Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core cros...Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
In this paper,the topological structure of the vehicle wireless network M2M(Machine to Machine)is used as the experimental research model,and four kinds of light coefficients are set as factors affecting the experimen...In this paper,the topological structure of the vehicle wireless network M2M(Machine to Machine)is used as the experimental research model,and four kinds of light coefficients are set as factors affecting the experimental results,namely,light intensity factor ∈ and α,to represent the light intensity coefficient and influence factor.The remaining energy consumption of mobile terminal equipment was measured respectively,the distance parameter from device to device,the maximum transmission energy consumption,and the correlation coefficient between environmental parameters and energy consumption parameters was analyzed.This paper discusses the impact of different topological structures on the environment,energy saving and emission reduction in the relatively flat terrain area,based on the planning scheme of parking area within the coverage range of base station signal,the transmission capability of vehicles as mobile device nodes within the coverage range of base station signal,and the signal coverage range of base station under different light intensity.As the distance between the base station and the vehicle mobile device node changes,the maximum transmission energy consumption of the mobile device node is obtained.Based on the above factors,the optimal performance optimization parking scheme and the optimal energy consumption optimization transmission scheme are obtained.展开更多
With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impu...With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.展开更多
From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction veh...From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.展开更多
Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, networ...Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.展开更多
The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet d...The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet dropout and transmission delay, a piecewise constant controller is adopted. With a guaranteed cost function, optimal controllers whose number is equal to the number of actuators are designed, and the design process is formulated as a convex optimal problem that can be solved by existing software. The control strategy is proposed as follows： when actuator failures appear, the fault detection and isolation unit sends out the information to the controller choosing strategy, and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen. Two illustrative examples are given to demonstrate the effectiveness of the proposed approach. By comparing with the existing methods, it can be seen that our method has a better performance.展开更多
The delay tolerant network(DTN) is an emerging concept, which is used to describe the network, where the communication link may disrupt frequently. To cope with this problem, the DTN uses the store-carry-forward(SCF) ...The delay tolerant network(DTN) is an emerging concept, which is used to describe the network, where the communication link may disrupt frequently. To cope with this problem, the DTN uses the store-carry-forward(SCF) transmission mode. With this policy, the messages in the DTN are transmitted based on the nodes' cooperation. However, the nodes may be selfish, so the source has to pay certain rewards for others to get their help. This paper studies the optimal incentive policy to maximize the source's final utility. First, it models the message spreading process as several ordinary differential equations(ODEs) based on the meanfield approximation. Then, it mathematically gets the optimal policy and explores the structure of the policy. Finally, it checks the accuracy of the ODEs model and shows the advantages of the optimal policy through simulation and theoretical results respectively.展开更多
Recently, network coding has been applied to the loss recovery of reliable broadcast transmission in wireless networks. Since it was proved that fi nding the optimal set of lost packets for XOR-ing is a complex NP-com...Recently, network coding has been applied to the loss recovery of reliable broadcast transmission in wireless networks. Since it was proved that fi nding the optimal set of lost packets for XOR-ing is a complex NP-complete problem, the available time-based retransmission scheme and its enhanced retransmission scheme have exponential computational complexity and thus are not scalable to large networks. In this paper, we present an efficient heuristic scheme based on hypergraph coloring and also its enhanced heuristic scheme to improve the transmission efficiency. Basically, our proposed schemes fi rst create a hypergraph according to the packet-loss matrix. Then our schemes solve the problem of generating XORed packets by coloring the edges of hypergraph. Extensive simulation results demonstrate that, the heuristic scheme based on hypergraph coloring and its enhanced scheme can achieve almost the same transmission efficiency as the available ones, but have much lower computational complexity, which is very important for the wireless devices without high computation capacity.展开更多
The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different W...The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.展开更多
High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-co...High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.展开更多
In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.A...In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.展开更多
Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces exce...Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces excessive intra-and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks.In this article,a dynamic spectrum control(DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity.Specifically,the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families,which represent the transmission decisions,by performing iterative and orthogonal sequence transformations.Based on the sequence families,multiple users can dynamically occupy different frequency slots for data transmission simultaneously.In addition,the collision probability of the data transmission is analyzed,which results in closed-form expressions of the reliable transmission probability and the secrecy probability.Then,the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities.Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance.Finally,the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.展开更多
In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption pro...In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption process, different individuals often make behavioral decisions in different ways, and it is of good practical importance to study the influence of individual heterogeneity on the behavior adoption process. In this paper, we propose a three-layer coupled model to analyze the process of co-evolution of official information diffusion, immunization behavior adoption and epidemic transmission in multiplex networks, focusing on individual heterogeneity in behavior adoption patterns. Specifically, we investigate the impact of the credibility of social media and the risk sensitivity of the population on behavior adoption in further study of the effect of heterogeneity of behavior adoption on epidemic transmission. Then we use the microscopic Markov chain approach to describe the dynamic process and capture the evolution of the epidemic threshold. Finally, we conduct extensive simulations to prove our findings. Our results suggest that enhancing the credibility of social media can raise the epidemic transmission threshold, making it effective at controlling epidemic transmission during the dynamic process. In addition, improving an individuals' risk sensitivity, and thus their taking effective protective measures, can also reduce the number of infected individuals and delay the epidemic outbreak. Our study explores the role of individual heterogeneity in behavior adoption in real networks, more clearly models the effect of the credibility of social media and risk sensitivity of the population on the epidemic transmission dynamic, and provides a useful reference for managers to formulate epidemic control and prevention policies.展开更多
Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life d...Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.展开更多
In order to improve the Energy Efficiency(EE)and spectrum utilization of Cognitive Wireless Powered Networks(CWPNs),a combined spatial-temporal Energy Harvesting(EH)and relay selection scheme is proposed.In the propos...In order to improve the Energy Efficiency(EE)and spectrum utilization of Cognitive Wireless Powered Networks(CWPNs),a combined spatial-temporal Energy Harvesting(EH)and relay selection scheme is proposed.In the proposed scheme,for protecting the Primary User(PU),a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU.Moreover,to increase the energy of the CWPNs,the EH zone in the two-layer guard zone allows the Secondary Users(SUs)to spatially harvest energy from the Radio Frequency(RF)signals of temporally active PUs.To improve the utilization of the PU spectrum,the guard zone outside the EH zone allows for the constrained power transmission of SUs.Moreover,the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs.In addition to the EE of the CWPNs,the outage probabilities of the SU and PU are derived.The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs.Furthermore,the relay selection transmission decreases the outage probabilities of the SUs.展开更多
The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission ...The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.展开更多
The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks.Based on the nodal channel quality,the game can work out a channel gain...The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks.Based on the nodal channel quality,the game can work out a channel gain threshold,which decides the candidates for taking part in the concurrent transmission.The utility formula is made for maximizing the overall throughput based on channel quality variation.For an achievable Bayesian Nash equilibrium（BNE） solution,this paper further prices the selfish players in utility functions for attempting to improve the channel gain one-sidedly.Accordingly,this game allows each node to distributedly decide whether to transmit concurrently with others depending on the Nash equilibrium（NE）.Besides,to make the proposed game practical,this paper next presents an efficient particle swarm optimization（PSO） model to fasten the otherwise very slow convergence procedure due to the large computational complexity.Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm.展开更多
