Abstract:The courtyard space of exhibition buildings carries multiple functions, such as outdoor exhibitions, art fairs, plant displays, and entertainment, which are closely related to the outdoor wind environment. In the deliberation of existing design schemes, the size and layout of the courtyard mainly rely on the subjective experience of the architects, lacking of relevant thinking between courtyard design and outdoor wind environment. This paper collects the courtyard area of 48 well-known exhibition buildings, obtains the quadratic function relationship between the ground floor area of the exhibition building and the courtyard area through scatter curve fitting, and proposes five typical layout models. On the basis of the climatic conditions in the hot summer and cold winter area (taking Hangzhou as an example), the influence of the outdoor wind environment was evaluated by the proportion of the suitable wind environment and the stability of the wind speed. Results show that the proportions of suitable wind environment areas for U-shaped enclosures (east-facing courtyards), segmented enclosures (west-facing courtyards), and segmented enclosures (east-facing courtyards) maintained at 99%, which can provide an ideal outdoor exhibition space. The variance of the measuring points in the U-shaped enclosure (east-facing courtyard) was small and the wind speed of the courtyard was stable, which meets the wind environment requirements of outdoor exhibitions. This research provides design reference for exhibition buildings with suitable wind environment.

Abstract:Frequent icing disasters on transmission lines have brought a great threat to the power grids. Most existing studies ignored the effects of time-varying meteorological conditions. To effectively predict the growth in line icing and provide reference for transmission line deicing and early warning of galloping, on the basis of the icing mechanism of conductors and the Euler gas-liquid two-phase flow model, we proposed a numerical simulation method for icing of transmission lines under the influence of time-varying meteorological parameters. A numerical simulation framework of conductor icing growth was established based on FLUENT and FENSAP-ICE software. The influence of different parameters on the quality and shape of the ice coating on the conductor of the transmission line was analyzed using 3D simulation model. Results show that the ice coating shape of the conductor was greatly affected by the wind speed, the median volume diameter of the supercooled water droplet, the liquid water content in the air, and the diameter of the conductor, but little affected by temperature. Finally, the validity of the simulation calculation method was verified by using the on-site icing monitoring data of real transmission lines and the corresponding meteorological data. The prediction results were in good agreement with the monitored results of transmission line icing, indicating that the proposed method has good accuracy.

Abstract:Multiple input multiple output synthetic aperture radar (MIMO SAR) can solve the high resolution and wide swath imaging conflicts of traditional SAR. To improve the suppression capability of MIMO SAR for echo signal interference between different channels, we adopted multidimensional waveform encoding technology to design system and studied the waveform design of MIMO SAR. Two typical signals in MIMO SAR (positive and negative linear frequency modulation signal and orthogonal frequency division modulation chirp) were selected and compared with short term shift orthogonal (STSO) signals. Considering the characteristics of multiple apertures simultaneously transmitting signal and receiving echo, MIMO SAR was combined with multidimensional waveform encoding technology for echo processing. Digital beam forming technology was used to divide multiple subswaths in distance dimension to achieve large mapping band imaging. In azimuth dimension, multichannel reconstruction algorithm was adopted to restore the periodic non-uniform sampling echo to uniform sampling, so as to achieve high resolution imaging, and then the design and imaging of the new SAR transmitting signal were carried out. Experimental results show that compared with other two signals, STSO signal had the best performance in terms of peak side lobe ratio, integral side lobe ratio, and resolution, which could suppress the fuzzy energy more thoroughly. STSO signal performed well in MIMO SAR, which was similar to that in traditional SAR. Besides, STSO had better focusing performance in MIMO SAR system with good imaging indexes, which could reduce system complexity.

