As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expo...As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.展开更多
Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection metho...Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.展开更多
Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study...Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.展开更多
Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the tra...Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.展开更多
Double-integrator multi-agent systems(MASs)might not achieve dynamical consensus,even if only partial agents suffer from self-sensing function failures(SSFFs).SSFFs might be asynchronous in real engineering applicatio...Double-integrator multi-agent systems(MASs)might not achieve dynamical consensus,even if only partial agents suffer from self-sensing function failures(SSFFs).SSFFs might be asynchronous in real engineering application.The existing fault-tolerant dynamical consensus protocol suitable for synchronous SSFFs cannot be directly used to tackle fault-tolerant dynamical consensus of double-integrator MASs with partial agents subject to asynchronous SSFFs.Motivated by these facts,this paper explores a new fault-tolerant dynamical consensus protocol suitable for asynchronous SSFFs.First,multi-hop communication together with the idea of treating asynchronous SSFFs as multiple piecewise synchronous SSFFs is used for recovering the connectivity of network topology among all normal agents.Second,a fault-tolerant dynamical consensus protocol is designed for double-integrator MASs by utilizing the history information of an agent subject to SSFF for computing its own state information at the instants when its minimum-hop normal neighbor set changes.Then,it is theoretically proved that if the strategy of network topology connectivity recovery and the fault-tolerant dynamical consensus protocol with proper time-varying gains are used simultaneously,double-integrator MASs with all normal agents and all agents subject to SSFFs can reach dynamical consensus.Finally,comparison numerical simulations are given to illustrate the effectiveness of the theoretical results.展开更多
Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data...Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.展开更多
Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain f...Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.展开更多
Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production.Thus,this paper proposes a new method of conducting the fault diagnosis of linear g...Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production.Thus,this paper proposes a new method of conducting the fault diagnosis of linear guide rails.First,synchrosqueezing transform(SST)combined with Gaussian high-pass filter,termed as SSTG,is proposed to process vibration signals of linear guide rails and obtain time-frequency images,thus helping realize fault feature visual enhancement.Next,the coordinate attention(CA)mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework,thus realizing accurate fault classifica-tion.Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%.The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.展开更多
This paper investigates the configuration design associated with boundary-constrained swarm flying.An analytic swarm configuration is identified to ensure the passive safety between each pair of spacecraft in the radi...This paper investigates the configuration design associated with boundary-constrained swarm flying.An analytic swarm configuration is identified to ensure the passive safety between each pair of spacecraft in the radial-cross-track plane.For the first time,this work derives the explicit configurable spacecraft amount to clarify the configuration's accommodation capacity while considering the maximum inter-spacecraft separation constraint.For larger-scale design problem that involves hundreds of spacecraft,this paper proposes an optimization framework that integrates a Relative Orbit Element(ROE)affine transformation operation and successional convex optimization.The framework establishes a multi-subcluster swarm structure,allowing decoupling the maintenance issues of each subcluster.Compared with previous design methods,it ensures that the computational cost for constraints verification only scales linearly with the swarm size,while also preserving the configuration optimization capacities.Numerical simulations demonstrate that the proposed analytic configuration strictly meets the design constraints.It is also shown that the proposed framework reduces the handled constraint amount by two orders compared with direct optimization,while achieving a remarkable swarm safety enhancement based on the existing analytic configuration.展开更多
In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet...In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet synchronous motor(PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.展开更多
In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban...In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield environments.By combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on it.Due to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases significantly.Therefore,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network architecture.Experimental results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target detection.The research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.展开更多
Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existin...Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.展开更多
This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA）...This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramor-Rao lower bound （CCRLB） of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator （CMLE） for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem （GTRS） which can be found via a simple bisection search. First-order mean square error （MSE） analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis.展开更多
Synthesis of autoclaved aerated concrete （AAC） has been carried out with carbide slag addition, and the carbide slag could be used as a main material to produce the AAC with the compressive strength about 2 MPa and ...Synthesis of autoclaved aerated concrete （AAC） has been carried out with carbide slag addition, and the carbide slag could be used as a main material to produce the AAC with the compressive strength about 2 MPa and the density below 0.6 g.cm-3. In this study, quartz sand acted as frame structure phase in the matrix, and quartz addition also influenced the Si/Ca of starting material. Tobermorite and CSH gel were formed readily at 62%, which seemed to enhance the compressive strength of samples. Curing time seemed to affect the morphology of phase produced, and specimen with the plate-like tobermorite formed at 10 h appeared to have a better compressive strength development than the fiber-like one at 18 h. The higher curing temperature seemed to favor the tobermorite and CSH gel formation, which also exerted a significant effect on the strength development of the samples. On the micro-scale, the formed CSH gel was filled in the interface of the matrix, and the tobermorite appeared to grow in internal-surface of the pores and interstices. The tobermorite or/and CSH formation seemed to densify the matrix, and therefore enhanced the strength of the samples.展开更多
