With the rapid development of green communications,energy consumption issue plays more and more important role in cooperative communication strategies and communication systems.Based on cooperative transmission model,...With the rapid development of green communications,energy consumption issue plays more and more important role in cooperative communication strategies and communication systems.Based on cooperative transmission model,a cooperative user selection scheme is proposed in consideration of both energy efficiency and interference factor.With the proposed scheme,the selected cooperative user consumes less energy and receives less interference.Furthermore,the main factor is analyzed to affect system performance,including signal-to-noise ratio(SNR)of source user and cooperative user,distance between source user and cooperative user or base station(BS),and fading factor in the transmission model.Through the proposed scheme,energy consumption and influence of interference are jointly taken into account during the cooperative user selection process.Besides,bit error rate(BER)in proposed scheme is also superior to existing schemes.Simulation results are presented to show the performance improvement of the proposed scheme.展开更多
To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.Firs...To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelli...With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelligent models.However,these data often contain sensitive information of users.Federated learning(FL),as a privacy preservation machine learning setting,allows users to obtain a well-trained model without sending the privacy-sensitive local data to the central server.Despite the promising prospect of FL,several significant research challenges need to be addressed before widespread deployment,including network resource allocation,model security,model convergence,etc.In this paper,we first provide a brief survey on some of these works that have been done on FL and discuss the motivations of the Communication Networks(CNs)and FL to mutually enable each other.We analyze the support of network technologies for FL,which requires frequent communication and emphasizes security,as well as the studies on the intelligence of many network scenarios and the improvement of network performance and security by the methods based on FL.At last,some challenges and broader perspectives are explored.展开更多
This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the ab...This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.展开更多
In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal w...In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.展开更多
Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for ...Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.展开更多
Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degr...Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degree of freedom(DOF), which is infeasible to the optimization of binary control multi-DOF system. A novel optimization method using for the locomotion and manipulation of an 18 DOFs tetrahedron-based mechanism called 5-TET is proposed. The optimization objective is to realize the required locomotion by executing the least number of struts.Binary control strategy is adopted, and forward kinematic and tipping dynamic analyses are performed, respectively.Based on a developed genetic algorithm(GA), the optimal number of alternative struts between two adjacent steps is obtained as 5. Finally, a potential manipulation function is proposed, and the energy consumption comparison between optimal 5-TET and the traditional wheeled robot is carried out. The presented locomotion optimization and manipulation planning enrich the research of tetrahedron-based mechanisms and provide the instruction to the successive locomotion and operation planning of multi-DOF mechanisms.展开更多
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number ...With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.展开更多
In this paper,the problem of time varying telecommunication delays in passive teleoperation systems is addressed.The design comprises delayed position,velocity and position-velocity signals with the local position and...In this paper,the problem of time varying telecommunication delays in passive teleoperation systems is addressed.The design comprises delayed position,velocity and position-velocity signals with the local position and velocity signals of the master and slave manipulators.Nonlinear adaptive control terms are employed locally to cope with uncertain parameters associated with the gravity loading vector of the master and slave manipulators.Lyapunov-Krasovskii function is employed for three methods to establish asymptotic tracking property of the closed loop teleoperation systems.The stability analysis is derived for both symmetrical and unsymmetrical time varying delays in the forward and backward communication channel that connects the local and remote sites.Finally,evaluation results are presented to illustrate the efectiveness of the proposed design for real-time applications.展开更多
Based on the proposed partly equidifferent mapping and its specific Differential Amplitude and Pulse Position Modulation(DAPPM) demodulation, a modified FSO scheme for turbulent channel is designed and analyzed. The n...Based on the proposed partly equidifferent mapping and its specific Differential Amplitude and Pulse Position Modulation(DAPPM) demodulation, a modified FSO scheme for turbulent channel is designed and analyzed. The novel Low Density Parity Check(LDPC) coded 4×4 and 4×8 DAPPM Free-Space Optical communication(FSO) system is constructed. The Monte Carlo simulation results show approximately 2d B transmit power reduction against classical LDPC-DAPPM at the identical Bit-Error-Rate in strong turbulent channel. The proposed partly equidifferent mapping is compatible with other modulations, so it enables widespread adoption in other coded FSO systems.展开更多
Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power contro...Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.展开更多
Path prediction of flexible needles based on the Fokker-Planck equation and disjunctive Kriging model is proposed to improve accuracy and consider the nonlinearity and anisotropy of soft tissues.The stochastic differe...Path prediction of flexible needles based on the Fokker-Planck equation and disjunctive Kriging model is proposed to improve accuracy and consider the nonlinearity and anisotropy of soft tissues.The stochastic differential equation is developed into the Fokker-Planck equation with Gaussian noise,and the position and orientation probability density function of flexible needles are then optimized by the stochastic differential equation.The probability density function obtains the mean and covariance of flexible needle movement and helps plan puncture paths by combining with the probabilistic path algorithm.The weight coefficients of the ordinary Kriging are extended to nonlinear functions to optimize the planned puncture path,and the Hermite expansion is used to calculate nonlinear parameter values of the disjunctive Kriging optimization model.Finally,simulation experiments are performed.Detailed comparison results under different path planning maps show that the kinematics model can plan optimal puncture paths under clinical requirements with an error far less than 2 mm.It can effectively optimize the path prediction model and help improve the target rate of soft tissue puncture with flexible needles through data analysis and processing of the mean value and covariance parameters derived by the probability density and disjunctive Kriging algorithms.展开更多
Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward o...Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward operation, have not been able to approach the limit of the max-flow min-cut throughput wherein sources transmitting information over bottleneck links have to compete for access to these links. With Network Coding, multiple sources are now able to transmit packets over bottleneck links simultaneously, achieving the max-flow min-cut through-put and increasing network capacity. While the majority of the contemporary literature has focused on the performance of Network Coding from a capacity perspective, the aim of this research has taken a new direction focusing on two Quality of Service metrics, e.g., Packet Delivery Ratio (PDR) and Latency, in conjunction with Network Coding protocols in Mobile Ad Hoc Networks (MANETs). Simulations are performed on static and mobile environments to determine a Quality of Service baseline comparison between Network Coding protocols and traditional ad hoc routing protocols. The results show that the Random Linear Network Coding protocol has the lowest Latency and Dynamic Source Routing protocol has the highest PDR in the static scenarios, and show that the Random Linear Network Coding protocol has the best cumulative performance for both PDR and Latency in the mobile scenarios.展开更多
This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the househ...This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy trading among the smart homes in a community microgrid. However, P2P trading potentially results in an unfair cost distribution among the participating households. To the best of our knowledge, the ECO-Trade algorithm is the first near-optimal cost optimization algorithm which considers the unfair cost distribution problem for a Demand Side Management (DSM) system coordinated with P2P energy trading. It also solves the time complexity problem of our previously proposed optimal model. Our results show that the solution time of the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Furthermore, we analyze the solutions and identify that the algorithm sometimes gets trapped at a local minimum because it alternately sets the microgrid price and quantity as constants. Finally, we describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization.展开更多
This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adapt...This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.展开更多
This research aims to estimate the long-term financial benefits of using smart grids to mitigate and adapt the power sector to climate change. In order to do that, twelve scenarios were analyzed applying an energy acc...This research aims to estimate the long-term financial benefits of using smart grids to mitigate and adapt the power sector to climate change. In order to do that, twelve scenarios were analyzed applying an energy accounting model （LEAP （Long-range Energy Alternatives Planning System）） that was developed using Brazilian historical data from 1970 to 2015. To conduct the analysis, the Sathaye and Ravindranath＇s three steps methodology was used. The main final results include a long-term cost-benefit analysis that is developed for each considered scenario. The initial phase includes the analysis of the projections for the power sector up to 2030. The following phase consists on the estimation of costs for operation, maintenance, losses and new electrical projects investments. And finally, all scenarios＇ results were compared and the benefits of implementing smart grids in the sector were estimated. The attained results show that smart grid implementation would contribute to reduce electricity tariffs, the generation costs as well as the costs associated with theft and fraud.展开更多
Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) tec...Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) technique between heterogenous networks for seamless connectivity with constrained resources. The conventional VHD approach is mainly based on received signal strength (RSS). The approach is inefficient for vertical handover, since it always selects the target network with the strongest signal without taking into consideration of factors such as quality of service (QoS), cost, delay, etc. In this paper, we present a hybrid approach by integrating the multi-cri- teria based VHD (MCVHD) technique and an algorithm based on fuzzy logic for efficient VHD among Wi-Fi, Radio and Satellite networks. The MCVHD provides a lightweight solution that aims to achieving seamless connectivity for mobile IoT Edge Gateway over a set of heterogeneous networks. The proposed solution is evaluated in real time using a testbed containing real IoT devices. Further, the testbed is integrated with lightweight and efficient software techniques, e.g., microservices, containers, broker, and Edge/Cloud techniques. The experimental results show that the proposed approach is suitable for an IoT environment and it outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service.展开更多
This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square se...This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square sections)were designed and manufactured to assess the impact of casting conditions on filling length,as magnesium alloys cause severe melting and melt-mold exothermic reactions,making investment casting challenging.Combinations of traditional Mg-mold reaction mitigation techniques,such as applying a protective mold coating(Yttria)and vacuum,were examined to determine their role in the filling process.The results suggest that when induction is employed to melt reactive alloys,these methods are not always beneficial,as initially thought.Particularly at higher melt temperatures,the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity:vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction.This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings,providing a viable route for the precision manufacturing of stent biomedical devices.展开更多
基金Supported by the National Natural Science Foundation of China(No.61372089,61571021)Beijing Natural Science Foundation(No.4132019)
文摘With the rapid development of green communications,energy consumption issue plays more and more important role in cooperative communication strategies and communication systems.Based on cooperative transmission model,a cooperative user selection scheme is proposed in consideration of both energy efficiency and interference factor.With the proposed scheme,the selected cooperative user consumes less energy and receives less interference.Furthermore,the main factor is analyzed to affect system performance,including signal-to-noise ratio(SNR)of source user and cooperative user,distance between source user and cooperative user or base station(BS),and fading factor in the transmission model.Through the proposed scheme,energy consumption and influence of interference are jointly taken into account during the cooperative user selection process.Besides,bit error rate(BER)in proposed scheme is also superior to existing schemes.Simulation results are presented to show the performance improvement of the proposed scheme.
基金National Natural Science Foundation of China(No.62073296)Natural Science Foundation of Zhejiang Province,China(No.LZ23F030010)Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province,China Jiliang University(No.ZNZZSZ-CJLU2022-03)Rights and permissions。
文摘To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
基金supported by National Key Research and Development Program of China(No.2023YFB2704200)Beijing Natural Science Foundation(No.4254064).
文摘With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelligent models.However,these data often contain sensitive information of users.Federated learning(FL),as a privacy preservation machine learning setting,allows users to obtain a well-trained model without sending the privacy-sensitive local data to the central server.Despite the promising prospect of FL,several significant research challenges need to be addressed before widespread deployment,including network resource allocation,model security,model convergence,etc.In this paper,we first provide a brief survey on some of these works that have been done on FL and discuss the motivations of the Communication Networks(CNs)and FL to mutually enable each other.We analyze the support of network technologies for FL,which requires frequent communication and emphasizes security,as well as the studies on the intelligence of many network scenarios and the improvement of network performance and security by the methods based on FL.At last,some challenges and broader perspectives are explored.
基金supported in part by the National Natural Science Foundation of China(61773072,61773051,61761166011,61773073)in part by the Innovative Talents Project of Liaoning Province of China(LR2016040)in part by the Natural Science Foundation of Liaoning Province of China(20180550691,20180550590)
文摘This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.
基金supported in part by the National Natural Science Foundation of China (61773051,61773072,61761166011)the Fundamental Research Fund for the Central Universities (2016RC021,2017JBZ003)
文摘In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.
基金fully supported under the National Natural Science Funds(Project Number:61501042 and 61302089)National High Technology Research and Development Program(863)of China(Project Number:2015AA016101 and 2015AA015702)BUPT Special Program for Youth Scientific Research Innovation(Grant No.2015RC10)
文摘Video streaming,especially hypertext transfer protocol based(HTTP)adaptive streaming(HAS)of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.
