Most view articles
2017, 49(3):1-14.
DOI: 10.11918/j.issn.0367-6234.2017.03.001
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.
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.
2024, 56(2):1-9.
DOI: 10.11918/202211085
Abstract:
The self-centering buckling-restrained brace (SBRB) was formed by making a self-centering system consisting of stacked disc springs (DS) and a buckling-restrained brace (BRB) work in parallel to control the residual deformation of BRB. Quasi-static tests were done to examine the effects of stiffness of stacked springs, end connections, self-centering ratios, etc. on the hysteretic behavior of SBRBs. The tests revealed that, compared with BRBs, the residual deformations of SBRBs were reduced greatly. SBRBs exhibited flag-shaped hysteretic curves, and in the later stage of tests, the steel plate brace underwent tensile failure while other components remained intact. The bearing capacity and energy dissipation capacity of SBRBs are mainly from the DS parts and BRB parts, respectively. Approximately 23%-36% energy dissipation in SBRBs is from the DS parts due to the friction between stacked springs. When the other constructional details remained the same, the DS parts with a higher stiffness of springs had a greater increase in bearing capacity after starting, and the DS parts with a higher starting force also had a higher self-centering ratio and smaller residual deformations. On the whole, the end connections had little effect on the residual deformations. The tension fracture of steel plate brace occurred earlier in the SBRB with rigid end connections due to bearing additional bending moments, while the SBRBs with pin end connections exhibited better energy dissipation. With the increase of self-centering ratios, the residual deformations of SBRBs reduced gradually and the appropriate ratios of SBRBs should be kept within 0.7-0.9 to efficiently control residual deformations and to avoid excessive demand of DS parts.
The self-centering buckling-restrained brace (SBRB) was formed by making a self-centering system consisting of stacked disc springs (DS) and a buckling-restrained brace (BRB) work in parallel to control the residual deformation of BRB. Quasi-static tests were done to examine the effects of stiffness of stacked springs, end connections, self-centering ratios, etc. on the hysteretic behavior of SBRBs. The tests revealed that, compared with BRBs, the residual deformations of SBRBs were reduced greatly. SBRBs exhibited flag-shaped hysteretic curves, and in the later stage of tests, the steel plate brace underwent tensile failure while other components remained intact. The bearing capacity and energy dissipation capacity of SBRBs are mainly from the DS parts and BRB parts, respectively. Approximately 23%-36% energy dissipation in SBRBs is from the DS parts due to the friction between stacked springs. When the other constructional details remained the same, the DS parts with a higher stiffness of springs had a greater increase in bearing capacity after starting, and the DS parts with a higher starting force also had a higher self-centering ratio and smaller residual deformations. On the whole, the end connections had little effect on the residual deformations. The tension fracture of steel plate brace occurred earlier in the SBRB with rigid end connections due to bearing additional bending moments, while the SBRBs with pin end connections exhibited better energy dissipation. With the increase of self-centering ratios, the residual deformations of SBRBs reduced gradually and the appropriate ratios of SBRBs should be kept within 0.7-0.9 to efficiently control residual deformations and to avoid excessive demand of DS parts.
2016, 48(4):20-25.
DOI: 10.11918/j.issn.0367-6234.2016.04.003
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.
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.
2024, 56(8):1-16.
DOI: 10.11918/202401061
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.
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.
2023, 55(5):107-113.
DOI: 10.11918/202204106
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.
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.
2016, 48(4):1-12.
DOI: 10.11918/j.issn.0367-6234.2016.04.001
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.
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.
2023, 55(5):1-13.
DOI: 10.11918/202206056
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.
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.
2015, 47(1):75-85.
DOI: 10.11918/j.issn.0367-6234.2015.01.012
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.
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.
2016, 48(2):50-56.
DOI: 10.11918/j.issn.0367-6234.2016.02.009
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.
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.
2017, 49(1):1-13.
DOI: 10.11918/j.issn.0367-6234.2017.01.001
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.
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.
2023, 55(5):78-87.
DOI: 10.11918/202112059
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.
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.
2023, 55(5):139-150.
DOI: 10.11918/202112016
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.
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.
2023, 55(5):88-97.
DOI: 10.11918/202111001
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.
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.
2023, 55(5):50-58.
DOI: 10.11918/202201126
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.
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.
2023, 55(5):132-138.
DOI: 10.11918/202112138
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.
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.
2023, 55(5):59-70.
DOI: 10.11918/202204057
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.
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.
2024, 56(2):28-36.