This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which...This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.展开更多
Model predictive current control(MPCC)and model predictive torque control(MPTC)are two derivatives of model predictive control.These two control methods have demonstrated their strengths in the fault-tolerant control ...Model predictive current control(MPCC)and model predictive torque control(MPTC)are two derivatives of model predictive control.These two control methods have demonstrated their strengths in the fault-tolerant control of multiphase motor drives.To explore the inherent link,the pros and cons of two strategies,the performance analysis and comparative investigation of MPCC and MPTC are conducted through a five-phase permanent magnet synchronous motor with open-phase fault.In MPCC,the currents of fundamental and harmonic subspaces are simultaneously employed and constrained for a combined regulation of the open-circuit fault drive.In MPTC,apart from the torque and the stator flux related to fundamental subspace,the x-y currents are also considered and predicted to achieve the control of harmonic subspace.The principles of two methods are demonstrated in detail and the link is explored in terms of the cost function.Besides,the performance by two methods is experimentally assessed in terms of steady-state,transition,and dynamic tests.Finally,the advantages and disadvantages of each method are concluded.展开更多
Chinese rice wine making is a typical simultaneous saccharification and fermentation （SSF） process. During the fermentation process, temperature is one of the key parameters which decide the quality of Chinese rice ...Chinese rice wine making is a typical simultaneous saccharification and fermentation （SSF） process. During the fermentation process, temperature is one of the key parameters which decide the quality of Chinese rice wine. To optimize the SSF process for Chinese rice wine brewing, the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model. The kinetic parameters as a function of temperature were evaluated using the software Origin8.0. Combing these functions with the mathematical model, an appropriate form of the model equations for the SSF considering the effects of temperature were developed. The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model. The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm. The optimum temperature profile began at a low temperature of 26℃ up to 30 h. The operating temperature increased rapidly to 31.9 ℃, and then decreased slowly to 18℃ at 65 h. Thereafter, the temperature was maintained at 18 ℃ until the end of fermentation. A maximum ethanol production of 89.3 g.L 1 was attained. Conceivably, our model would facilitate the improvement of Chinese rice wine production at the industrial scale.展开更多
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC...For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.展开更多
In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation s...In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great challenges.As a result,this paper studies an efficient and intelligent computation offloading mechanism with resource allocation.Specifically,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission power.Meanwhile,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading(TD3PG-ICO)algorithm is proposed to solve this optimization problem.By combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading policy.Additionally,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random probability.The available resources of the target node are dynamically allocated to improve the model decision-making effect.Finally,the simulation results show that this proposed algorithm has faster convergence speed and good robustness.It can always approach the greedy algorithm with respect to the lowest total energy consumption.Furthermore,compared with full local and Deep Q-learning Network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.展开更多
The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of...The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61701197in part by the National Key Research and Development Program of China under Grant No.2021YFA1000500(4)in part by the 111 project under Grant No.B23008.
文摘As Internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex tasks.However,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security vulnerabilities.Blockchain technology can maintain a shared ledger among servers.In the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and security.To protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing.To address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high reliability.Simulation results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in latency.Moreover,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.
基金Supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(22KJB520012)the Research Project on Higher Education Reform in Jiangsu Province(2023JSJG781)the College Student Innovation and Entrepreneurship Training Program Project(202313571008Z)。
文摘Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.
基金This work was supported by Shandong medical and health science and technology development plan project(No.202012070393).
文摘Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.
基金supported in part by The National Natural Science Foundation of China(62071255,62171232,61771257)The Major Projects of the Natural Science Foundation of the Jiangsu Higher Education Institutions(20KJA510009)+3 种基金The Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications),Ministry of Education(JZNY201914)The open research fund of National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology,Nanjing University of Posts and Telecommunications(KFJJ20170305)The Research Fund of Nanjing University of Posts and Telecommunications(NY218012)Henan province science and technology research projects High and new technology(No.182102210106).
文摘Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.