基金Supported by National Science-Technology Support Plan Projects of China (Grant No.2015BAK04B00)2015 Sino-German Postdoc Scholarship Program (Grant No.57165010)
文摘Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degree of freedom(DOF), which is infeasible to the optimization of binary control multi-DOF system. A novel optimization method using for the locomotion and manipulation of an 18 DOFs tetrahedron-based mechanism called 5-TET is proposed. The optimization objective is to realize the required locomotion by executing the least number of struts.Binary control strategy is adopted, and forward kinematic and tipping dynamic analyses are performed, respectively.Based on a developed genetic algorithm(GA), the optimal number of alternative struts between two adjacent steps is obtained as 5. Finally, a potential manipulation function is proposed, and the energy consumption comparison between optimal 5-TET and the traditional wheeled robot is carried out. The presented locomotion optimization and manipulation planning enrich the research of tetrahedron-based mechanisms and provide the instruction to the successive locomotion and operation planning of multi-DOF mechanisms.
基金The work of Vinay Chamola and F.Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles,and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program for Building Trust in Connected and Autonomous Vehicles(TrustCAV).
文摘With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
基金supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Research Fellowship,Canada Research Chairs Program and University of Ottawa Research Chair Program
文摘In this paper,the problem of time varying telecommunication delays in passive teleoperation systems is addressed.The design comprises delayed position,velocity and position-velocity signals with the local position and velocity signals of the master and slave manipulators.Nonlinear adaptive control terms are employed locally to cope with uncertain parameters associated with the gravity loading vector of the master and slave manipulators.Lyapunov-Krasovskii function is employed for three methods to establish asymptotic tracking property of the closed loop teleoperation systems.The stability analysis is derived for both symmetrical and unsymmetrical time varying delays in the forward and backward communication channel that connects the local and remote sites.Finally,evaluation results are presented to illustrate the efectiveness of the proposed design for real-time applications.
基金supported by the National High-tech R&D Program (863 Program) 2013AA041003the Natural Science Foundation of China under Grants 51165033the Science and Technology Department of Jiangxi Province of China under grant 20151BBE50046,20142BBE50035 and 20151BAB207052
文摘Based on the proposed partly equidifferent mapping and its specific Differential Amplitude and Pulse Position Modulation(DAPPM) demodulation, a modified FSO scheme for turbulent channel is designed and analyzed. The novel Low Density Parity Check(LDPC) coded 4×4 and 4×8 DAPPM Free-Space Optical communication(FSO) system is constructed. The Monte Carlo simulation results show approximately 2d B transmit power reduction against classical LDPC-DAPPM at the identical Bit-Error-Rate in strong turbulent channel. The proposed partly equidifferent mapping is compatible with other modulations, so it enables widespread adoption in other coded FSO systems.
基金This work was supported by the National Natural Science Foundation of China(61871058)Key Special Project in Intergovernmental International Scientific and Technological Innovation Cooperation of National Key Research and Development Program(2017YFE0118600).
文摘Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.
基金The National Natural Science Foundation of China(No.61903175,62163024,62163026)the Academic and Technical Leaders Foundation of Major Disciplines of Jiangxi Province under Grant(No.20204BCJ23006).
文摘Path prediction of flexible needles based on the Fokker-Planck equation and disjunctive Kriging model is proposed to improve accuracy and consider the nonlinearity and anisotropy of soft tissues.The stochastic differential equation is developed into the Fokker-Planck equation with Gaussian noise,and the position and orientation probability density function of flexible needles are then optimized by the stochastic differential equation.The probability density function obtains the mean and covariance of flexible needle movement and helps plan puncture paths by combining with the probabilistic path algorithm.The weight coefficients of the ordinary Kriging are extended to nonlinear functions to optimize the planned puncture path,and the Hermite expansion is used to calculate nonlinear parameter values of the disjunctive Kriging optimization model.Finally,simulation experiments are performed.Detailed comparison results under different path planning maps show that the kinematics model can plan optimal puncture paths under clinical requirements with an error far less than 2 mm.It can effectively optimize the path prediction model and help improve the target rate of soft tissue puncture with flexible needles through data analysis and processing of the mean value and covariance parameters derived by the probability density and disjunctive Kriging algorithms.