Abstract:The payload launched to deep space can be increased by using lunar gravity assist (LGA). There are two main methods for designing LGA transfer trajectory. The forward patched method firstly parameterizes the constrained orbit and then optimizes the orbit with the global optimization algorithm. The forward method is intuitive, but has many optimization parameters, which causes time-consuming calculation and poor convergence stability. The backward patched method firstly determines the optimal orbit of the heliocentric segment and then calculates the geocentric LGA. The backward patched method consumes less calculation time, but with poor accuracy, and it is not easy to converge to the global optimal solution. To solve the LGA escape trajectory in a short time, this paper proposes a hybrid optimization method based on the existing methods. The proposed hybrid optimization method makes full use of the advantages of fast convergence speed of backward patched method and high precision of forward patched method. The method takes the trajectory corresponding to the Pareto solution set of the multi-objective optimization of the backward patched method as the reference trajectory, and carries out local optimization near the reference trajectory. Then, the forward method is applied to quickly obtain the optimal transfer trajectory by using the global search algorithm. Numerical simulation was carried out for the detection of near-Earth asteroids 1989 ML and 2003 SM84. The results show that compared with the forward patched method, the hybrid optimization method improved the convergence stability and computational efficiency effectively. The convergence stability of the solution was improved to 3-4 times, and the calculation time was reduced by 50%. The results indicate that the hybrid optimization method excels in convergence stability and computational efficiency.

Abstract:Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR), which can maintain maximum transmission rate and minimum latency in a network link. However, the BBR algorithm was reported to cause serious round trip time (RTT) fairness problems by some evaluation experiments. The impact of the mismatch between pacing rate and bottleneck bandwidth caused by the asynchronous detection mechanism of BBR algorithm was analyzed to optimize the RTT fairness, and an optimized algorithm BBR-adaptive (BBR-A) was proposed based on pacing gain model. According to the relationship between RTT and pacing gain, a pacing gain adjustment model based on inverse proportional function was established, which replaces the fixed pacing gain coefficient in the original BBR algorithm. By interleaving the up and down pacing gain coefficients to adjust the pacing rate, each BBR flow could compete for bandwidth resources fairly. Experimental results of network simulator 3 (NS3) show that the channel utilization of BBR-A algorithm was slightly improved compared with BBR algorithm. In the experiment of RTT fairness, BBR-A reduced the throughput difference between different RTT flows, and Jain fairness index was at least 1.5 times higher than BBR algorithm with different buffer sizes and RTT differences. The retransmission rate of BBR-A algorithm was significantly reduced. By adaptively adjusting the pacing gain coefficient, the pacing rate between different flows was balanced, and the RTT fairness of BBR algorithm was improved.

Abstract:Deep learning-based image inpainting methods leave little trace information in the tampered image, which makes forensics extremely difficult. There are few studies on inpainting forensics, and the localization of tampered areas is inaccurate. Therefore, a dynamic feature fusion forensics network (DF³Net) was proposed for locating tampered areas that have undergone deep image inpainting operations. Firstly, the network expanded single input to multi-inputs by exploiting different tamper trace enhancement methods including spatial rich model (SRM) filtering, high-pass filtering of spatial domain, and high-pass filtering of frequency domain. Then, a dynamic feature fusion module was proposed to extract effective inpainting trace features and conduct dynamic feature fusion. Secondly, the network adopted the encoder-decoder architecture as basic framework, and a multi-scale feature extraction module was added at the end of the encoder to obtain contextual information at different scales. Finally, a spatially weighted channel attention module was designed for the skip connection between encoder and decoder, so as to achieve a focused supplementation of the lost details. Experimental results show that DF³Net could locate the tampered areas more accurately than existing methods on different datasets, and was robust against JPEG compression and Gaussian noise.