基金National Natural Science Foundation of China(No.61876073)Fundamental Research Funds for the Central Universities of China(No.JUSRP21920)。
文摘Double-integrator multi-agent systems(MASs)might not achieve dynamical consensus,even if only partial agents suffer from self-sensing function failures(SSFFs).SSFFs might be asynchronous in real engineering application.The existing fault-tolerant dynamical consensus protocol suitable for synchronous SSFFs cannot be directly used to tackle fault-tolerant dynamical consensus of double-integrator MASs with partial agents subject to asynchronous SSFFs.Motivated by these facts,this paper explores a new fault-tolerant dynamical consensus protocol suitable for asynchronous SSFFs.First,multi-hop communication together with the idea of treating asynchronous SSFFs as multiple piecewise synchronous SSFFs is used for recovering the connectivity of network topology among all normal agents.Second,a fault-tolerant dynamical consensus protocol is designed for double-integrator MASs by utilizing the history information of an agent subject to SSFF for computing its own state information at the instants when its minimum-hop normal neighbor set changes.Then,it is theoretically proved that if the strategy of network topology connectivity recovery and the fault-tolerant dynamical consensus protocol with proper time-varying gains are used simultaneously,double-integrator MASs with all normal agents and all agents subject to SSFFs can reach dynamical consensus.Finally,comparison numerical simulations are given to illustrate the effectiveness of the theoretical results.
基金the National Natural Science Foundation of China under Grant No.62072255.
文摘Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.
基金supported by the National Natural Science Foundation of China(62173175,61873033)the Shandong Provincial Natural Science Foundation(ZR2024MF032)。
文摘Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.
基金supported by the following organizations:National Natural Science Foundation of China(Grant Nos.52375522,52207036,and 62203010)the Anhui Provincial Nat-ural Science Foundation(Grant Nos.2308085Y03 and 2208085QE167)+2 种基金the Project of the Outstanding Young Talents in Colleges and Universities of Anhui Province(Grant No.gxyqZD2022006)the College Natural Science Research Key project of Anhui Education Department(Grant No.KJ2021A0018)the University Outstanding Youth Research Project of Anhui Province(Grant No.2022AH030016)。
文摘Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production.Thus,this paper proposes a new method of conducting the fault diagnosis of linear guide rails.First,synchrosqueezing transform(SST)combined with Gaussian high-pass filter,termed as SSTG,is proposed to process vibration signals of linear guide rails and obtain time-frequency images,thus helping realize fault feature visual enhancement.Next,the coordinate attention(CA)mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework,thus realizing accurate fault classifica-tion.Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%.The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.
基金co-supported by the National Natural Science Foundation of China(Nos.52272408,U21B2008)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023B1515120018)。
文摘This paper investigates the configuration design associated with boundary-constrained swarm flying.An analytic swarm configuration is identified to ensure the passive safety between each pair of spacecraft in the radial-cross-track plane.For the first time,this work derives the explicit configurable spacecraft amount to clarify the configuration's accommodation capacity while considering the maximum inter-spacecraft separation constraint.For larger-scale design problem that involves hundreds of spacecraft,this paper proposes an optimization framework that integrates a Relative Orbit Element(ROE)affine transformation operation and successional convex optimization.The framework establishes a multi-subcluster swarm structure,allowing decoupling the maintenance issues of each subcluster.Compared with previous design methods,it ensures that the computational cost for constraints verification only scales linearly with the swarm size,while also preserving the configuration optimization capacities.Numerical simulations demonstrate that the proposed analytic configuration strictly meets the design constraints.It is also shown that the proposed framework reduces the handled constraint amount by two orders compared with direct optimization,while achieving a remarkable swarm safety enhancement based on the existing analytic configuration.
基金supported in part by the National Natural Science Foundation of China under Grant 52307056in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210475。
文摘In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet synchronous motor(PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(61806024,62206257)the Jilin Province Science and Technology Development Plan Key Research and Development Project(20210204050YY)+1 种基金the Wuxi University Research Start-up Fund for Introduced Talents(2023r004,2023r006)Jiangsu Engineering Research Center of Hyperconvergence Application and Security of IoT Devices,Jiangsu Foreign Expert Workshop,Wuxi City Internet of Vehicles Key Laboratory.
文摘In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield environments.By combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on it.Due to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases significantly.Therefore,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network architecture.Experimental results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target detection.The research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.
基金supported by the National Natural Science Foundation of China,China (Grants No.62171232)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.
基金co-supported by the National Natural Science Foundation of China (Nos. 61304264 and 61305017)the Innovation Foundation of Industry, Education and Research of Jiangsu Province (No. BY2014023-25)
文摘This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramor-Rao lower bound （CCRLB） of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator （CMLE） for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem （GTRS） which can be found via a simple bisection search. First-order mean square error （MSE） analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis.
