection strategy was designed, and the adaptive filtering factor were set to prevent the interference of wrong labels, thereby improving the accuracy and robustness of the algorithm. Finally, experiments were conducted on artificial and real networks with different scales, complexities, and overlap rates. The results show that the modularity and normalized mutual information of CILPA are superior to those of mainstream algorithms such as COPRA and SLPA, with a smaller standard deviation. This indicates that the proposed method possesses both effectiveness and stability in overlapping community detection, providing a reliable method for the analysis of overlapping communities in large-scale complex networks.

Abstract:

In order to address the challenge of designing reasonable offloading decisions for mobile edge computing (MEC) in multi-user environments, which leads to load imbalance, excessive total latency, and response delays, this paper proposed a latency-sensitive heuristic task offloading method. Firstly, to address the issues of limited computational resources and insufficient battery power of edge devices during computation task processing, the paper introduced an edge server-centric offloading paradigm and established a system model and a latency optimization model. Subsequently, it introduced an improved proximal policy optimization algorithm (I-PPO), which extended the offline training process, designed a reward mechanism that considers the impact of multi-agent decisions, and incorporated global information based on specific agents into the features, enabling the algorithm to be suitable for multi-user environments. Furthermore, building upon I-PPO, the paper introduced task priority scheduling decisions into the task offloading execution process, resulting in the development of a latency-sensitive lightweight heuristic task offloading algorithm, denoted as HTAI. This further optimized system latency and enhanced user satisfaction. Simulation experiments demonstrate that the I-PPO algorithm proposed in this paper, compared to similar algorithms, effectively improves convergence speed, optimization capability, and robustness, and it can be applied in multi-agent environments. Moreover, the algorithm proposed herein outperforms other algorithms in terms of total system latency and edge server load balance, exhibiting strong stability.

Abstract:

In order to realize the demand for low-cost and long-term detection of large-scale building groups and to expand the identification range of the sensor, this paper proposed a metal crack sensor based on passive chipless radio frequency identification (RFID) technology. Based on the influencing factors such as cross-polarization and operating bandwidth and a large amount of simulation data on the HFSS platform in the early stage, the paper designed a sensor model with excellent detection performance. Horizontal, vertical, and diagonal cracks were constructed, and electromagnetic excitation of the plane wave was used to test the influence of different crack shapes on the sensing and detection. The position of various types of cracks was changed, and changes in the electric field of the resonant cavity, current, and response amplitude were analyzed to determine the optimal identification range of the sensor. The results show that the average amplitude deviation of the sensor’s response to structural damage detection is 5 dB in the ultra-high frequency band. The change of crack position will affect the surface current distribution, which will change the response amplitude, while the structural damage response is detuned compared with the crack-free response, and the change of crack position does not affect the detectability of cracks. The sensor is capable of detecting cracks in different directions at any position on the surface of an object over a full range, improving the identification range and enabling real-time monitoring of cracks with small positional variations at a high resolution.

Abstract:

A fault tolerant control scheme based on integral sliding mode surface is developed for spacecraft attitude stabilization in the presence of actuator faults, misalignments, magnitude saturation and external disturbances simultaneously. This approach is based on a novel integral-type sliding mode control strategy to compensate for these un-desired issues without controller reconfiguration. Especially, it guarantees the reachability of the system states by involving adaptive control technique to relax the boundary information in advance. A sufficient condition for the controller to accommodate magnitude saturation is also presented and then the fault tolerant attitude control system can be guaranteed theoretically to be asymptotically stable by using Lyapunov method. Numerical simulation results shows that the proposed control law can quarantee the stability of the spacecraft attitude control system in the presence of actuators' failures, and it has good robust performance.

Abstract:

To investigate the ground motion intensity measures suitable for evaluating high-rise structures under near-fault ground motions with pulse-like effect, this paper proposes a new ground motion intensity measure considering period elongation effect and higher mode effect based on acceleration spectrum. Taking two high-rise reinforced chimney structures (120 m and 240 m) as research objects, the correlation between damage indices (ParkAng damage index, maximum inter-story drift ratio, maximum structural curvature, maximum floor acceleration, and maximum roof displacement) of high-rise structures and 37 ground motion intensity measures was studied under near-fault ground motions using OpenSEES. Results show that the proposed intensity measure was the optimal index in predicting the ParkAng damage of high-rise concrete structures under near-fault ground motions. High correlation between velocity-related intensity measures and structural damage index was observed. As the structural period increased, the correlation between damage indices and displacement-related intensity measures was improved. Besides, peak ground acceleration had limitations in characterizing the deformation and failure of high-rise structures, but it could be used to analyze the seismic performance of non-structural components. The research results can provide reference for selecting proper measures and structural damage indices to evaluate the seismic performance of high-rise structures under near-fault ground motions.

Abstract:

It is well known that unmanned aerial vehicle (UAV) is more and more widely applied in military and civil areas. In order to play the better role of UAV, it is needed to utilize multi UAVs cooperative formation to accomplish cooperative reconnaissance, combat, defense and spraying pesticides and other tasks. The multi UAVs cooperative formation control technology mainly contains the following key techniques: data fusion technology, sensing technology, task allocation technology, path planning technology, formation control technology, communication network technology and virtual/physical verification platform technology. Firstly, summarize the research and development of key technologies worldwide. Then, the classification for multi UAVs formation control methods is mainly investigated, and the problems about formation design and adjustment, formation reconfiguration are summarized. Finally, the challenges and future development for multi UAV cooperative formation are prospected. Research shows: at present, the theory of multi UAV formation flight has acquired fruitful results, while the real cooperative formation flight test can only be implemented in the simple communication environment. The real time performance for task allocation and path planning is not high. The robustness of control methods to cope with the unexpected situation is low. The cooperative sensing ability for multi UAV with multi sensor is insufficient. The simulation of the entity is lacked. Breaking through the above key technologies, carrying out the cooperative formation flight of multi UAV in complex sensing constraints and complex communication environment, putting forward more effective control method and carrying out the UAV physical formation flying test so that the UAV can finish the task better may be the future research directions.

Abstract:

The advent of Industry 4.0 has spawned the widespread application of digital twin technology, providing digital solutions for intelligent manufacturing and product life-cycle management. In the field of civil engineering, the enhancement of digital disaster prevention and civil structure management is a critical component in the development of future smart cities. On one hand, the establishment of precise and reliable digital twins of real-life civil structures can facilitate disaster prevention from extreme hazards, as well as identify and warn against potential risks. On the other hand, digital twins lay the foundation for technological advancements in the digital construction and management of future cities. This study first categorizes the fundamental concepts and developmental stages of digital twin technology. Then, the acquisition of twining data and construction of digital twins for civil structures are systematically summarized. Building on this foundation, a comprehensive review and outlook is presented on the application of digital twin technology in civil engineering, encompassing the operation and maintenance of structures, disaster simulation and digital twin cities.

Abstract:

The development of advanced nano-integrated circuit processes has led to a decreasing threshold charge in microelectronic devices, resulting in an increased rate of soft errors caused by single-event effects in digital circuits. To enhance the radiation resistance of standard cells in integrated circuits, this paper proposes a NAND gate structure that is resistant to single-event transients (SETs). In the triple well process, by shorting the substrate and source of each NMOS transistor in the pull-down network, the radiation resistance of the NAND gate was effectively improved, and the hardening of the proposed NAND gate became more effective as the number of inputs increased. Particle incidence simulation experiments were performed by Sentaurus TCAD software in hybrid simulation mode. For the NMOS transistor connected to the output node, the three-dimensional physical model that has been calibrated by the process was used, and the Spice model provided by the manufacturer was adopted for other MOS transistors. Simulation results show that the proposed two-input NAND in 40 nm process could reduce the output voltage fluctuation amplitude in three-input cases at the linear energy transfer (LET) value of incidence particle of 10 MeV·cm²/mg. Besides, the effect of immunity to single particle incidence was achieved in the input mode with N₂ transistor closed. For the hardened three-input NAND gate, the output voltage disturbance could be reduced by up to 85.4% even in the “worst case”. Therefore, the proposed hardening method for NAND gate has a significant effect against SET.

Abstract:

Due to the complex background of ship targets and much irrelevant interference in visual images, it is difficult to conduct ship detection. In addition, there are few datasets for multi-category ship detection and the samples are often unbalanced, which makes the ship target detection performance degraded. Considering the ship detection background interference, an improved YOLOv3 model was proposed by introducing SimAM attention mechanism, which was used to enhance the weight of the ship target in the extracted features and suppress the weight of background interference, thus improving the model detection performance. Meanwhile, strong real-time data augmentation was applied to improve the unbalanced distribution of sample scales, and transfer learning was combined to improve the ship detection accuracy in the condition of a restricted number of samples. The visualization results of extracted features show that the improved model could suppress irrelevant background features, and the abilities of feature extraction and target localization were enhanced. Without introducing additional learnable parameters, the proposed model achieved 96.93% and 71.49% for mAP.5 and mAP.75 on the SeaShips dataset, and detection speed reached 66 frames per second, indicating a good balance between detection accuracy and efficiency. The improved model optimized the target features more effectively compared with the Saliency-aware CNN and eYOLOv3 models, resulting in an improvement of mAP.5 by 9.53% and 9.19%. The mAP.5 for ship type target detection on Singapore Maritime Dataset reached 81.81%, indicating that the proposed model has good generalization performance.

Abstract:

In the ultra-dense network environment, each access point is deployed in the hotspot area, which forms a complex heterogeneous network. Users need to choose the appropriate network to access, so as to achieve the best performance. Network selection problem is to choose the optimal network for the user, so that the user or network performance reaches the best. In order to solve the access selection problem of users in ultra-dense networks, we proposed an ultra-dense network access selection algorithm based on the improved deep Q network (DQN), considering network states, user preferences, and service types, and combining with load balancing strategies. First, by analyzing the influence of network attributes and user preferences on network selection, the appropriate network parameters were selected as the parameters of the access selection algorithm. Then, the problem of network access selection was modeled by Markov decision-making process, and the states, actions, and reward functions of the model were designed. Finally, the optimal network strategy was obtained by using DQN to solve the network selection model. In addition, the target function of traditional DQN was optimized to avoid overestimation of Q value by DQN, and a priority experience replay mechanism was introduced to improve learning efficiency. Simulation results show that the method could well solve the problem of overestimation of traditional DQN, accelerate the convergence of neural network, effectively reduce user congestion, and improve network throughput performance.