文摘Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward operation, have not been able to approach the limit of the max-flow min-cut throughput wherein sources transmitting information over bottleneck links have to compete for access to these links. With Network Coding, multiple sources are now able to transmit packets over bottleneck links simultaneously, achieving the max-flow min-cut through-put and increasing network capacity. While the majority of the contemporary literature has focused on the performance of Network Coding from a capacity perspective, the aim of this research has taken a new direction focusing on two Quality of Service metrics, e.g., Packet Delivery Ratio (PDR) and Latency, in conjunction with Network Coding protocols in Mobile Ad Hoc Networks (MANETs). Simulations are performed on static and mobile environments to determine a Quality of Service baseline comparison between Network Coding protocols and traditional ad hoc routing protocols. The results show that the Random Linear Network Coding protocol has the lowest Latency and Dynamic Source Routing protocol has the highest PDR in the static scenarios, and show that the Random Linear Network Coding protocol has the best cumulative performance for both PDR and Latency in the mobile scenarios.
文摘This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy trading among the smart homes in a community microgrid. However, P2P trading potentially results in an unfair cost distribution among the participating households. To the best of our knowledge, the ECO-Trade algorithm is the first near-optimal cost optimization algorithm which considers the unfair cost distribution problem for a Demand Side Management (DSM) system coordinated with P2P energy trading. It also solves the time complexity problem of our previously proposed optimal model. Our results show that the solution time of the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Furthermore, we analyze the solutions and identify that the algorithm sometimes gets trapped at a local minimum because it alternately sets the microgrid price and quantity as constants. Finally, we describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization.
文摘This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.
文摘This research aims to estimate the long-term financial benefits of using smart grids to mitigate and adapt the power sector to climate change. In order to do that, twelve scenarios were analyzed applying an energy accounting model （LEAP （Long-range Energy Alternatives Planning System）） that was developed using Brazilian historical data from 1970 to 2015. To conduct the analysis, the Sathaye and Ravindranath＇s three steps methodology was used. The main final results include a long-term cost-benefit analysis that is developed for each considered scenario. The initial phase includes the analysis of the projections for the power sector up to 2030. The following phase consists on the estimation of costs for operation, maintenance, losses and new electrical projects investments. And finally, all scenarios＇ results were compared and the benefits of implementing smart grids in the sector were estimated. The attained results show that smart grid implementation would contribute to reduce electricity tariffs, the generation costs as well as the costs associated with theft and fraud.
文摘Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) technique between heterogenous networks for seamless connectivity with constrained resources. The conventional VHD approach is mainly based on received signal strength (RSS). The approach is inefficient for vertical handover, since it always selects the target network with the strongest signal without taking into consideration of factors such as quality of service (QoS), cost, delay, etc. In this paper, we present a hybrid approach by integrating the multi-cri- teria based VHD (MCVHD) technique and an algorithm based on fuzzy logic for efficient VHD among Wi-Fi, Radio and Satellite networks. The MCVHD provides a lightweight solution that aims to achieving seamless connectivity for mobile IoT Edge Gateway over a set of heterogeneous networks. The proposed solution is evaluated in real time using a testbed containing real IoT devices. Further, the testbed is integrated with lightweight and efficient software techniques, e.g., microservices, containers, broker, and Edge/Cloud techniques. The experimental results show that the proposed approach is suitable for an IoT environment and it outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service.
基金financed by National Funds through the Portuguese funding agency,FCT–Funda??o para a Ciência e a Tecnologia,within the strategic projects UIDB/04436/2020,UIDB/00481/2020 and LA/P/0063/2020(DOI 10.54499/LA/P/0063/2020)。
文摘This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square sections)were designed and manufactured to assess the impact of casting conditions on filling length,as magnesium alloys cause severe melting and melt-mold exothermic reactions,making investment casting challenging.Combinations of traditional Mg-mold reaction mitigation techniques,such as applying a protective mold coating(Yttria)and vacuum,were examined to determine their role in the filling process.The results suggest that when induction is employed to melt reactive alloys,these methods are not always beneficial,as initially thought.Particularly at higher melt temperatures,the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity:vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction.This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings,providing a viable route for the precision manufacturing of stent biomedical devices.