Abstract:The indoor physical environment of buildings such as light environment, wind environment, and temperature and humidity field has great impact on the life quality of users, so the design of indoor physical environment is especially important. Surrogate model is an effective rapid prediction method of building performance, which can help architects in selecting or iteratively adjusting the plan at the initial stage of design. However, most existing surrogate models focus on calculating single valued data such as energy consumption and daylighting coefficient, lacking of calculation methods for “matrix” data (i.e. cloud map) such as illuminance distribution, wind field, and temperature and humidity field. To this end, a building performance prediction method was proposed combined with the conditional generative adversarial network (CGAN). Based on the pix2pix algorithm, the CGAN model of cloud images of illuminance and wind field was constructed, and the mapping relationship between the images was established. The performance data of the corresponding plane were generated by inputting the building outline, so as to rapidly predict the building performance and improve the efficiency of the initial design of the building. In order to evaluate the accuracy of the prediction results, we established an evaluation system based on the channel histogram method and the grayscale value method, which was applied for similarity verification. Results show that the trained architectural CGAN model could quickly draw cloud images within 1 s, which was much faster than traditional simulation software, and the prediction results were highly consistent with the simulation calculation results. The similarity between the results reached 93.82% (illuminance) and 82.99% (wind field), which proved the effectiveness of the prediction method of building indoor matrix data based on CGAN model.

Abstract:Visual display terminal (VDT) and paper are indispensable reading mediums in peoples life. Providing suitable lighting environment for VDT and paper interactive reading is an important development direction to improve the quality of indoor lighting environment and meet public health needs. In order to explore the impact of lighting environment on the vision and cognition of interactive reading, we conducted subjective and objective evaluation experiments by using a combination method of subjective evaluation, visual task performance, and physiological index measurement. An experimental platform was established to study the effects of four lighting parameters (horizontal illuminance, illuminance ratio, color temperature, and screen brightness) on the visual perception (visual comfort, visual efficacy, and visual fatigue) and cognitive performance (cognitive efficiency and emotional arousal) of interactive reading. Experimental results show that for interactive reading, horizontal illuminance had the greatest impact on visual fatigue. Illuminance ratio had a significant impact on visual perception and cognitive performance. The color temperature of the light source had the greatest impact on visual comfort and visual efficacy. The brightness of the VDT screen had the greatest impact on visual fatigue, cognitive efficiency, and emotional arousal. According to parameter analysis, the optimal lighting environment parameters for interactive reading with high visual comfort and high emotional arousal are horizontal illuminance 750 lx, vertical illuminance 300 lx (illuminance ratio 2.5), and color temperature 4 000 K, and the best screen brightness is 100 cd/m². The optimal lighting environment parameters for interactive reading with high visual efficiency, high cognitive efficiency, and low visual fatigue are horizontal illuminance 300 lx, vertical illuminance 200 lx (illuminance ratio 1.5), and color temperature 5 000 K, and the recommended screen brightness is 100 cd/m². The results can provide basis for the lighting design and optimization of interactive reading.

Abstract:The shear transfer pathway in the shear span of high-strength concrete beam was analyzed under shear failure to investigate the shear mechanical property of the inclined section of high-strength concrete beam. The contribution of high-strength concrete to the shear capacity of beam was considered based on the beam action and arch action. The stirrup contribution was determined by using the simplified modified compression field theory, and the influence of size effect was taken into consideration. Through the combination of concrete and stirrup terms, the calculation model for the shear capacity of high-strength concrete beam under concentrated load was established, which has a clear and reasonable physical significance. The effects of concrete strength, shear-span ratio, relative depth of shear compression zone, ratio of longitudinal reinforcement, ratio of stirrup, and size effect were comprehensively analyzed. In addition, the test data of 207 high-strength concrete beams with web reinforcement were employed to evaluate the proposed calculation model, and compared with the prediction results of GB 50010—2010, ACI 318-19, EN 1992-1-1:2004, Zararis formula, and Zsutty formula. Results show that the proposed model could reflect the shear failure mechanism of the inclined section of beam and predict the shear capacity of high-strength concrete beams. Besides, the model was less sensitive to the wide range variation of shear parameters, and the prediction accuracy had no obvious correlation. Thus, in combination with beam-arch action and simplified modified compression field theory, the proposed shear model can be used to calculate the shear capacity of high-strength concrete beams with high accuracy and stability.