The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclea...The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclear.Here,we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations.We explored the position(i.e.,root,middle,or tip)of early detected samples in the evolutionary tree of SARS-CoV-2.In total,6799 transmission chains and 1766 transmission networks were reconstructed,with chain lengths ranging from 1-9 nodes.The root node samples of the 1766 transmission networks were from 58 countries or regions and showed no common ancestor,indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected(i.e.,all samples were located at the tip position of the evolutionary tree).No root node sample was found in any sample(n=31,all from the Chinese mainland)collected in the first 15 days from 24 December 2019.Results using six-month data or RaTG13-referenced mutation data were similar.The reconstruction method was verified using a simulation approach.Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan,China.Thus,a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.展开更多
Opportunistic networks are random networks and do not communicate with each other among respective communication areas.This situation leads to great difficulty in message transfer.This paper proposes a reducing energy...Opportunistic networks are random networks and do not communicate with each other among respective communication areas.This situation leads to great difficulty in message transfer.This paper proposes a reducing energy consumption optimal selection of path transmission(OSPT) routing algorithm in opportunistic networks.This algorithm designs a dynamic random network topology,creates a dynamic link,and realizes an optimized selected path.This algorithm solves a problem that nodes are unable to deliver messages for a long time in opportunistic networks.According to the simulation experiment,OSPT improves deliver ratio,and reduces energy consumption,cache time and transmission delay compared with the Epidemic Algorithm and Spray and Wait Algorithm in opportunistic networks.展开更多
文摘Space-division multiplexing(SDM)utilizing uncoupled multi-core fibers(MCF)is considered a promising candidate for nextgeneration high-speed optical transmission systems due to its huge capacity and low inter-core crosstalk.In this paper,we demonstrate a realtime high-speed SDM transmission system over a field-deployed 7-core MCF cable using commercial 400 Gbit/s backbone optical transport network(OTN)transceivers and a network management system.The transceivers employ a high noise-tolerant quadrature phase shift keying(QPSK)modulation format with a 130 Gbaud rate,enabled by optoelectronic multi-chip module(OE-MCM)packaging.The network management system can effectively manage and monitor the performance of the 7-core SDM OTN system and promptly report failure events through alarms.Our field trial demonstrates the compatibility of uncoupled MCF with high-speed OTN transmission equipment and network management systems,supporting its future deployment in next-generation high-speed terrestrial cable transmission networks.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
文摘In this paper,the topological structure of the vehicle wireless network M2M(Machine to Machine)is used as the experimental research model,and four kinds of light coefficients are set as factors affecting the experimental results,namely,light intensity factor ∈ and α,to represent the light intensity coefficient and influence factor.The remaining energy consumption of mobile terminal equipment was measured respectively,the distance parameter from device to device,the maximum transmission energy consumption,and the correlation coefficient between environmental parameters and energy consumption parameters was analyzed.This paper discusses the impact of different topological structures on the environment,energy saving and emission reduction in the relatively flat terrain area,based on the planning scheme of parking area within the coverage range of base station signal,the transmission capability of vehicles as mobile device nodes within the coverage range of base station signal,and the signal coverage range of base station under different light intensity.As the distance between the base station and the vehicle mobile device node changes,the maximum transmission energy consumption of the mobile device node is obtained.Based on the above factors,the optimal performance optimization parking scheme and the optimal energy consumption optimization transmission scheme are obtained.
基金the Science and Technology Commission of Shanghai Municipality(No.19030501100)the Technical Service Platform for Vibration and Noise Testing and Control of New Energy Vehicles(No.18DZ2295900)。
文摘With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.
基金supported by Research Fund for Doctoral Program of Higher Education of China (No.20020183003)
文摘From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.
基金Projects(70373017 70572090) supported by the National Natural Science Foundation of China
文摘Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.
基金supported by National Outstanding Youth Foundation (No. 60525303)National Natural Science Foundation of China(No. 60704009)+1 种基金Key Project for Natural Science Research of Hebei Education Department (No. ZD200908)the Doctor Fund of YanShan University (No. B203)
文摘The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet dropout and transmission delay, a piecewise constant controller is adopted. With a guaranteed cost function, optimal controllers whose number is equal to the number of actuators are designed, and the design process is formulated as a convex optimal problem that can be solved by existing software. The control strategy is proposed as follows： when actuator failures appear, the fault detection and isolation unit sends out the information to the controller choosing strategy, and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen. Two illustrative examples are given to demonstrate the effectiveness of the proposed approach. By comparing with the existing methods, it can be seen that our method has a better performance.