DOI: 10.11918/202211080
Abstract:
To explore the effects of lightweight sand on the strain hardening properties of ultra high performance concrete (UHPC) with different specimen sizes, the lightweight sand was used to replace the yellow sand by equal volume. Nine groups of uniaxial tensile tests were carried out with different lightweight sand volume rate ranging from 0 to 35% and different specimen thicknesses from 30 mm to 100 mm. Meanwhile, simultaneous acoustic emission real-time flaw detection tests were conducted. Results show that the volume rate of lightweight sand demonstrates little effect on the stress and strain at the elastic limit point of UHPC, but when the volume rate of lightweight sand increases from 0 to 35%, the ultimate tensile strength and ultimate tensile strain of UHPC grow from 10.6 MPa and 2.35×10-3 to 19.4 MPa and 4.3×10-3 respectively. When the volume rate of lightweight sand is greater than 15%, the strain hardening degree of UHPC significantly increase with more damage points generated and more uniformly distributed inside the specimen, showing remarkable crack control capability. With the same lightweight sand volume rate, the strain hardening degree of UHPC decreases with the increase of specimen thickness, and the damage points inside the specimen tend to be concentrated, exhibiting an obvious size effect.
To explore the effects of lightweight sand on the strain hardening properties of ultra high performance concrete (UHPC) with different specimen sizes, the lightweight sand was used to replace the yellow sand by equal volume. Nine groups of uniaxial tensile tests were carried out with different lightweight sand volume rate ranging from 0 to 35% and different specimen thicknesses from 30 mm to 100 mm. Meanwhile, simultaneous acoustic emission real-time flaw detection tests were conducted. Results show that the volume rate of lightweight sand demonstrates little effect on the stress and strain at the elastic limit point of UHPC, but when the volume rate of lightweight sand increases from 0 to 35%, the ultimate tensile strength and ultimate tensile strain of UHPC grow from 10.6 MPa and 2.35×10-3 to 19.4 MPa and 4.3×10-3 respectively. When the volume rate of lightweight sand is greater than 15%, the strain hardening degree of UHPC significantly increase with more damage points generated and more uniformly distributed inside the specimen, showing remarkable crack control capability. With the same lightweight sand volume rate, the strain hardening degree of UHPC decreases with the increase of specimen thickness, and the damage points inside the specimen tend to be concentrated, exhibiting an obvious size effect.
2024, 56(2):18-27.
DOI: 10.11918/202210012
Abstract:
In order to accurately predict the rotational stiffness of embedded steel column bases, shallowly embedded, in particular, this paper proposes a stiffness model that collectively allows for the effects of the embedded depth, shear deformation of the embedded column, axial force applied to the column and restraint from the base plate. For developing the model, the embedded segment of the column is simplified as a beam on the Winkler foundation, and its deformation is computed using the Timoshenko beam theory on Winkler foundation, considering the influence of axial force applied to the column and solved by initial parameter method. To investigate the influence of the above-mentioned factors and verify the proposed model, finite element (FE) models are developed and validated against existing experimental results. Comparisons between the FE results and model predictions show that the proposed model exhibits higher accuracy than the theoretical models reported, especially for shallowly embedded column bases. The model prediction and FE results indicate that the rotational stiffness of the column base depends on the embedded depth in a nonlinear fashion-the stiffness increases rapidly for small embedded depth but remains almost constant when embedded depth is larger than a certain value; the axial compression load applied to the steel column is a beneficial factor to the stiffness; the restraint from the base plate shows a significant effect on the rotational stiffness of embedded column bases with the embedded depth ratio less than 1.5, being negligible when the embedded depth ratio increases up to 2.5.
In order to accurately predict the rotational stiffness of embedded steel column bases, shallowly embedded, in particular, this paper proposes a stiffness model that collectively allows for the effects of the embedded depth, shear deformation of the embedded column, axial force applied to the column and restraint from the base plate. For developing the model, the embedded segment of the column is simplified as a beam on the Winkler foundation, and its deformation is computed using the Timoshenko beam theory on Winkler foundation, considering the influence of axial force applied to the column and solved by initial parameter method. To investigate the influence of the above-mentioned factors and verify the proposed model, finite element (FE) models are developed and validated against existing experimental results. Comparisons between the FE results and model predictions show that the proposed model exhibits higher accuracy than the theoretical models reported, especially for shallowly embedded column bases. The model prediction and FE results indicate that the rotational stiffness of the column base depends on the embedded depth in a nonlinear fashion-the stiffness increases rapidly for small embedded depth but remains almost constant when embedded depth is larger than a certain value; the axial compression load applied to the steel column is a beneficial factor to the stiffness; the restraint from the base plate shows a significant effect on the rotational stiffness of embedded column bases with the embedded depth ratio less than 1.5, being negligible when the embedded depth ratio increases up to 2.5.
2023, 55(5):114-121.
DOI: 10.11918/202109131
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.
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.
2023, 55(5):14-21.
DOI: 10.11918/202201069
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.
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.