Abstract:Low density parity check code (LDPC) is a widely used channel coding, especially in long code. Corresponding to coding is decoding. The complexity of traditional LDPC decoding algorithm is high. Approximate operation has been adopted in the minimum sum (MS) decoding algorithm to reduce the complexity. Although the complexity can be effectively reduced, some BER performance is sacrificed. In view of the problem, we proposed a class fitting modified minimum sum (CFMMS) decoding algorithm, which performs the approximate operation for a second time based on the MS decoding algorithm. The algorithm constructs a fitting function according to the nonlinear function in MS algorithm, which can make different processing for the variable node information in different thresholds, and achieve accurate compensation for the updating process of verification nodes, so that the obtained results are closer to the confidence propagation algorithm. On the basis of the hierarchical scheduling strategy, a layered class fitting modified minimum sum (LCFMMS) decoding algorithm was proposed, which can change the update order of node information, improve the reliability of node information in iterative update, accelerate the convergence speed of decoding, and save storage space. Simulation and numerical results show that the proposed decoding algorithm improved bit-error rate (BER) performance to a certain extent, and had low computational complexity and fast decoding convergence speed.

Abstract:The fast detection of obstacles is the key technology for the navigation, obstacle avoidance, and trajectory tracking of mobile robots. Most sensors have problems such as distance blind zone and abnormal scale, and the existing algorithms are generally limited by complex calculation, making it difficult to meet real-time requirements. This paper proposes a new method of rapid obstacle detection and localization for mobile robots based on the difference of inverse perspective mapping. The method takes the level ground as the reference plane to perform inverse perspective transformation on the images captured by the adjacent view cameras in the around view monitoring system mounted on the mobile robots, and obtains the inverse perspective mapping difference by image difference. In the images, the point with zero pixel value is located on the reference plane; otherwise it is outside the plane. After thresholding and filtering operation, the method can be used to distinguish targets not on the reference plane, so as to realize the fast detection of obstacles. Besides, obstacles can be located accurately on the basis of the inverse projection mapping difference through the transformation from the pixel coordinate system to the coordinate system of the robot. Different types, sizes, and distances of obstacles within 5 m were tested. The average detection accuracy was 97.3%, the average detection time per frame was 46 ms, and the average localization error was 1.1%. The experimental results show that the proposed method can quickly and effectively detect and locate the dynamic and static obstacles near the mobile robot.

Abstract:Considering the poor performance of existing co-saliency detection algorithms in multiple salient object complex scenarios, a co-saliency detection algorithm with efficient channel attention and feature fusion was proposed. Firstly, the pre-trained deep convolutional neural network was adopted to extract multi-scale features of the images, and a saliency semantic feature extraction module with edge saliency feature was designed to avoid the lack of edge information caused by fully convolutional neural networks. Secondly, the association information between images in the group was obtained based on the inner product principle, and adaptive weighting was carried out according to the association degree; a collaborative feature extraction algorithm was designed in combination with the attention layer of efficient channel. Finally, a feature fusion module based on efficient attention layer was designed, so as to fuse the results of co-saliency feature extraction at high-level semantic features with low-level features, and supervise the predictions with multi-branches simultaneously. Three classic datasets were tested, and six existing collaborative saliency detection algorithms were compared with the proposed algorithm. Results show that the proposed algorithm not only improved the accuracy of collaborative saliency detection and the richness of edge information in complex scenarios, but also had better performance of collaborative saliency detection. The effectiveness and necessity of each designed module were further verified by ablation experiments.