基金Funded by the National Natural Science Foundation of China(Nos.51272180,51072138)
文摘Synthesis of autoclaved aerated concrete （AAC） has been carried out with carbide slag addition, and the carbide slag could be used as a main material to produce the AAC with the compressive strength about 2 MPa and the density below 0.6 g.cm-3. In this study, quartz sand acted as frame structure phase in the matrix, and quartz addition also influenced the Si/Ca of starting material. Tobermorite and CSH gel were formed readily at 62%, which seemed to enhance the compressive strength of samples. Curing time seemed to affect the morphology of phase produced, and specimen with the plate-like tobermorite formed at 10 h appeared to have a better compressive strength development than the fiber-like one at 18 h. The higher curing temperature seemed to favor the tobermorite and CSH gel formation, which also exerted a significant effect on the strength development of the samples. On the micro-scale, the formed CSH gel was filled in the interface of the matrix, and the tobermorite appeared to grow in internal-surface of the pores and interstices. The tobermorite or/and CSH formation seemed to densify the matrix, and therefore enhanced the strength of the samples.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61203147,61374047,and 61403168)
文摘This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.
基金supported in part by the Fundamental Research Funds for Central Universities under Grant JUSRP121020the Natural Science Foundation of Jiangsu Province under Grant BK20210475。
文摘Model predictive current control(MPCC)and model predictive torque control(MPTC)are two derivatives of model predictive control.These two control methods have demonstrated their strengths in the fault-tolerant control of multiphase motor drives.To explore the inherent link,the pros and cons of two strategies,the performance analysis and comparative investigation of MPCC and MPTC are conducted through a five-phase permanent magnet synchronous motor with open-phase fault.In MPCC,the currents of fundamental and harmonic subspaces are simultaneously employed and constrained for a combined regulation of the open-circuit fault drive.In MPTC,apart from the torque and the stator flux related to fundamental subspace,the x-y currents are also considered and predicted to achieve the control of harmonic subspace.The principles of two methods are demonstrated in detail and the link is explored in terms of the cost function.Besides,the performance by two methods is experimentally assessed in terms of steady-state,transition,and dynamic tests.Finally,the advantages and disadvantages of each method are concluded.
基金Supported by the National Natural Science Foundation of China(21276111,21206053,61305017)the Programme of Introducing Talents of Discipline to Universities(B12018)+2 种基金Fundamental Research Funds for the Central Universities(JUSRP11558)the Natural Science Foundation of Jiangsu Province(no.BK20160162)the Fundamental Research Funds for the Central Universities(JUSRP51510)
文摘Chinese rice wine making is a typical simultaneous saccharification and fermentation （SSF） process. During the fermentation process, temperature is one of the key parameters which decide the quality of Chinese rice wine. To optimize the SSF process for Chinese rice wine brewing, the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model. The kinetic parameters as a function of temperature were evaluated using the software Origin8.0. Combing these functions with the mathematical model, an appropriate form of the model equations for the SSF considering the effects of temperature were developed. The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model. The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm. The optimum temperature profile began at a low temperature of 26℃ up to 30 h. The operating temperature increased rapidly to 31.9 ℃, and then decreased slowly to 18℃ at 65 h. Thereafter, the temperature was maintained at 18 ℃ until the end of fermentation. A maximum ethanol production of 89.3 g.L 1 was attained. Conceivably, our model would facilitate the improvement of Chinese rice wine production at the industrial scale.
基金the China Agriculture Research System(No.CARS-49)Jiangsu College of Humanities and Social Sciences Outside Campus Research Base & Chinese Development of Strategic Research Base for Internet of Things
文摘For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.
基金partially supported by the National Natural Science Foundation of China(No.61971235)the China Postdoctoral Science Foundation(No.2018M630590)+3 种基金the Jiangsu Planned Projects for Postdoctoral Research Funds(No.2021K501C)the 333 High-level Talents Training Project of Jiangsu Provincethe 1311 Talents Plan of NJUPTthe Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX20_0851).
文摘In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great challenges.As a result,this paper studies an efficient and intelligent computation offloading mechanism with resource allocation.Specifically,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission power.Meanwhile,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading(TD3PG-ICO)algorithm is proposed to solve this optimization problem.By combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading policy.Additionally,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random probability.The available resources of the target node are dynamically allocated to improve the model decision-making effect.Finally,the simulation results show that this proposed algorithm has faster convergence speed and good robustness.It can always approach the greedy algorithm with respect to the lowest total energy consumption.Furthermore,compared with full local and Deep Q-learning Network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.
基金supported in part by the Natural Science Foundation of China under Grants 61801243, 61671144, and 61971238by the China Postdoctoral Science Foundation under Grant 2019M651914+1 种基金by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB510026by the Foundation of Nanjing University of Posts and Telecommunications under Grant NY218124
文摘The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.