基金supported by the National Nature Science Foundation of China(61871388)
文摘The delay tolerant network(DTN) is an emerging concept, which is used to describe the network, where the communication link may disrupt frequently. To cope with this problem, the DTN uses the store-carry-forward(SCF) transmission mode. With this policy, the messages in the DTN are transmitted based on the nodes' cooperation. However, the nodes may be selfish, so the source has to pay certain rewards for others to get their help. This paper studies the optimal incentive policy to maximize the source's final utility. First, it models the message spreading process as several ordinary differential equations(ODEs) based on the meanfield approximation. Then, it mathematically gets the optimal policy and explores the structure of the policy. Finally, it checks the accuracy of the ODEs model and shows the advantages of the optimal policy through simulation and theoretical results respectively.
基金supported by the National Natural Science Foundation of China (60502046, 60573034)863 Foundation of China (2007AA01Z215)
文摘Recently, network coding has been applied to the loss recovery of reliable broadcast transmission in wireless networks. Since it was proved that fi nding the optimal set of lost packets for XOR-ing is a complex NP-complete problem, the available time-based retransmission scheme and its enhanced retransmission scheme have exponential computational complexity and thus are not scalable to large networks. In this paper, we present an efficient heuristic scheme based on hypergraph coloring and also its enhanced heuristic scheme to improve the transmission efficiency. Basically, our proposed schemes fi rst create a hypergraph according to the packet-loss matrix. Then our schemes solve the problem of generating XORed packets by coloring the edges of hypergraph. Extensive simulation results demonstrate that, the heuristic scheme based on hypergraph coloring and its enhanced scheme can achieve almost the same transmission efficiency as the available ones, but have much lower computational complexity, which is very important for the wireless devices without high computation capacity.
文摘The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.
基金financial support from the National Natural Science Foundation of China(Grant No.61971201)。
文摘High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.
基金This work is partly supported by the National Key Research and Development Program of China(Grant No.2020YFB1805403)the National Natural Science Foundation of China(Grant No.62032002)the 111 Project(Grant No.B21049).
文摘In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.
基金supported by the National Natural Science Foundation of China(61825104 and 91638204)the China Scholarship Council(CSC)+1 种基金the Natural Sciences and Engineering Research Council(NSERC)of CanadaUniversity Innovation Platform Project(2019921815KYPT009JC011)。
文摘Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces excessive intra-and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks.In this article,a dynamic spectrum control(DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity.Specifically,the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families,which represent the transmission decisions,by performing iterative and orthogonal sequence transformations.Based on the sequence families,multiple users can dynamically occupy different frequency slots for data transmission simultaneously.In addition,the collision probability of the data transmission is analyzed,which results in closed-form expressions of the reliable transmission probability and the secrecy probability.Then,the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities.Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance.Finally,the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 72174121 and 71774111)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learningthe Natural Science Foundation of Shanghai (Grant No. 21ZR1444100)。
文摘In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption process, different individuals often make behavioral decisions in different ways, and it is of good practical importance to study the influence of individual heterogeneity on the behavior adoption process. In this paper, we propose a three-layer coupled model to analyze the process of co-evolution of official information diffusion, immunization behavior adoption and epidemic transmission in multiplex networks, focusing on individual heterogeneity in behavior adoption patterns. Specifically, we investigate the impact of the credibility of social media and the risk sensitivity of the population on behavior adoption in further study of the effect of heterogeneity of behavior adoption on epidemic transmission. Then we use the microscopic Markov chain approach to describe the dynamic process and capture the evolution of the epidemic threshold. Finally, we conduct extensive simulations to prove our findings. Our results suggest that enhancing the credibility of social media can raise the epidemic transmission threshold, making it effective at controlling epidemic transmission during the dynamic process. In addition, improving an individuals' risk sensitivity, and thus their taking effective protective measures, can also reduce the number of infected individuals and delay the epidemic outbreak. Our study explores the role of individual heterogeneity in behavior adoption in real networks, more clearly models the effect of the credibility of social media and risk sensitivity of the population on the epidemic transmission dynamic, and provides a useful reference for managers to formulate epidemic control and prevention policies.
基金supported,in part,by the National Nature Science Foundation of China under Grant Numbers 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401.
文摘Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.