Abstract:The mechanical properties of the connection joints/horizontal joints of the fabricated shear wall structure play a vital role in the fabricated structure. To improve the seismic performance of the fabricated shear wall structure, we designed and developed a new type of horizontal connection device based on the energy dissipation technology. Through the design, two kinds of horizontal connection devices with different numbers of bolts were prepared. The dynamic mechanical properties of the devices under different sinusoidal loading frequencies were tested, and the influences of bolt preload, loading frequency, and bolt number on the mechanical properties of the device were explored. The influence of temperature rise caused by friction on the mechanical properties of the device was investigated. Results show that the device could ensure the reliable connection of the structure and meanwhile consume much seismic energy due to the internal friction, so as to effectively reduce the seismic response of the structure and protect the main structure. In addition, the bolt preload linearly correlated with the energy dissipation capacity and secant stiffness of the device, but had little effect on the equivalent damping ratio. The mechanical properties of the horizontal connection device were hardly affected by the loading frequency. Increasing the number of bolts could greatly improve the energy consumption capacity of the device. Bolt preload and loading frequency were important factors for the temperature rise in the device, but the temperature change in the device had little effect on its mechanical properties.

Abstract:The basic salp swarm algorithm (SSA) may suffer from the drawbacks of slow convergence and low accuracy of high-dimensional solutions. To solve these limitations, we proposed an improved SSA algorithm (OOSSA) which integrates orthogonal refracted opposition-based learning strategy and self-adaptive inertia weight strategy into SSA. In orthogonal refracted learning strategy, the refracted opposition-based learning based on the optical lens imaging principle was employed to enhance the exploration scope of the inverse solution space, which greatly reduced the probability of the algorithm falling into the local optimum. The orthogonal experimental design was used to construct several partial opposite solutions that take the refracted-based inverse values in part of the dimension, so as to deeply mine and preserve the dominant dimensional information of the current individual and the refracted-based opposite individual. In addition, an adaptive inertia weight was introduced in the follower position update phase to effectively improve the follower search pattern and enhance the local exploitation ability of the algorithm. The CEC2017 benchmark functions were employed for simulation experiments. Also, Wilcoxon’s rank-sum test and Friedman test were performed to verify the superiority of the proposed method. Experimental results show that the proposed OOSSA outperformed the basic SSA, eight improved SSA variants, and nine cutting-edge swarm-based intelligence algorithms. Moreover, the algorithm was applied to an engineering design problem, and results show that the algorithm had better performance than other algorithms in engineering optimization. Finally, an OOSSA-based robot path planning approach was developed for solving the path planning problem in autonomous mobile robots. The proposed algorithm was simulated in three environment settings and compared with other algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), grey wolf optimizer (GWO), firefly algorithm (FA), and SSA. Simulation results show that the proposed algorithm could plan the optimal collision-free paths compared with its competitors. The systematic experiments indicate that the OOSSA algorithm can be an effective tool for problem optimization.

Abstract:In view of the problems of poor global search ability and weak adaptive ability of mayfly algorithm, an enhance global search and adaptive mayfly algorithm (MIWMA) was proposed. Firstly, the non-uniform Gaussian mutation strategy was adopted to update the position of male mayfly and female mayfly, guide the global optimal position mutation to leading other individuals to approach the good position, and promote the population to have certain guidance, so as to improve the global search ability and enhance the diversity of the population. Secondly, the adaptive inertia weight of incomplete gamma function and beta cumulative distribution was introduced to establish a better balance between the global search and development ability, regulate the global search and local search ability of the population, and then improve the convergence accuracy of the algorithm, which is conducive to the potential of the global search of the population to find the optimal solution. The local stagnation countermeasure strategy was introduced. On the basis of the iterative stagnation, the inertia part and social part of mayfly speed update were adjusted to make it have the optimal search state and enhance the global search ability of the algorithm. The classic test function set and IEEE CEC2021 test competition set were used for test optimization comparison to verify the effectiveness and robustness of the algorithm. Results show that the proposed algorithm had better stability, robustness, and reliability by using Friedman and Wilcoxon rank sum test. Finally, two engineering problems were used for optimization. The results verified the applicability of the algorithm in engineering optimization problems and are suitable for solving optimization problems requiring high precision.