Abstract:

With the increasing scale of network, the accurate and real-time prediction of network flow is essential for traffic scheduling and routing design. However, due to the nonlinearity and uncertainty of network flow data, some traditional methods fail to achieve good prediction accuracy. Considering the complex spatialtemporal features of network flow, a novel network flow prediction method based on spatialtemporal features fusion (ST-Fusion) was proposed, combined with encoderdecoder architecture. First, the encoder was designed with two parallel feature channels: temporal and spatial channels. The temporal features were extracted by integrating gated recurrent unit (GRU) and self-attention mechanism, and the graph convolutional network (GCN) was used to extract the spatial features. Then, the temporal and spatial features extracted by the encoder were fused by using the bilateral gated mechanism. Finally, the fused features were input into the GRU-based decoder to generate prediction results. Experiments were conducted on three public datasets (GEANT, ABILENE, and CERNET) using evaluation metrics including MAE, RMSE, ACCURACY, and VAR. Experimental results showed that the ST-Fusion method achieved better performance in network flow prediction.

Abstract:

In order to accurately analyze the morphological characteristics and distribution properties of aggregates in the mixture, and to have a more comprehensive, in-depth, and specific understanding of aggregates, CT scanning, digital image processing, and three-dimensional geometric reconstruction technologies were used to reconstruct the real shape of aggregate particles. Five morphological characteristic parameters of aggregates were proposed, and a digital evaluation and experimental analysis were conducted on the morphological characteristics of three aggregates. The accuracy of the digital reconstruction method was validated and the morphological distribution characteristics of aggregate particles were analyzed. Additionally, the Pearson correlation method was utilized to analyze the correlations among the morphological parameters. The study demonstrates that the use of CT scanning technology and digital reconstruction technology can accurately restore the morphological characteristics of aggregate particles and obtain morphological parameters. There are significant distribution characteristics of the morphology of different particle sizes of the same aggregate. The three-dimensional needle-like index and three-dimensional texture index show little variation across different particle sizes. As the particle size increases, the variability of the morphology parameter values for aggregates decreases. The overall three-dimensional texture index follows a power-law distribution, and the complexity gradually decreases with the increase of particle size. Additionally, as the particle size increases, the three-dimensional edge angle gradually stabilizes. There is a strong negative correlation between three-dimensional edge angle and solid moment degree, as well as between solid sphericity and three-dimensional edge angle. Conversely, there is a strong positive correlation between sphericity of the actual shape and the three-dimensional texture index. Digital 3D reconstruction can accurately and comprehensively describes and analyzes the morphology and distribution characteristics of aggregates.

Abstract:

To improve the ductility of steelultra-high performance concrete (UHPC) composite structures, we proposed a type of demountable steelUHPC composite slab based on demountable shear connectors. The flexural tests for demountable steelUHPC composite slabs with different shear connection degrees were designed and completed. The failure mode, ultimate capacity, stiffness, cracking behavior, and relative slip of demountable steelUHPC composite slabs were analyzed and compared with those of steelUHPC composite slabs with welded shear connectors. The demountability of demountable steelUHPC composite slabs was discussed. The ultimate flexural capacity and flexural stiffness of demountable steelUHPC composite slabs were theoretically analyzed, and related calculation formulas were deduced. Results showed that the failure mode of demountable steelUHPC composite slabs was longitudinal horizontal shear bonding failure. Reducing the stud spacing could enhance the cooperative deformation capacity of demountable steelUHPC composite slabs, resulting in the improvement of their ultimate flexural capacity, stiffness at the elastic-plastic stage, and crack control ability. Different from the steelUHPC composite slabs with welded shear connectors, the steel slab and UHPC slab of the demountable steelUHPC composite slabs could be easily disassembled even in the condition of large deformation. The formulas for the ultimate flexural capacity and flexural stiffness of demountable steelUHPC composite slabs were derived. It was proposed that the height of UHPC slab should be reduced when calculating the flexural stiffness, and the reduction coefficient (β_U) was suggested to be 0.85 in serviceability state. The theoretical calculation results were in good agreement with the test results. The research results can provide theoretical basis for the design and application of steelUHPC composite slabs with demountable shear connectors.

Abstract:

In view of the weaknesses of poor computing and storage capabilities of edge devices, lightweight processing was carried out on the backbone network CSPDarkNet53 for feature extraction in the traditional YOLOv5 model, and a lightweight gesture recognition algorithm MPE-YOLOv5 was proposed to realize the deployment of the model in low-power edge devices. Considering the problem that it is difficult to identify large-scale transformation targets and tiny targets due to less feature extraction in lightweight model, efficient channel attention (ECA) mechanism was added to alleviate the loss of information after high-level feature mapping due to the reduction of feature channel. A detection layer for tiny targets was added to improve the sensitivity to tiny target gestures. EIoU was selected as the loss function of the detection frame to improve the positioning accuracy. The effectiveness of the MPE-YOLOv5 algorithm was verified on the self-made dataset and NUS-Ⅱ public dataset, and the MPE-YOLOv5 algorithm was compared with lightweight M-YOLOv5 algorithm and original YOLOv5 algorithm on the self-made dataset. Experimental results show that the model parameters, model size, and computational complexity of the improved algorithm were 21.16%, 25.33%, and 27.33% of the original algorithm, and the average accuracy was 97.2%. Compared with the lightweight model M-YOLOv5, MPE-YOLOv5 improved the average accuracy by 8.72% while maintaining the original efficiency. The proposed MPE-YOLOv5 algorithm can better balance between the detection accuracy and real-time reasoning speed of the model, and can be deployed on edge terminals with limited hardware.

Abstract:

To study the mechanical performance of concrete-filled double-skin steel tubular long columns under compressive and torsional loads, two ordinary circular steel tube reinforced concrete columns and two double-layered steel tube reinforced concrete columns were subjected to low-cycle reciprocating tests under pure torsion and torsion-compression loading using a developed Stewart six-degree-of-freedom loading platform. Based on the tests, the bearing capacity, torsional deformation, energy dissipation, and hysteresis performance of each specimen were compared and analyzed, and finite element parameter analysis was conducted. The study shows that both ordinary circular steel tube reinforced concrete columns and double-layered circular steel tube reinforced concrete columns have good torsional resistance. Compared with ordinary circular steel tube reinforced concrete columns, the initial stiffness and bearing capacity of double-layered steel tube reinforced concrete columns are slightly improved, the hysteresis curve is more full, and the energy dissipation capacity and ductility are greatly improved. Parameter analysis shows that when the steel content is constant, the larger the thickness ratio of the inner steel tube, the more beneficial it is for torsional resistance; and within a certain range of axial loads, the torsional resistance of steel tube reinforced concrete columns can be improved.

Abstract:

In order to give full play to the advantages of the high degree of industrialization of assembly and the excellent mechanical properties of steel-concrete composite structures, a kind of assembled double-slotted channel steel-concrete composite floor slab was proposed. Three groups of simply supported composite floor slab specimens were tested under four-point loading, and the mechanical properties of the composite floor slab under vertical static load were studied. The development law of floor cracks, deflection and strain (steel bar, steel beam, concrete slab) with load was analyzed. Based on the limit equilibrium method, the bearing capacity calculation formula considering the tensile membrane effect and stiffness strengthening coefficient was proposed. The results showed that the deformation of the composite slab is characterized by two-way slab. When the specimens are destroyed, the corner cracks and arc cracks appear on the top of the slab, the central area of the concrete slab bottom shows mesh cracks and oblique cracks extending to the corner, and the plastic bending of the double main girder occurs. When the center deflection of the floor reaches l₀/40, the load of the specimens is 327.63 kN, 436.92 kN and 406.12 kN respectively, and the bearing capacity of the composite floor is higher. The strain development of the steel bar is larger in the direction perpendicular to the steel beam and yields along the plastic hinge line. The calculation formula considering the tensile membrane effect and the stiffness strengthening coefficient is in good agreement with the test results, and the load-deflection curve of the floor is accurately predicted.

Abstract:

Considering the failure mechanism and weaknesses of traditional fabricated shear wall structures under strong earthquakes, a new type of fabricated shear wall with functions of energy dissipation and shock absorption was proposed. On the basis of model test and numerical simulation, seismic performance tests were carried out on four specimens with scale ratio of 1∶1.54 and shear span ratio of 1.52. Further analysis was conducted to investigate the effects of bolt number, axial compression ratio, and reinforcement ratio of edge members on the seismic performance of the new fabricated shear wall, including failure modes, hysteretic performance, bearing capacity, displacement ductility, stiffness degradation, and energy dissipation capacity. Test results show that the four specimens experienced shear compression failure, which was the same as the cast-in-place shear wall with the same shear span ratio. However, the proposed shear wall had better hysteretic performance and energy dissipation capacity, and the energy dissipation capacity was higher than that of the cast-in-place shear wall at the failure point. When the number of bolts decreased, the hysteretic performance of the new fabricated shear wall decreased, the wall deformation increased, while the bearing capacity remained almost unchanged. When the axial compression ratio or reinforcement ratio of edge members decreased, the bearing capacity decreased, and the ultimate displacement increased. Finally, the finite element model of the specimens was established by ABAQUS program. Comparisons of numerical results and test results showed a good agreement, verifying the correctness of the model, which can be applied to the analysis of the new fabricated shear wall.

Abstract:

To improve the construction efficiency of building envelope and solve the long-term problems of falling off and ignition of traditional external insulation systems, a kind of rock wool composite insulation external formwork (RWCIEF) system integrating insulation and building formwork was proposed. The RWCIEF structure from inside to outside was designed as follows: inner reinforcing layer, rock wool insulation core material, adhesive layer, insulation transition layer, and outer reinforcing layer. Taking Harbin as an example, the optimal thickness of rock wool insulation core material was determined based on the life cycle cost (C_lc). The feasibility of RWCIEF in engineering was explored by combining finite element analysis with theoretical calculation. The bending properties, construction bearing capacity, and stress and deformation under temperature effect of RWCIEF were calculated and analyzed. The influences of groove form, groove width, groove depth, and groove spacing on the bending properties of RWCIEF were discussed. Results showed that the theoretical calculation results of bending properties of RWCIEF were in good agreement with the finite element analysis results. The grooving treatment effectively improved the bending properties of RWCIEF. Considering the bending properties, thermal characteristics, and processing angle, groove forms of symmetrical cross grooves or symmetrical longitudinal grooves were suggested, with the groove depth and width of 10 mm and the groove spacing of 150 mm. The designed RWCIEF met the construction bearing capacity and could fully guarantee the construction quality of the thermal insulation works of the outer enclosure structure. The maximum tensile stress and compressive stress caused by temperature effect did not exceed the bearing capacity of the outer reinforcing layer of RWCIEF, which indicates that RWCIEF is unlikely to hollow in summer or crack in winter. The proposed RWCIEF system can provide a new idea and method for the future research directions of exterior envelope insulation and building formwork engineering.