文摘In order to improve the Energy Efficiency(EE)and spectrum utilization of Cognitive Wireless Powered Networks(CWPNs),a combined spatial-temporal Energy Harvesting(EH)and relay selection scheme is proposed.In the proposed scheme,for protecting the Primary User(PU),a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU.Moreover,to increase the energy of the CWPNs,the EH zone in the two-layer guard zone allows the Secondary Users(SUs)to spatially harvest energy from the Radio Frequency(RF)signals of temporally active PUs.To improve the utilization of the PU spectrum,the guard zone outside the EH zone allows for the constrained power transmission of SUs.Moreover,the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs.In addition to the EE of the CWPNs,the outage probabilities of the SU and PU are derived.The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs.Furthermore,the relay selection transmission decreases the outage probabilities of the SUs.
文摘The topology control is an effective approach which can improve the quality of wireless sensor network at all sides. Through studying the mechanism of sensor network data transmission, the nature of data transmission in wireless sensor network is concluded as a kind of responsibility transmission. By redefining the responsibility and availability of nodes, the strategy for cluster head selection is studied, the responsibility and availability is determined by the combination of the residual energy, location and current flow of nodes. Based on the above, new clustering network topology control algorithm based on responsibility transmission CNTCABRT and hierarchical multi-hop CNTCABRT is presented in this paper, whose algorithm structure is along the famous LEACH algorithm. Experimental result demonstrates its promising performance over the famous LEACH algorithm in the cluster head selection, the size of cluster, the deployment of nodes and the lifetime of nodes, and several innovative conclusions are proposed finally.
基金supported by the National Natural Science Foundation of China (6120113361172055+6 种基金60832005U083500461072067)the Postdoctoral Science Foundation of China (20100481323)the Program for New Century Excellent Talents (NCET-11-0691)the "111 Project"of China (B08038)the Foundation of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (11105)
文摘The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks.Based on the nodal channel quality,the game can work out a channel gain threshold,which decides the candidates for taking part in the concurrent transmission.The utility formula is made for maximizing the overall throughput based on channel quality variation.For an achievable Bayesian Nash equilibrium（BNE） solution,this paper further prices the selfish players in utility functions for attempting to improve the channel gain one-sidedly.Accordingly,this game allows each node to distributedly decide whether to transmit concurrently with others depending on the Nash equilibrium（NE）.Besides,to make the proposed game practical,this paper next presents an efficient particle swarm optimization（PSO） model to fasten the otherwise very slow convergence procedure due to the large computational complexity.Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm.
基金supported by the Ministry of Science and Technology of the People’s Republic of China(2021YFC0863400)Institute of Zoology,Chinese Academy of Sciences(E0517111,E122G611)。
文摘The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclear.Here,we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations.We explored the position(i.e.,root,middle,or tip)of early detected samples in the evolutionary tree of SARS-CoV-2.In total,6799 transmission chains and 1766 transmission networks were reconstructed,with chain lengths ranging from 1-9 nodes.The root node samples of the 1766 transmission networks were from 58 countries or regions and showed no common ancestor,indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected(i.e.,all samples were located at the tip position of the evolutionary tree).No root node sample was found in any sample(n=31,all from the Chinese mainland)collected in the first 15 days from 24 December 2019.Results using six-month data or RaTG13-referenced mutation data were similar.The reconstruction method was verified using a simulation approach.Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan,China.Thus,a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.
基金Supported by the National Natural Science Foundation of China(No.61379057,61073186,61309001,61379110,61103202)Doctoral Fund of Ministry of Education of China(No.20120162130008)the National Basic Research Program of China(973 Program)(No.2014CB046305)
文摘Opportunistic networks are random networks and do not communicate with each other among respective communication areas.This situation leads to great difficulty in message transfer.This paper proposes a reducing energy consumption optimal selection of path transmission(OSPT) routing algorithm in opportunistic networks.This algorithm designs a dynamic random network topology,creates a dynamic link,and realizes an optimized selected path.This algorithm solves a problem that nodes are unable to deliver messages for a long time in opportunistic networks.According to the simulation experiment,OSPT improves deliver ratio,and reduces energy consumption,cache time and transmission delay compared with the Epidemic Algorithm and Spray and Wait Algorithm in opportunistic networks.