Abstract:

To analyze the saturation and coupling characteristics of SynRM and realize inductance identification with small disturbance and low error, an online inductance decoupling identification algorithm for SynRM is proposed. The influence of magnetic saturation and coupling on the voltage and flux linkage equations is first described to interpret the saturation and coupling characteristics of the inductance, and a decoupling motor model is developed by introducing a coupling angle. This model enables the analysis of saturation and coupling effects from a decoupling perspective. Then, an online identification strategy based on a virtual-axis equivalent impedance model is designed to identify both the coupling angle and inductance in real time. The proposed method is validated on a 3 kW SynRM experimental platform under various operating conditions. Experimental results demonstrate that the proposed algorithm effectively realizes online inductance decoupling identification, with identification errors for both the coupling angle and inductance within acceptable limits. Moreover, the inductance decreases with the increase of current, and the coupling angle increases with the increase of current. The changing trends of coupling angle and inductance identification results also verify the accuracy of motor saturation and coupling characteristic analysis. Compared to other inductance identification algorithms, the proposed algorithm does not require high chip computing power. While simplifying inductance calculations, it can also follow motor control in real time and output accurate values.

Abstract:

Image segmentation is to divide the region with special meanings into several disjoint sub-regions according to certain rules, which is the key link between image processing and image analysis. The traditional watershed image segmentation method is widely used, which has the advantages of fast and simple. However, it is easily interfered by noise, and the segmentation results are prone to lose important edge information, resulting in over-segmentation. In view of the problem of the traditional watershed image segmentation method, an improved watershed image segmentation method based on adaptive structural elements was proposed. First, the adaptive structural elements with variable shapes were constructed by using local density, symmetry, and boundary features of adjacent pixels of image targets, so as to ensure a good consistency between the proposed structural elements and the shape of image targets. Then, the adaptive structural elements were used to obtain the morphological gradient of the image, which could improve the positioning accuracy of the target edge. The L₀ norm gradient minimization and morphological open-close hybrid reconstruction were used to modify the gradient image, so as to reduce the local invalid minimum points in the gradient image and suppress the occurrence of over-segmentation. Finally, watershed segmentation was performed on the modified gradient image to realize accurate segmentation of the target region of the image. Experimental results show that the method could effectively restrain over-segmentation of traditional watershed algorithm and improve the accuracy of the target edge positioning, with high precision of image segmentation.

Abstract:

Autonomous navigation based on sequential images (ANBSI) is the key technology of pinpoint landing missions for future deep space exploration and also is one of the major development directions for deep space exploration technology. The necessity of developing ANBSI for planetary pinpoint landing is elaborated in this paper. Firstly, state-of-art developments of ANBSI are reviewed in terms of active sensing and passive sensing. Then, the key techniques applied in ANBSI for planetary landing are summarized and analyzed. Finally, according to the analysis of the key techniques, the main issues of ANBSI are raised and their future developments are overviewed.

Abstract:

In view of the problems of low brightness and obvious color distortion of the sky in restored images in most existing algorithms for image dehazing, a haze removal method for UAV aerial images based on atmospheric light value and graph estimation was proposed. First, the depth-of-field image was obtained according to the color attenuation prior theory, and the mean value of the region with the minimum deviation in the depth-of-field image was taken as the atmospheric light value. Then, a random walk clustering method was designed to estimate the atmospheric light map. The random walk algorithm was used to cluster the image into N sub-regions, and the mean value of the first 0.1% pixels of the sub-regions was taken as the regional atmospheric light value, which was then combined and refined by guided filtering to obtain the atmospheric light map. Next, the two atmospheric light estimators were fused into a new atmospheric light map with atmospheric light valuegraph estimation, which is a more accurate atmospheric light estimator. The transmittance was obtained by haze-lines prior method, and a dark compensation method was proposed to improve the transmission accuracy. Finally, according to the atmospheric scattering model, a clear restored image was obtained based on the fused atmospheric light map and optimized transmittance. Experimental results show that compared with other algorithms, the proposed algorithm improved the information entropy, mean gradient, blur coefficient, and contrast by 1.1%, 6.3%, 8.5%, and 6.4%, respectively, with better subjective visual effect and more abundant information.

Abstract:

In order to investigate the flexural behavior of ultra-high performance concrete (UHPC) lightweight composite decks under local wheel load, four demountable steelUHPC composite slabs connected by high-strength bolts were designed and four-point bending test was conducted. The influence of steel plate type and spacing of shear connector on the flexural characteristics of demountable steelUHPC composite slabs was analyzed, including failure mode, load-deflection curve, interface relative slip, crack width, and sectional strain distribution. Results showed that under positive bending moment, the failure mode of composite slabs adopting Q355 steel plate was that the high-strength bolt was cut off. While the failure mode of composite slabs using steel plate with negative Poisson’s ratio (NPR) was as follows: part of high-strength bolts was cut off, part of pre-embedded elongated nuts with cushion was pulled out, and UHPC collapsed due to instantaneous instability. Besides, under the same spacing of high-strength bolts, the relative slip of plate end of composite slabs employing NPR steel plate was relatively small, indicating that NPR steel plate can effectively delay and restrain the relative slip between steel plate and UHPC plate, thus improving the synergistic deformation capacity, flexural stiffness, and flexural bearing capacity for composite slabs. According to the sectional strain distribution analysis, due to the negative Poisson’s ratio effect, high stiffness, and high yield strength of NPR steel plate, the tensile strain between NPR steel plate and the bottom UHPC layer maintained strain compatibility during the whole loading process, and the upward displacement for sectional plastic neutral axis could be ignored with increasing load. Therefore, under the premise that NPR steel plate is employed to improve the flexural performance of steelUHPC composite slab system, the thickness of UHPC should be reasonably matched with the performance of NPR steel plate, so as to give full play to their material properties, and avoid the buckling failure prior to the material strength failure of UHPC.

Abstract:

This paper proposes a low-latency intelligent network data transmission scheduling algorithm for real-time network transmission demand scenarios of low latency, stable transmission, and high quality of experience (QoE). The algorithm consists of two parts: data block queuing control strategy and congestion control strategy. The data block queuing control strategy presents a cost-effective model that integrates the creation time and effective time of data blocks, effectively solving the problem of uneven information transmission under transmission time constraint. The congestion control strategy proposes a deep deterministic policy gradient (DDPG) method based on the Gumbel distribution sampling reparameterization with mixed experience prioritization model, which solves the problem that DDPG is not applicable to the congestion control of discrete network action space and significantly improves the quality of network congestion control by adaptively adjusting the sending parameters through learning. Results show that the proposed queuing algorithm could effectively improve QoE in real-time transmission scenarios, and the improved DDPG for congestion control could significantly reduce transmission delay. In the same scenario, compared with traditional network data transmission scheduling algorithms, by integrating the proposed queuing and congestion control strategies, the improved intelligent network data transmission scheduling algorithm could maintain a good balance between low latency and stable transmission and provide higher data transmission quality.

Abstract:

In view of the problem that general pre-trained models are not suitable for named entity recognition tasks in the medical domain, a neural network architecture that integrates knowledge graph in the medical domain was proposed. The elastic position and masking matrix were used to avoid semantic confusion and semantic interference in self-attention calculation of pre-trained model. The idea of multi-task learning in fine-tuning was adopted, and the optimization algorithm of recall learning was employed for pre-trained model to balance between general semantic expression and learning of the target task. Finally, a more efficient vector representation was obtained and label prediction was conducted. Experimental results showed that the proposed architecture achieved better results than the mainstream pre-trained models in the medical domain, and had relatively good results in the general domain. The architecture avoided retraining pre-trained models in particular domain and additional coding structures, which greatly reduced computational cost and model size. In addition, according to the ablation experiments, the medical domain was more dependent on the knowledge graph than the general domain, indicating the effectiveness of integrating the knowledge graph method in the medical domain. Parameter analysis proved that the optimization algorithm which used recall learning could effectively control the update of model parameters, so that the model retained more general semantic information and obtained more semantic vector representation. Besides, the experimental analysis showed that the proposed method had better performance in the category with a small number of entities.

Abstract:

To investigate widely applicable shear capacity calculation methods of reinforced concrete column-steel beam (RCS) hybrid connection, this study analyzed the shear failure experimental data of RCS connections in recent years. The experimental results were compared with the calculation results of Chinese specification, Nishiyama method, Parra method, and ASCE guideline, and the parametric applicability of each method was discussed. The comparison results showed that all the four methods had engineering value. The minimum discreteness of the results of Parra method was obtained, and the calculation process of Chinese specification was the simplest. The results of parametric study showed that all the four methods were suitable for connections with different stirrup ratios and positions. However, conservative estimates were obtained for specimens with small axial load ratios (from 0 to 0.2) and column-through connections. For the Chinese specification, the predicted strength of connections with concrete strength higher than 60 MPa was unsafe, while the predicted strength of connections with transverse beams was conservative. Therefore, it is suggested to introduce concrete strength coefficient and confined coefficient of transverse beam into the equation considering the influence of these two factors.

Abstract:

To reasonably select a suitable set of ground motion parameters and effectively reduce the uncertainty of structural damage prediction, various ground motion parameters were preferentially selected based on the elastic network regression technique. First, the elastic network regression model was established based on various ground motion parameters and the seismic capacity of a generic set of single-degree of freedom (SDOF) systems obtained from the results of incremental dynamical analysis. Second, the values of regression coefficients in the elastic network regression model and the number of times that the regression coefficients have non-zero values were used to define the sensitivity and frequency of ground motion parameters, respectively. Third, the ranking of ground motion parameters used for seismic capacity prediction was established in terms of sensitivity and frequency of ground motion parameters obtained from the results of elastic network regression on a generic set of SDOF systems. Results were statistically organized to evaluate the influence of various ground motions, structural types and structural limit-states. The analysis result obtained from an 8-story steel frame verified that the use of ground motion parameters selected based on elastic network regression can effectively reduce the uncertainty of structural damage prediction. In addition, results showed that the standard deviation of the residuals in the regression analysis for different structural limit-states was significantly reduced when the representative ground motion parameters were employed in the least squares regression model. Moreover, representative ground motion parameters that are less affected by various ground motions, structural types and structural limit-states were selected based on the ranking results obtained from a generic set of SDOF systems. Findings of this study can provide a theoretical basis for the comparison of ground motion parameters used for the prediction of structural seismic capacity.

Abstract:

To study the bending performance of prestressed steelbamboo composite I-shaped beams, 12 prestressed composite I-shaped beams were designed and manufactured for bending tests, considering prestress loads, prestressing schemes, and loading schemes as basic parameters. The experimental phenomenon was observed, and failure characteristics were analyzed during the test. The influences of different parameters on the load-bearing capacity, strain distribution, and deformation performance were explored, and an approximate formula for the bearing capacity of the prestress composite beam was proposed. The results indicated that the prestressed composite beams have relatively good performances from the perspectives of combination effect, deformation characteristic, and bearing capacity. Failure modes of tested specimens were mainly owing to the bamboo flanges damage and the local buckling of steel plates. With the technique of prestress and the increase of prestress level, the deformation performances can be improved effectively, as well as the load-bearing capacity considering the same deflection situation. Moreover, the improvements can be more significant with the two-point prestressing scheme. The mid-span strain distribution of prestressed composite beams conforms to the plane section assumption, and the neutral axis moves down with the increase of the prestressing level. Finally, the bearing capacities based on the theoretical calculation matched well with the experimental results, which showed the applicability of the proposed methods.

Abstract:

In complex geological environments such as deep layers, the mechanical and damage characteristics of rocks have a decisive impact on the development of high-temperature engineering. To further explore the mechanical properties of high-temperature rocks and their damage mechanisms under load, this study delves into yellow sandstone samples exposed to varying temperatures (25 ℃, 200 ℃, 400 ℃, 600 ℃, 800 ℃). Based on X-ray tomography (CT) technology, obtain internal pore data and 3D model of yellow sandstone, analyze the variation law of porosity of yellow sandstone with temperature. Additionally, numerical simulations were executed to delve into the evolution of microcracks and the damage mechanisms inherent in yellow sandstone under distinct temperature conditions. This microscopic approach unveils the thermal damage mechanisms of rocks under high temperatures. Key findings include: as temperature rises, the total porosity of yellow sandstone follows a quadratic growth trend, accompanied by a decrease in pore distribution uniformity. The main factors of thermal damage in yellow sandstone include: high-temperature dehydration, thermal decomposition of mineral components, and expansion of mineral particles. The increase in porosity due to thermal decomposition and particle expansion is a key factor in thermal damage. Between 25400 ℃, differential expansion and compression of mineral grains generate localized stress zones, predominantly fostering intergranular cracks within yellow sandstone. In the 400-800 ℃ range, phase transitions and mineral component decomposition within yellow sandstone amplify these stress zones, favoring intragranular crack propagation. A damage evolution model of yellow sandstone under thermal action was constructed by defining the damage variable based on the porosity of yellow sandstone, which can provide theoretical basis and technical support for the study of damage mechanism in high-temperature rock mechanics.

Abstract:

With the gradual deepening of the study of seepage erosion in soil, the research methods of soil particle loss and deformation and failure mechanism show the characteristics of multi-scale. The computational fluid dynamics-discrete element coupling method (CFD-DEM) provides an effective method to study the macroscopic mechanical characteristics of soil on the microscale, considering the influence of fluid-solid interaction. Regarding the current application status of CFD-DEM coupling method in geotechnical engineering, this paper systematically summarizes the advantages and disadvantages of existing fluid-solid coupling calculation methods, focusing on the modeling strategies of CFD-DEM coupling method. These strategies include solid particle shape modeling and inter-particle contact models, control equations and parameter calculation methods for the fluid phase, as well as CFD-DEM coupling calculation. Furthermore, the paper conducts an in-depth exploration of related issues and concludes by proposing future development directions for the CFD-DEM coupling method.

Abstract:

To study the influence of the alkaline activator on the performance of steel slag cement, the effects of alkaline activators (water glass、Na₂CO₃/NaOH、NaOH) on the macroscopic mechanical properties of steel slag cement are studied in this article. Further, the microscopic characteristic is investigated by hydration heat release, X-ray diffraction (XRD), thermogravimetric analysis (DSC-TG), scanning electron microscope (SEM) and mercury intrusion porosimetry test (MIP). The results show that the alkaline activators increased the basicity in the early hydration liquid phase of steel slag cement, accelerating depolymerization of steel slag vitreous to produce H₃SiO-₄ and H₃AlO^2-₄, improving the reaction rate, facilitating the formation of C-S-H gel and zeolite products, which is manifested by the shortening of setting time and induction period, the increase of reaction heat, cumulative heat release and early mechanical strength; the influence of alkaline activators on the properties of steel slag cement is related to its molecular structure of activators, and the order of influence was water glass, Na₂CO₃/NaOH, NaOH in descending order; Water glass could increase the alkalinity of liquid phase in steel slag cement, and the SiO^2-₃ could react with Ca(OH)₂ to produce C-S-H gel. The addition of alkaline activators can promote the hydration reaction of steel slag cement, which is helpful to the improvement of the mechanical properties and the compactness of the microstructure of steel slag cement.

Abstract:

To improve the electromagnetic performance of the interior U-shaped permanent magnet synchronous motor and reduce the vibration noise of the motor body, a magnet-focusing type equal volume misalignment segmented interior U-shaped permanent magnet synchronous motor is proposed. First, the electromagnetic performance expression of the motor such as gap magnetic flux density, no-load back-emf, and output torque, and the expression of electromagnetic vibration such as radial electromagnetic force, vibration velocity and acceleration are derived. Second, the effects of the separate addition of magnetic barrier structure, misalignment structure, and Halbach magnetization structure on the electromagnetic performance of the motor are investigated. Finally, the structure parameters are analyzed and optimized, and the electromagnetic performance, electromagnetic vibration, and noise fluctuation of 5 types of U-shaped magnet pole permanent magnet synchronous motors are compared. The study shows that the four added structures have a significant effect on motor performance. In terms of electromagnetic performance, the addition of a magnetic barrier, misalignment structure, and auxiliary can improve the motor output torque and reduce the slot torque. The Halbach magnetization can improve the output torque, radial air-gap magnetic flux density distribution, and radial electromagnetic force distribution, and combination of the three can improve the motor output torque. The output torque becomes more smooth, and the slot torque and torque ripple are significantly reduced. In terms of vibration noise, the auxiliary slot structure is added to significantly suppress the 8th and 16th harmonic amplitude of the radial electromagnetic force; the magnetic barrier structure can suppress the low-frequency vibration of the motor, and the misalignment structure and Halbach magnetization can suppress the high-frequency vibration acceleration of the motor, and the four structures can significantly reduce the radial electromagnetic force the space of 8 and 16 times, and the vibration acceleration of 4 times and 6 times in time is significantly suppressed, the maximum sound pressure level and mechanical of the motor meet the requirements of motor operation, and the rotor sample of the permanent magnet motor is machined.

Abstract:

To understand and investigate the impact of summer thermal reflection from glass curtain walls on the microclimate of surrounding environments, and to raise awareness of this rarely acknowledged issue, which is often overlooked due to its invisibility, this study employed optical simulation, scale modeling, and field measurements to identify reflection zones and quantify thermal reflection effects. Firstly, the principles, variation patterns, and potential hazards of thermal reflection from curtain walls are explored using optical mirror reflection principles. Secondly, taking Chunxi Road Plaza and its surrounding curtain wall buildings in Chengdu as an example, optical simulation and experimental modeling methods were used to determine the hourly positions of thermal reflection areas on the square. Through on-site thermal environment testing and comparative analysis, the impact of curtain wall thermal reflection was quantified. Finally, a series of strategies were proposed to control curtain wall thermal reflection and mitigate its hazards. The results indicate that compared to normal areas, the average radiant temperature in thermal reflection areas was higher by 4 to 13 ℃, air temperature was elevated by 1 to 2 ℃, humidity was reduced by 5% to 10%, and the universal thermal climate index was increased by 2 to 5 ℃. These findings confirm the significant influence of summer curtain wall thermal reflection on microclimate environments and pedestrian thermal comfort. Therefore, incorporation of thermal reflection management strategies into urban planning and curtain wall design is imperative to reduce its negative impacts.

Abstract:

To investigate the seismic behavior of reinforced concrete (RC) columns with stay-in-place ultra-high performance concrete (UHPC) formworks, named URC columns for short, we selected different assembly methods and surface treatment methods of UHPC formworks as design parameters and carried out pseudo-static tests on nine URC columns and one RC column. The assembly methods of UHPC formworks were boltangle steel connection, bolt connection, and epoxy resin mortar. The surface treatment methods of UHPC formworks were natural surface, bubble film printing, and adding ribs. The pseudo-static tests were conducted to study the influence of different assembly methods and surface treatment methods on the seismic behaviors of the URC columns. Additionally, on the basis of the assumption of plane section, a formula was proposed to predict the eccentric compressive bearing capacity of the URC columns. Results show that the bonding surface between UHPC formwork and concrete core had no apparent damage before the peak load, indicating that the URC columns have good integrity. In particular, the URC columns connected by boltangle steel had no interface bonding failure even under the failure load. Compared with the traditional RC column, the ultimate bearing capacity, ductility, and energy consumption of the URC columns were increased by 6.4%43.3%, 11.4%48.7%, and 27.7%85.3%, respectively. Among the three assembly methods, the URC columns connected by bolt and angle steel had the highest bearing capacity and the most reliable connection. Finally, the results calculated by the proposed formula were in good agreement with the test results, which can provide reference for practical application.

Abstract:

To study the impact of corrosion of both longitudinal bar and stirrup on the bonding performance of steel bar and concrete, we fabricated 25 corroded reinforced concrete (RC) specimens by the accelerated corrosion method of electroosmosis-constant current-dry wet cycles. Pullout tests were carried out on the specimens, and the influences of parameters such as longitudinal bar corrosion, stirrup corrosion, cover thickness, and stirrup spacing on bonding properties were studied. The effect of corrosion on the bonding force between concrete and steel bar was analyzed, and the degradation of the bonding performance was attributed to the reduction of the material behavior and the degradation of the constraint effect. On the basis of the test results, a modified bondslip constitutive model was established and verified considering design parameters and both corrosion of longitudinal bar and stirrup. A stressslip model of corroded longitudinal bar was obtained by combining the proposed constitutive model and infinitesimal algorithm. In the OpenSees platform, the stressslip model was applied to the zero-length section element, and the numerical model of corroded RC components considering bondslip behavior was established by adopting fiber-based beam-column element and zero-length section element. The accuracy of the model was verified according to the quasi-static test data of the corroded RC column, and the fiber model considering only corrosion damage was used for auxiliary verification. Results show that the bonding force between concrete and steel bar increased first and then decreased with the increase in the corrosion degree. Increasing the cover thickness could slightly improve the bonding force, while the increase in stirrup density could significantly improve the bonding force. Compared with the fiber model, the bearing capacity, cumulative energy dissipation, and ultimate displacement errors were reduced by 12.8%, 23.5%, and 14.2% in the constructed fiber model, indicating that the constructed model can reasonably calculate the contribution of steel bar slip and accurately predict the overall seismic response of the corroded RC columns.

Abstract:

In order to compare the mechanical performance differences between grouted sleeve lapping connectors and butt connectors, uniaxial tensile and high stress repeated tension-compression tests were conducted on 41 lap connectors and 20 butt connectors. Results showed that under uniaxial tension and high stress repeated tension-compression loading, the total elongation ratio with maximum force of two kinds of connectors was greater than 6% and the ductility coefficient was greater than 4. The strength basically met the requirements of the codes. Under uniaxial tension after high stress repeated tension-compression, the bearing capacity of both connectors increased, while the initial stiffness and ductility of the specimens decreased. Moreover, the residual deformation of the lap connector was reduced by the anti-deflection measures, but the measured value of the residual deformation of the lap connector was slightly larger than that of the butt connector due to the limited constraint stiffness of the anti-deflection measures. However, the residual deformation of the lap connector and the butt connector of anti-deflection generally met the requirements of the specification. After high stress repeated tension-compression, during uniaxial tension testing, the middle section of the sleeve of the lap connector was longitudinally compressed and circumferentially stretched in the early stage of loading. In the later stage of loading, it experienced longitudinal stretch and circumferential compression, while the sleeve of the butt connector was longitudinally stretched and circumferentially stretched throughout the loading process. In the case of uniaxial tension after high stress repeated tension-compression, the maximum longitudinal tensile strain of the middle section of the sleeve near the bar side of the anti-deflection and non-deflection lap connector was 0.10 to 0.39 times and 0.13 to 0.18 times of the butt connector, respectively. Furthermore, the maximum circumferential compressive strain was 0.09 to 0.49 times and 0.02 to 0.32 times of the butt connector, respectively, which indicated that the lap connector had relatively low requirements on the material of the sleeve. When the diameter of rebar was the same, the material cost of the lap connector was about 35% lower than that of the butt connector.

Abstract:

To investigate the dynamic performance and unloading failure characteristics of coal under non-hydrostatic conditions, based on 3D dynamic and static loading experiment, the effect of unloading method on the macroscopic failure characteristics of unloading coal samples after dynamic disturbance was studied. Firstly, Ф50 mm split Hopkinson pressure bar system was used to carry out the dynamic experiment of coal sample under 3D dynamic and static loading for the purpose of studying the influence of axial compression and strain rate on the dynamic response of coal samples. Secondly, based on the response surface theory, a regression model considering the interaction of factors was constructed by using the central composite test method and the significance of single factor and factor interaction were analyzed. Afterwards, combined with factor interaction, Weibull distribution and Drucker-Prager criterion, the strength statistical damage constitutive model of coal was modified. The reliability of the model was verified by comparing the theoretical and experimental results. Finally, with the help of loading and unloading electro-hydraulic servo device, the influence and mechanism of axial pressure, impact pressure and unloading mode on the failure characteristics of coal samples were explored. The results showed that the constructed strength statistical damage model has a correlation coefficient R²≥0.88, which can characterize the dynamic response behavior of coal samples. The coal samples with synchronous unloading after impact are mostly spalled, and the tensile interface moves backward and eventually disappears with the increase of axial pressure, unable to form spall failure. The failure modes of coal samples under non-synchronous unloading mainly include overall integrity, spalling and compression-shear failure. However, when the impact pressure is in the range of 0.4 to 0.6 MPa and the axial pressure is 14.5 MPa, a mixed failure mode of ‘spalling + compression-shear’ is observed.

Abstract:

To meet the requirements of large deployment ratio and high precision for deployable membrane mechanism in space missions, a deployable membrane mechanism based on Miura elastic creases was proposed and subjected to model, analyze and develop prototype. According to the crease distribution law and geometric relations, Miura-ori geometric model was established to investigate the influence law of the crease parameters on the deployment ratio and creases total length, and to calculate and optimize the crease parameters. In ABAQUS/Explicit, the numerical simulation models of the four-creases basic unit with θ= 90° and θ<90° were established respectively to analyze the mechanical behavior of the key membrane creases, and the feasibility of two-dimensional elastic crease was preliminarily proved. The elastoplasticity of the triangular membrane of Miura-ori was further studied, and the change curve of stress with folding process at the intersection of creases was plotted and the peak stress of which was within the range of material elasticity. And the space deployable membrane mechanism prototype was developed to conduct validation and analysis. The results show that the mechanism configuration design scheme is reasonable and a membrane folding scheme based on Miura-ori with large deployment ratio and small creases total length could be obtained by optimizing the crease parameters, and the high surface flatness of the deployed membrane proves the feasibility and superiority of Miura elastic creases.

Abstract:

In order to improve the separability of powdered activated carbon, a new type of magnetic activated carbon was prepared using chemical co-precipitation.Using methylene blue as target pollutants, performance of the powdered magnetic activated carbon was studied under varied conditions of pH, contact time and initial methylene blue concentrations, via the comparison with powdered activated carbon. The results showed that the adsorption capacity of synthetic magnetic powdered activated carbon was higher than that of the powdered activated carbon, and an alkaline pH value and adequate contact time were favorable for the pollutants removal. Under the condition of 100 mg/L methylene blue concentration, 0.4 g/L magnetic activated carbon dosage of, pH 9 and a reaction time of 300 minutes, the removal rate of methylene blue reached 98.9%. The adsorption behavior of methylene blue on magnetic activated carbon fitted the Langmuir isotherm and Elovich dynamics model. Thermodynamic analysis indicated that the adsorption was spontaneous endothermic reaction of single molecule layer, and the chemical adsorption played an important role during the adsorption process. The magnetic activated carbon had a good recyclable performance, it could complete precipitation in 10 minutes under natural condition, and could be quickly separated in 30 seconds under the action of outside magnetic field.

Abstract:

To accurately evaluate the safety and comfortability of structures after a long period of service under dynamic loads such as earthquakes and wind, it is critical to establish a structural dynamic model that can accurately reflect the dynamic responses of actual buildings under seismic, wind and other dynamic loads utilizing the monitoring/inspection data. In this paper, for popular frame building structures, an equivalent simplified dynamic modeling method is proposed by using a few numbers of wireless mobile sensors. First, the principle of equivalent interstory shear force for a simplified model of buildings is proposed, which proves that the simplified model constructed based on this principle has the ability to accurately simulate the dynamic response of actual buildings. Second, the form of simplified model of frame structure was derived, and the characteristics of the simplified model parameters were studied. Then, an iterative identification method for the parameters of the simplified model was proposed, which can identify all parameters of the simplified model by solely using a small number of wireless mobile sensors. Finally, a numerical simulation example of a 12-story 3-span steel frame structure was conducted, which investigates the predictive capability of the equivalent simplified model constructed by the method proposed herein to predict the dynamic responses of the actual frame structure subjected to different types of horizontal excitations, under the condition of without knowing the specific format of structural stiffness degradation and using only a small number of moving acceleration sensors. Simulation results show that the equivalent simplified model can very accurately predict the dynamic responses of the actual frame structure subjected to different types of horizontal excitations. Therefore, the model updating method for the equivalent simplified model of frame structures proposed herein will have important application potential in evaluating the structural safety and comfort of existing frame building structures under dynamic loads, such as wind and earthquake.

Abstract:

Carbon dioxide (CO₂) capture, utilization and storage, as an emerging technology that can help reduce coal chemical plant greenhouse gas emission by large scale, have drawn significant attention. Pipeline transportation is an essential part of the technology, but high cost has greatly limited its application. Therefore the main objective is to develop an optimization model for supporting CO₂ pipeline transportation system planning to reduce the overall carbon capture utilization and storage (CCUS) system cost by optimizing key technology process of a CO₂ transportation system. The developed model was further applied to Shaanxi Yanchang's CCUS project for planning its CO₂ transportation system. The results indicated that in case of low demand of CO₂ storage, a gas-phase CO₂ pipeline transportation system coupled with in-situ compression and injection was recommended. In the case of high demand of CO₂ storage, this study would recommend a super-critical / density phase transportation system which could have lower system cost than gas phase pipeline system as the cost for compression at the site of storage can be saved

Abstract:

To explore the improvement mechanism of welding round steel at soffit on the flexural performance of eccentric concrete-filled steel tube (CFST) members, we established a numerical model of CFST beams reinforced with round steel by using ABAQUS software and verified the model by test results. By analyzing the bending momentdeflection curve, bending momentaxial strain curve, hoop strain curve, restraint index, and neutral axis offset of eccentric CFST members reinforced with round steel, the improvement mechanism of the flexural performance of the eccentric CFST members was revealed. Besides, the influence of the diameter of the round steel and the slenderness ratio of the beams on the flexural performance of eccentric CFST members reinforced with round steel was analyzed. Results show that welding round steel could lower the position of the neutral axis of the section and increase the hoop strain of the steel tube on the compression side. Therefore, the concrete area in compression was increased, and the restraint effect of the steel tube on the compression side on the concrete was enhanced. Furthermore, the flexural bearing capacity and flexural stiffness of the eccentric CFST members were improved, and the larger the diameter of the round steel, the greater the improvement. The ultimate bending moment of the eccentric CFST members decreased with the increase in the axial compression ratio, and the larger the diameter of round steel and the slenderness ratio of beams, the greater the reduction. Welding round steel had a better effect on improving the bending performance of the eccentric member with a large slenderness ratio, and the larger the axial compression ratio, the better the improvement effect.

Abstract:

The existing graph-construction methods for graph optimization-based SLAM are summarized. The SLAM methods can be divided into three main classes, Kalman filter-based, partical filter-based and graph optimization-based, and the advantages and disadvantages of each class are overviewed. Moreover, there are mainly three graph modeling methods for the graph optimization-based SLAM problem, namely dynamic Bayesian network (DBN)-based model, factor graph-based model and Markov random field-based model. The key techniques of the front-end stage in graph optimization-based SLAM method, which mainly include data association between consecutive frame and loop closure detection, are discussed. Some newest research achievements on feature extraction, matching method, motion estimation, loop closure detection are introduced.

Abstract:

There are a large number of new and old concrete interfaces in prefabricated concrete structures. The template effect causes the enlargement of cement mortar porosity in the interfacial zones, which weakens their mechanical properties and durability. In order to quantitatively describe the porosity distribution characteristics of cement mortar in interfacial zones, new and old concrete specimens with smooth vertical interfaces and different water cement ratios were prefabricated. Scanning electron microscopy (SEM) was used to obtain the gray images of each specimen at different positions from the interface. Digital image processing (DIP) tools were used for image information enhancement and binarization. Thus, the ratio of pore pixels to total pixels was obtained, namely nominal porosity. With test results, the distribution characteristics of nominal porosity in the interfacial zones of new and old concrete with smooth vertical interfaces were analyzed. On the basis of the stable relationship between nominal porosity and real porosity, a porosity distribution model was established for the interfacial zones of new and old concrete with smooth vertical interfaces. Furthermore, considering the continuous variation of new and old concrete contents in the chiseled section, the porosity distribution model of chiseled interfacial zone was established. Results show that the nominal porosity reached the maximum at the interface, then decreased gradually towards the interior of concrete, and finally tended to be stable. The overall variation trend could be characterized by Gaussian function. With the increase in water cement ratio, the nominal porosity of each position from interface to interior concrete presented a relatively increasing trend, but the relative nominal porosity from the interfacial zone to the interior stable zone was nearly the same for the concrete with different water cement ratios.

Abstract:

Starting from the tactile sensing performance of human skin, the progress and key technologies of tactile sensors for e-skin (electronic skin) akin to human skin by multidisciplinary fields are comprehensively reviewed. The sensing principle, new materials and structures, advanced design and making methods, sensing characteristics and performance of tactile sensors are analyzed. The recent domestic and foreign research advances of electronic skin tactile sensor array in flexibility, elasticity, spatial resolution, sensitivity, fast response, transparency, lightweight, multifunction and other aspects are summarized. It is difficult to achieve the tactile sensors for e-skin with high stretchable and flexible, less complex production process for high sensitivity e-skin, strong extensibility and low cost. The tactile sensors for e-skin can be widely used in robotics, medical health, aeronautics and space military, intelligent manufacturing, automotive security and other fields. The development of tactile sensors for e-skin toward the direction of high stretchable and flexible, high sensitivity in wide range, multifunction, self-healing and self-cleaning, self-powered and transparent, has been pointed out.

Abstract:

To update the structural finite element model through stochastic static displacement measurement data and maintain the computational efficiency, we proposed a stochastic model updating method based on homotopy meta-model and Bayesian sampling method. First, the objective function was constructed by using the static displacement of the structure, and the delayed rejection adaptive sampling algorithm was used to estimate the posterior probability density of the updated parameters. In the process of sampling, the homotopy meta-model was adopted instead of the finite element model to calculate the static displacement of the structure. Numerical examples and test results show that when updating the finite element model of variable cross-section beam, as opposed to the quadratic response surface model, by incorporating the homotopy meta-model into the static Bayesian model method, the posterior probability density of the updated parameters could reproduce the stochastic response of the structure more accurately, making the probability density function of the stochastic response of the updated structure more consistent with that of the measured results. Even when the coefficient of variation of the stochastic measurement error was large and the difference between the prior information and the real updated parameters was large, the proposed method could quickly obtain the posterior probability density of the updated parameters, so that the probability density function of the structural stochastic displacement response calculated by the updated parameters was consistent with that of the measured results. The homotopy meta-model combined with Bayesian sampling algorithm can update the stochastic model of the structure quickly and accurately within the probability framework.

Abstract:

To study the influences of roughness, gravel content, and normal stress on the shear mechanical properties of the interface between limestone spoil mixture and concrete, a series of interface shear tests were carried out on limestone spoil mixtures with four types of gravel content and concrete surfaces with five types of roughness under different normal stress conditions by using a new large-scale direct shear apparatus. The influences of roughness, gravel content, and normal stress on the shear strength of the interface were investigated, and the internal relationships between roughness, gravel content, normal stress, and the shear mechanical properties of the interface, as well as the mechanism of shear strength were revealed. Test results show that under the same normal stress condition, as the roughness increased, the shear strength of the interface increased and then decreased, and the rough interface significantly increased the degree of dilation of the interface, but with the increase in normal stress, the influence of roughness on the shear strength and normal deformation of the interface was weakened. Under the condition of same normal stress, with the increase in gravel content, the variation of the shear strength of the interface was closely related to the magnitude of the normal stress. Besides, the shear strength of the interface was highly consistent with the Mohr-Coulomb strength criterion, and the influence of roughness and gravel content on the apparent cohesion of the interface was more significant than that on the internal friction angle of the interface. To a great extent, rational roughness and gravel content can improve the shear strength of the interface between spoil mixture and concrete.

Abstract:

To explore the mechanical properties of integral abutment-RC pile-soil structure, we designed and prefabricated four integral abutment-RC pile models with different pile foundation reinforcement ratios and cross-section shapes taking an integral abutment bridge in China as background. The quasi-static test of integral abutment-RC pile-soil interaction under low cyclic loading was carried out. The effects of reinforcement ratio and cross-section shape of RC pile on the mechanical properties of abutment-RC pile-soil system were mainly studied, and the soil resistance behind abutment, the soil resistance beside pile, and the distribution of pile strain and bending moment were analyzed. Results show that under the action of cyclic displacement of abutment, the distribution of soil resistance near the abutment back along the height direction changed from “triangular” distribution to “parabolic” distribution, and that away from the back of the abutment was basically in “triangular” distribution. The resistance of soil behind abutment was affected by the reinforcement ratio and section shape of RC pile, so it is necessary to increase the reinforcement ratio of RC pile or use rectangular section to improve the integrity of integral abutment-RC pile-soil system. The cumulative deformation of pile affected the distribution of soil resistance on the sides of pile, which reduced the soil resistance behind pile and increased the soil resistance in front of pile. The specimens with larger reinforcement ratio or rectangular cross-sectional RC piles were less affected by the cumulative deformation, and the integrity of abutment-pile foundation-soil structure was better. When the integral abutment-RC pile structure moved to the river span side, the distribution of pile strain and bending moment was consistent with that of traditional pile foundation. When moving to the riverbank, the maximum strain and bending moment of pile appeared at the joint between the bottom of abutment and the top of pile. Increasing the reinforcement ratio of RC pile or adopting rectangular cross-sectional RC pile can effectively reduce the strain and bending moment of pile body and improve the mechanical performance of RC pile foundation.

Abstract:

The rapid development and extensive application of ships highlight the vital role of the analysis and control of ship deck flow field. To improve the flow field of ship deck, a novel active flow control method based on jet is proposed, and by taking the position of helicopter rotor disk as an example, the effect of different jet device parameters on the optimization of helicopter rotor disk flow field is analyzed. First, the numerical simulation model of the flow field of the ship deck was established to examine the influence of active flow control on the ship deck flow field based on the Navier-Stokes equation. Then, the k-ε turbulence model was chosen and the effectiveness of the method was validated. Finally, the streamline and velocity distribution of ship deck flow field with jet device were simulated. Combined with the influence of flow field information on rotor force, the flow control effect of jet device on ship deck flow field was compared and analyzed. The results show that the addition of upper jet can reduce the influence range of reflux zone in the deck flow field and the velocity gradient of rotor disk flow field accordingly. The reduction of the velocity gradient of the rotor disk flow field tends to effectively reduce the aerodynamic variation and the response of the rotor. Adding jet devices under different inflow angles may reduce the response and improve the safety of the helicopter by controlling the deck flow field. As the jet velocity exerts a significant influence on the flow field control effect, the optimal jet velocity should be selected with reference to the installation position of the jet device to achieve better control effect.

Abstract:

In order to explore the erosion damage mechanism of polymer cement protective layer on concrete surface of water transfer project under the action of high-speed water flow, the erosion characteristics of protective layer were studied by using improved high-pressure water gun erosion test equipment. Four characteristic parameters including maximum length, maximum width, maximum depth, and volume of erosion area were extracted by 3D scanning. The erosion damage pattern, damage parameter evolution law, and damage mechanism of protective layer under different spray pressure, spray length, spray angle, and spray time were analyzed. Taking the maximum erosion depth of protective layer as the target value, a prediction model of protective layer erosion depth based on Logistic regression function was established. Results show that under the same working conditions, the four erosion damage characteristic parameters of protective layer all increased with the increase in spray pressure and erosion time. With the increase in spray length (from 0.5 cm to 6.6 cm), the erosion pattern of protective layer changed from "hourglass" to "strip". In this process, the damage effect of hydraulic fracturing on the interface between protective layer and concrete decreased. The proposed prediction model of erosion depth of protective layer achieved good accuracy, and the erosion damage degree of protective layer could be significantly reduced by increasing the spray length and spray angle, which provides a reference for the surface protection design of concrete engineering.

Abstract:

To study the effect and mechanism of cohesive sediment gradation on cavitation and cavitation erosion in high velocity flow, we selected two cohesive sediment gradation curves and conducted research in a self-developed small looped water tunnel. Sediment-laden flows with different mass percentages of cohesive sediment smaller than a certain grain size were prepared, and the real-time pressure within cavitation and cavitation erosion zones in working section of water tunnel was measured by a dynamic pressure data acquisition system. Concrete specimens with different mix proportions were prepared. Tests of cavitation erosion on the concrete specimens under different mass percentages of cohesive sediment smaller than a certain grain size were carried out for 4 h. The mass loss of concrete specimen per hour was adopted to characterize the cavitation erosion amount . Results show that the time-averaged pressure and cavitation number at each measurement point in the cavitation erosion zone of the working section of water tunnel gradually increased with the decrease in the mass percentage of cohesive sediment smaller than a certain grain size. With the decrease in the mass percentage of cohesive sediment smaller than a certain grain size, the cavitation erosion amount of concrete specimens gradually increased. The anti-cavitation erosion capacity of concrete specimens with higher strength was significantly greater than that with lower strength at the same flow velocity. Cavitation zone was mainly located in the front of the specimen at lower velocity, while it was located in the rear at higher velocity. Under the same sediment concentration, the higher the percentage of cohesive fine grain used in the test, the greater the cavitation erosion amount was.

Abstract:

To describe the nonlinear magneto-mechanical coupling effect of materials more accurately, a coupled magneto-elastic model and a variable stiffness model were proposed based on nonlinear magneto-strictive strain equation, effective field theory, and energy balance equation. The magneto-mechanical effects and variable stiffness effects of ferromagnetic materials were studied, and the theoretical results of the nonlinear magnetization model were coupled with the simulation process using numerical analysis software. The results showed that the defect leakage field distribution obtained by the simulation was consistent with the existing research results, which verified the feasibility and accuracy of the proposed model and simulation method. The effects of stress, defect size, and defect location on the surface magnetic field were also analyzed. The results showed that under the action of tensile load, the normal magnetic field signal on the surface of the sample was like an S-shaped curve, and the tangential signal was like a conical curve, and its extreme values first decreased and then increased with the increase in the load. When there was a defect in the sample, the signals obtained on different acquisition paths were very different, and the peak value of the leakage magnetic field on the defect edge path was negatively correlated with the defect length, but the peak distance and span were opposite. On the collection route far from the defect, the peak value and span of the leakage magnetic field signal were positively correlated with the defect length.

Abstract:

The wear of cutter is an important factor affecting the efficiency of shield tunneling, which is also a basis for determining the time and frequency of cutter replacement. As it is difficult to evaluate the overall wear state of the cutters in the process of shield tunneling, three wear degrees (light, moderate, and severe) were proposed based on the relationship between the wear amount of each cutter and the limited wear at the cutters change site. The theoretical relationship between three main tunneling parameters (thrust, torque, and tunneling speed) and the cutting force component of a single cutter was derived, and a method for recognizing the overall wear state of cutters was proposed by using the wavelet packet algorithm to decompose the tunneling parameter signals. In this method, the eigenvectors composed by the standard deviation of the wavelet packet coefficient of decomposed signal nodes were used as the wear recognition index. Sensitivity analysis was performed to find out the most sensitive node eigenvector of the cutter wear. Then the functional relationship between the wear state and the wear recognition index was determined by fitting. The analysis of the section from Dayun station to Baohe station of Shenzhen Metro Line 14 showed that the method could accurately recognize the overall wear state of the shield cutters. Among the three tunneling parameters, the recognition accuracy was the highest when using the tunneling speed signal, followed by the thrust signal, and the torque signal was the lowest. The proposed method is easy to use and cost-effective, since it only needs to analyze the automatically collected tunneling signals without installing any sensors, which provides reference for cutter replacement.

Abstract:

It is well known that unmanned aerial vehicle (UAV) is more and more widely applied in military and civil areas. In order to play the better role of UAV, it is needed to utilize multi UAVs cooperative formation to accomplish cooperative reconnaissance, combat, defense and spraying pesticides and other tasks. The multi UAVs cooperative formation control technology mainly contains the following key techniques: data fusion technology, sensing technology, task allocation technology, path planning technology, formation control technology, communication network technology and virtual/physical verification platform technology. Firstly, summarize the research and development of key technologies worldwide. Then, the classification for multi UAVs formation control methods is mainly investigated, and the problems about formation design and adjustment, formation reconfiguration are summarized. Finally, the challenges and future development for multi UAV cooperative formation are prospected. Research shows: at present, the theory of multi UAV formation flight has acquired fruitful results, while the real cooperative formation flight test can only be implemented in the simple communication environment. The real time performance for task allocation and path planning is not high. The robustness of control methods to cope with the unexpected situation is low. The cooperative sensing ability for multi UAV with multi sensor is insufficient. The simulation of the entity is lacked. Breaking through the above key technologies, carrying out the cooperative formation flight of multi UAV in complex sensing constraints and complex communication environment, putting forward more effective control method and carrying out the UAV physical formation flying test so that the UAV can finish the task better may be the future research directions.

Abstract:

To study the development status of wearable soft upper limb exoskeleton and its key technical challenges, the current literature in this field was analyzed and summarized. Exoskeletons can effectively provide functions such as protection and support to address limb fatigue and physical function decline resulting from high-intensity and repetitive work, as well as limb movement disorders caused by stroke or occupational diseases. Additionally, they have the capability to restore or enhance human movement ability through additional power and functionality. Wearable soft exoskeletons, as a new development direction of exoskeletons, have obvious advantages over traditional rigid exoskeletons, such as structural flexibility, human-machine interaction, and wearable comfort. Firstly, this paper provides a detailed analysis of three main driving methods of soft upper limb exoskeleton (rope drive, pneumatic, shape memory alloy). The relevant research results and corresponding structural characteristics of different driving methods are throughly examined. Then, the key technical challenges of soft upper limb exoskeleton are analyzed and expounded from four aspects: structure, material, control and auxiliary technology. Finally, considering the needs of exoskeleton applications in different fields, future trends in soft upper limb exoskeleton technology are speculated to focus on flexibility, comfort, compliance and intelligence. This study shows that the technology for wearable soft upper limb exoskeletons is still in its early stages, with many technical challenges to be solved. Futhurmore, breakthroughs in key technological challenges can be facilitated by novel soft actuators, soft sensors and other related advancements.

Abstract:

A fault tolerant control scheme based on integral sliding mode surface is developed for spacecraft attitude stabilization in the presence of actuator faults, misalignments, magnitude saturation and external disturbances simultaneously. This approach is based on a novel integral-type sliding mode control strategy to compensate for these un-desired issues without controller reconfiguration. Especially, it guarantees the reachability of the system states by involving adaptive control technique to relax the boundary information in advance. A sufficient condition for the controller to accommodate magnitude saturation is also presented and then the fault tolerant attitude control system can be guaranteed theoretically to be asymptotically stable by using Lyapunov method. Numerical simulation results shows that the proposed control law can quarantee the stability of the spacecraft attitude control system in the presence of actuators' failures, and it has good robust performance.

Abstract:

In view of the weaknesses of poor computing and storage capabilities of edge devices, lightweight processing was carried out on the backbone network CSPDarkNet53 for feature extraction in the traditional YOLOv5 model, and a lightweight gesture recognition algorithm MPE-YOLOv5 was proposed to realize the deployment of the model in low-power edge devices. Considering the problem that it is difficult to identify large-scale transformation targets and tiny targets due to less feature extraction in lightweight model, efficient channel attention (ECA) mechanism was added to alleviate the loss of information after high-level feature mapping due to the reduction of feature channel. A detection layer for tiny targets was added to improve the sensitivity to tiny target gestures. EIoU was selected as the loss function of the detection frame to improve the positioning accuracy. The effectiveness of the MPE-YOLOv5 algorithm was verified on the self-made dataset and NUS-Ⅱ public dataset, and the MPE-YOLOv5 algorithm was compared with lightweight M-YOLOv5 algorithm and original YOLOv5 algorithm on the self-made dataset. Experimental results show that the model parameters, model size, and computational complexity of the improved algorithm were 21.16%, 25.33%, and 27.33% of the original algorithm, and the average accuracy was 97.2%. Compared with the lightweight model M-YOLOv5, MPE-YOLOv5 improved the average accuracy by 8.72% while maintaining the original efficiency. The proposed MPE-YOLOv5 algorithm can better balance between the detection accuracy and real-time reasoning speed of the model, and can be deployed on edge terminals with limited hardware.

Abstract:

The advent of Industry 4.0 has spawned the widespread application of digital twin technology, providing digital solutions for intelligent manufacturing and product life-cycle management. In the field of civil engineering, the enhancement of digital disaster prevention and civil structure management is a critical component in the development of future smart cities. On one hand, the establishment of precise and reliable digital twins of real-life civil structures can facilitate disaster prevention from extreme hazards, as well as identify and warn against potential risks. On the other hand, digital twins lay the foundation for technological advancements in the digital construction and management of future cities. This study first categorizes the fundamental concepts and developmental stages of digital twin technology. Then, the acquisition of twining data and construction of digital twins for civil structures are systematically summarized. Building on this foundation, a comprehensive review and outlook is presented on the application of digital twin technology in civil engineering, encompassing the operation and maintenance of structures, disaster simulation and digital twin cities.

Abstract:

In the ultra-dense network environment, each access point is deployed in the hotspot area, which forms a complex heterogeneous network. Users need to choose the appropriate network to access, so as to achieve the best performance. Network selection problem is to choose the optimal network for the user, so that the user or network performance reaches the best. In order to solve the access selection problem of users in ultra-dense networks, we proposed an ultra-dense network access selection algorithm based on the improved deep Q network (DQN), considering network states, user preferences, and service types, and combining with load balancing strategies. First, by analyzing the influence of network attributes and user preferences on network selection, the appropriate network parameters were selected as the parameters of the access selection algorithm. Then, the problem of network access selection was modeled by Markov decision-making process, and the states, actions, and reward functions of the model were designed. Finally, the optimal network strategy was obtained by using DQN to solve the network selection model. In addition, the target function of traditional DQN was optimized to avoid overestimation of Q value by DQN, and a priority experience replay mechanism was introduced to improve learning efficiency. Simulation results show that the method could well solve the problem of overestimation of traditional DQN, accelerate the convergence of neural network, effectively reduce user congestion, and improve network throughput performance.

Abstract:

The existing graph-construction methods for graph optimization-based SLAM are summarized. The SLAM methods can be divided into three main classes, Kalman filter-based, partical filter-based and graph optimization-based, and the advantages and disadvantages of each class are overviewed. Moreover, there are mainly three graph modeling methods for the graph optimization-based SLAM problem, namely dynamic Bayesian network (DBN)-based model, factor graph-based model and Markov random field-based model. The key techniques of the front-end stage in graph optimization-based SLAM method, which mainly include data association between consecutive frame and loop closure detection, are discussed. Some newest research achievements on feature extraction, matching method, motion estimation, loop closure detection are introduced.

Abstract:

Autonomous navigation based on sequential images (ANBSI) is the key technology of pinpoint landing missions for future deep space exploration and also is one of the major development directions for deep space exploration technology. The necessity of developing ANBSI for planetary pinpoint landing is elaborated in this paper. Firstly, state-of-art developments of ANBSI are reviewed in terms of active sensing and passive sensing. Then, the key techniques applied in ANBSI for planetary landing are summarized and analyzed. Finally, according to the analysis of the key techniques, the main issues of ANBSI are raised and their future developments are overviewed.

Abstract:

Starting from the tactile sensing performance of human skin, the progress and key technologies of tactile sensors for e-skin (electronic skin) akin to human skin by multidisciplinary fields are comprehensively reviewed. The sensing principle, new materials and structures, advanced design and making methods, sensing characteristics and performance of tactile sensors are analyzed. The recent domestic and foreign research advances of electronic skin tactile sensor array in flexibility, elasticity, spatial resolution, sensitivity, fast response, transparency, lightweight, multifunction and other aspects are summarized. It is difficult to achieve the tactile sensors for e-skin with high stretchable and flexible, less complex production process for high sensitivity e-skin, strong extensibility and low cost. The tactile sensors for e-skin can be widely used in robotics, medical health, aeronautics and space military, intelligent manufacturing, automotive security and other fields. The development of tactile sensors for e-skin toward the direction of high stretchable and flexible, high sensitivity in wide range, multifunction, self-healing and self-cleaning, self-powered and transparent, has been pointed out.

Abstract:

In order to improve the separability of powdered activated carbon, a new type of magnetic activated carbon was prepared using chemical co-precipitation.Using methylene blue as target pollutants, performance of the powdered magnetic activated carbon was studied under varied conditions of pH, contact time and initial methylene blue concentrations, via the comparison with powdered activated carbon. The results showed that the adsorption capacity of synthetic magnetic powdered activated carbon was higher than that of the powdered activated carbon, and an alkaline pH value and adequate contact time were favorable for the pollutants removal. Under the condition of 100 mg/L methylene blue concentration, 0.4 g/L magnetic activated carbon dosage of, pH 9 and a reaction time of 300 minutes, the removal rate of methylene blue reached 98.9%. The adsorption behavior of methylene blue on magnetic activated carbon fitted the Langmuir isotherm and Elovich dynamics model. Thermodynamic analysis indicated that the adsorption was spontaneous endothermic reaction of single molecule layer, and the chemical adsorption played an important role during the adsorption process. The magnetic activated carbon had a good recyclable performance, it could complete precipitation in 10 minutes under natural condition, and could be quickly separated in 30 seconds under the action of outside magnetic field.

Abstract:

Considering the failure mechanism and weaknesses of traditional fabricated shear wall structures under strong earthquakes, a new type of fabricated shear wall with functions of energy dissipation and shock absorption was proposed. On the basis of model test and numerical simulation, seismic performance tests were carried out on four specimens with scale ratio of 1∶1.54 and shear span ratio of 1.52. Further analysis was conducted to investigate the effects of bolt number, axial compression ratio, and reinforcement ratio of edge members on the seismic performance of the new fabricated shear wall, including failure modes, hysteretic performance, bearing capacity, displacement ductility, stiffness degradation, and energy dissipation capacity. Test results show that the four specimens experienced shear compression failure, which was the same as the cast-in-place shear wall with the same shear span ratio. However, the proposed shear wall had better hysteretic performance and energy dissipation capacity, and the energy dissipation capacity was higher than that of the cast-in-place shear wall at the failure point. When the number of bolts decreased, the hysteretic performance of the new fabricated shear wall decreased, the wall deformation increased, while the bearing capacity remained almost unchanged. When the axial compression ratio or reinforcement ratio of edge members decreased, the bearing capacity decreased, and the ultimate displacement increased. Finally, the finite element model of the specimens was established by ABAQUS program. Comparisons of numerical results and test results showed a good agreement, verifying the correctness of the model, which can be applied to the analysis of the new fabricated shear wall.

Abstract:

To investigate the ground motion intensity measures suitable for evaluating high-rise structures under near-fault ground motions with pulse-like effect, this paper proposes a new ground motion intensity measure considering period elongation effect and higher mode effect based on acceleration spectrum. Taking two high-rise reinforced chimney structures (120 m and 240 m) as research objects, the correlation between damage indices (ParkAng damage index, maximum inter-story drift ratio, maximum structural curvature, maximum floor acceleration, and maximum roof displacement) of high-rise structures and 37 ground motion intensity measures was studied under near-fault ground motions using OpenSEES. Results show that the proposed intensity measure was the optimal index in predicting the ParkAng damage of high-rise concrete structures under near-fault ground motions. High correlation between velocity-related intensity measures and structural damage index was observed. As the structural period increased, the correlation between damage indices and displacement-related intensity measures was improved. Besides, peak ground acceleration had limitations in characterizing the deformation and failure of high-rise structures, but it could be used to analyze the seismic performance of non-structural components. The research results can provide reference for selecting proper measures and structural damage indices to evaluate the seismic performance of high-rise structures under near-fault ground motions.

Abstract:

In view of the problems of low brightness and obvious color distortion of the sky in restored images in most existing algorithms for image dehazing, a haze removal method for UAV aerial images based on atmospheric light value and graph estimation was proposed. First, the depth-of-field image was obtained according to the color attenuation prior theory, and the mean value of the region with the minimum deviation in the depth-of-field image was taken as the atmospheric light value. Then, a random walk clustering method was designed to estimate the atmospheric light map. The random walk algorithm was used to cluster the image into N sub-regions, and the mean value of the first 0.1% pixels of the sub-regions was taken as the regional atmospheric light value, which was then combined and refined by guided filtering to obtain the atmospheric light map. Next, the two atmospheric light estimators were fused into a new atmospheric light map with atmospheric light valuegraph estimation, which is a more accurate atmospheric light estimator. The transmittance was obtained by haze-lines prior method, and a dark compensation method was proposed to improve the transmission accuracy. Finally, according to the atmospheric scattering model, a clear restored image was obtained based on the fused atmospheric light map and optimized transmittance. Experimental results show that compared with other algorithms, the proposed algorithm improved the information entropy, mean gradient, blur coefficient, and contrast by 1.1%, 6.3%, 8.5%, and 6.4%, respectively, with better subjective visual effect and more abundant information.

Abstract:

This paper proposes a low-latency intelligent network data transmission scheduling algorithm for real-time network transmission demand scenarios of low latency, stable transmission, and high quality of experience (QoE). The algorithm consists of two parts: data block queuing control strategy and congestion control strategy. The data block queuing control strategy presents a cost-effective model that integrates the creation time and effective time of data blocks, effectively solving the problem of uneven information transmission under transmission time constraint. The congestion control strategy proposes a deep deterministic policy gradient (DDPG) method based on the Gumbel distribution sampling reparameterization with mixed experience prioritization model, which solves the problem that DDPG is not applicable to the congestion control of discrete network action space and significantly improves the quality of network congestion control by adaptively adjusting the sending parameters through learning. Results show that the proposed queuing algorithm could effectively improve QoE in real-time transmission scenarios, and the improved DDPG for congestion control could significantly reduce transmission delay. In the same scenario, compared with traditional network data transmission scheduling algorithms, by integrating the proposed queuing and congestion control strategies, the improved intelligent network data transmission scheduling algorithm could maintain a good balance between low latency and stable transmission and provide higher data transmission quality.

Abstract:

In view of the problem that general pre-trained models are not suitable for named entity recognition tasks in the medical domain, a neural network architecture that integrates knowledge graph in the medical domain was proposed. The elastic position and masking matrix were used to avoid semantic confusion and semantic interference in self-attention calculation of pre-trained model. The idea of multi-task learning in fine-tuning was adopted, and the optimization algorithm of recall learning was employed for pre-trained model to balance between general semantic expression and learning of the target task. Finally, a more efficient vector representation was obtained and label prediction was conducted. Experimental results showed that the proposed architecture achieved better results than the mainstream pre-trained models in the medical domain, and had relatively good results in the general domain. The architecture avoided retraining pre-trained models in particular domain and additional coding structures, which greatly reduced computational cost and model size. In addition, according to the ablation experiments, the medical domain was more dependent on the knowledge graph than the general domain, indicating the effectiveness of integrating the knowledge graph method in the medical domain. Parameter analysis proved that the optimization algorithm which used recall learning could effectively control the update of model parameters, so that the model retained more general semantic information and obtained more semantic vector representation. Besides, the experimental analysis showed that the proposed method had better performance in the category with a small number of entities.

Abstract:

Image segmentation is to divide the region with special meanings into several disjoint sub-regions according to certain rules, which is the key link between image processing and image analysis. The traditional watershed image segmentation method is widely used, which has the advantages of fast and simple. However, it is easily interfered by noise, and the segmentation results are prone to lose important edge information, resulting in over-segmentation. In view of the problem of the traditional watershed image segmentation method, an improved watershed image segmentation method based on adaptive structural elements was proposed. First, the adaptive structural elements with variable shapes were constructed by using local density, symmetry, and boundary features of adjacent pixels of image targets, so as to ensure a good consistency between the proposed structural elements and the shape of image targets. Then, the adaptive structural elements were used to obtain the morphological gradient of the image, which could improve the positioning accuracy of the target edge. The L₀ norm gradient minimization and morphological open-close hybrid reconstruction were used to modify the gradient image, so as to reduce the local invalid minimum points in the gradient image and suppress the occurrence of over-segmentation. Finally, watershed segmentation was performed on the modified gradient image to realize accurate segmentation of the target region of the image. Experimental results show that the method could effectively restrain over-segmentation of traditional watershed algorithm and improve the accuracy of the target edge positioning, with high precision of image segmentation.

Abstract:

The development of advanced nano-integrated circuit processes has led to a decreasing threshold charge in microelectronic devices, resulting in an increased rate of soft errors caused by single-event effects in digital circuits. To enhance the radiation resistance of standard cells in integrated circuits, this paper proposes a NAND gate structure that is resistant to single-event transients (SETs). In the triple well process, by shorting the substrate and source of each NMOS transistor in the pull-down network, the radiation resistance of the NAND gate was effectively improved, and the hardening of the proposed NAND gate became more effective as the number of inputs increased. Particle incidence simulation experiments were performed by Sentaurus TCAD software in hybrid simulation mode. For the NMOS transistor connected to the output node, the three-dimensional physical model that has been calibrated by the process was used, and the Spice model provided by the manufacturer was adopted for other MOS transistors. Simulation results show that the proposed two-input NAND in 40 nm process could reduce the output voltage fluctuation amplitude in three-input cases at the linear energy transfer (LET) value of incidence particle of 10 MeV·cm²/mg. Besides, the effect of immunity to single particle incidence was achieved in the input mode with N₂ transistor closed. For the hardened three-input NAND gate, the output voltage disturbance could be reduced by up to 85.4% even in the “worst case”. Therefore, the proposed hardening method for NAND gate has a significant effect against SET.

Abstract:

To study the connection method between reinforced bars of the precast concrete(PC) structure,81 plug-in filling hole for lap-joint of steel bar sample tests were made,and the main factors,such as reinforced bar diameter,concrete strength and anchorage length etc.were considered during the tests.The test results indicated that the ultimate failure state of all the anchoring specimens were the external reinforced bar yielding or broken up by pulling,and the abnormal anchorage was not destroyed.When the basic anchoring length was reduced by 10% or even 20%,specimens still showed enough safety.Based on this,the basic anchoring length of plug-in filling hole for lap-joint of steel bar can be given as 0.8 la.

Abstract:

Due to the complex background of ship targets and much irrelevant interference in visual images, it is difficult to conduct ship detection. In addition, there are few datasets for multi-category ship detection and the samples are often unbalanced, which makes the ship target detection performance degraded. Considering the ship detection background interference, an improved YOLOv3 model was proposed by introducing SimAM attention mechanism, which was used to enhance the weight of the ship target in the extracted features and suppress the weight of background interference, thus improving the model detection performance. Meanwhile, strong real-time data augmentation was applied to improve the unbalanced distribution of sample scales, and transfer learning was combined to improve the ship detection accuracy in the condition of a restricted number of samples. The visualization results of extracted features show that the improved model could suppress irrelevant background features, and the abilities of feature extraction and target localization were enhanced. Without introducing additional learnable parameters, the proposed model achieved 96.93% and 71.49% for mAP.5 and mAP.75 on the SeaShips dataset, and detection speed reached 66 frames per second, indicating a good balance between detection accuracy and efficiency. The improved model optimized the target features more effectively compared with the Saliency-aware CNN and eYOLOv3 models, resulting in an improvement of mAP.5 by 9.53% and 9.19%. The mAP.5 for ship type target detection on Singapore Maritime Dataset reached 81.81%, indicating that the proposed model has good generalization performance.

Abstract:

Carbon dioxide (CO₂) capture, utilization and storage, as an emerging technology that can help reduce coal chemical plant greenhouse gas emission by large scale, have drawn significant attention. Pipeline transportation is an essential part of the technology, but high cost has greatly limited its application. Therefore the main objective is to develop an optimization model for supporting CO₂ pipeline transportation system planning to reduce the overall carbon capture utilization and storage (CCUS) system cost by optimizing key technology process of a CO₂ transportation system. The developed model was further applied to Shaanxi Yanchang's CCUS project for planning its CO₂ transportation system. The results indicated that in case of low demand of CO₂ storage, a gas-phase CO₂ pipeline transportation system coupled with in-situ compression and injection was recommended. In the case of high demand of CO₂ storage, this study would recommend a super-critical / density phase transportation system which could have lower system cost than gas phase pipeline system as the cost for compression at the site of storage can be saved