Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H...Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.展开更多
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to thi...Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.展开更多
By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the e...By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.展开更多
The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. ...The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.展开更多
Bilateral telerobot can help human operator feel and control the remote pre unknown environment as if he/she is at the remote site, which is usually called “telepresence”. However, the transmission time delay betw...Bilateral telerobot can help human operator feel and control the remote pre unknown environment as if he/she is at the remote site, which is usually called “telepresence”. However, the transmission time delay between master manipulator and slave manipulator causes instability of the telerobot system and bad “telepresence” of human operator. In this paper, a new adaptive and passive control scheme based on active impedance matching is proposed to guarantee stability and transparency of the bilateral telerobot system. A genetic algorithm (GA) is used in this control scheme to directly identify parameters of the environment impedance.展开更多
On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC alg...On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.展开更多
In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have ei...In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have either high computational complexity or relatively poor performance. Delay scheduling is a constraint optimization problem with non-deterministic polynomial（ NP） hard feathers. In this paper,we proposed an immune algorithm-based suboptimal method to solve the problem. Suitable immune operators have been designed such as encoding,clone,mutation and selection. The simulation results show that the proposed algorithm yields near-optimal performance and operates with much lower computational complexity.展开更多
In recent years, since airspace restrictions and the volume of passenger traffic are increasing, the rate of flight delay is rising rapidly and the contradiction about it is outstanding. Flight delay event not only ho...In recent years, since airspace restrictions and the volume of passenger traffic are increasing, the rate of flight delay is rising rapidly and the contradiction about it is outstanding. Flight delay event not only holds up passengers’ time, but also makes the airlines suffer a lot. So a scientific and reasonable guidance is necessary to reduce the delay effect. This paper firstly establishes a method to assess the degree of airport delays and get all factors which caused the flight delays quantification, and ultimately we offer a proposal to deal with the flow factor, which is the principal reason for flight delays.展开更多
In this paper, we made a detail analysis for the ESAMPH algorithm, and proposed ESAMPH_D algorithm according to the insufficient of ESAMPH algorithm. The ESAMPH_D algorithm does not consider those paths that do not sa...In this paper, we made a detail analysis for the ESAMPH algorithm, and proposed ESAMPH_D algorithm according to the insufficient of ESAMPH algorithm. The ESAMPH_D algorithm does not consider those paths that do not satisfy the delay constraint, so we can ensure that all paths be taken into account will meet the limit of delay constraint, then we find the least costly path in order to build a minimum cost multicast tree. Simulation results show that the algorithm is better than ESAMPH algorithm in performance.展开更多
Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (20...Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (2005). However, the acceptance rate of a proposed value will always be less than in the standard Metropolis-Hastings. We can fix this problem by using the Metropolis-Hastings algorithm with delayed rejection (MHDR) proposed by Tierney and Mira (1999). In this paper, we combine the ideas of MHDA and MHDR to propose a new MH algorithm, named the Metropolis-Hastings algorithm with delayed acceptance and rejection (MHDAR). The new algorithm reduces the computational cost by division of the prior or likelihood functions and increase the acceptance probability by delay rejection of the second stage. We illustrate those accelerating features by a realistic example.展开更多
Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed conve...Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.展开更多
A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four diff...A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four different factors are selected to establish the flight classification model and a method is given to calculate the delay cost for each class. Finally, the proposed method is implemented in the sequencing problems of flights in a terminal area, and results are compared with that of the traditional classification method（TCM）. Results show that the new classification model is effective in reducing the expenses of flight delays, thus optimizing the sequences of arrival and departure flights, and improving the efficiency of air traffic control.展开更多
A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalize...A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector.The gradient algorithm is used to deal with the identification problem.The effectiveness of this method is illustrated through simulation.展开更多
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
The new method which uses the consensus algorithm to solve the coordinate control problems of multiple unmanned underwater vehicles （multi-UUVs） formation in the case of leader-following is adapted. As the communica...The new method which uses the consensus algorithm to solve the coordinate control problems of multiple unmanned underwater vehicles （multi-UUVs） formation in the case of leader-following is adapted. As the communication between the UUVs is difficult and it is easy to be interfered under the water, time delay is assumed to be time-varying during the members communicate with each other. Meanwhile, the state feedback linearization method is used to transfer the nonlinear and coupling model of UUV into double-integrator dynamic. With this simplified double-integrator math model, the UUV formation coordinate control is regarded as consensus problem with time-varying communication delays. In addition, the position and velocity topologies are adapted to reduce the data volume in each data packet which is sent between members in formation. With two independent topologies designed, two cases of communication delay which are same and different are considered and the sufficient conditions are proposed and analyzed. The stability of the multi-UUVs formation is proven by using Lyapunov-Razumilkhin theorem. Finally, the simulation results are presented to confirm and illustrate the theoretical results.展开更多
In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinfo...In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.展开更多
In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated wit...In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.展开更多
In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of po...In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of power-distribution communication networks, connecting users to control centers. Hence, they are vital for the reliable, safe, and economical operation of a power system. This paper proposes a planning method for AP allocation that takes into consideration economics, reliability, network delay, and （n-l） resilience. First, an optimization model for the AP location is established, which minimizes the cost of installing APs, while satisfying the reliability, network delay, and （n-1） resilience constraints. Then, an improved genetic algorithm is proposed to solve the optimization problem. The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily. Furthermore, it can be applied effectively with high flexibility and scalability.展开更多
基金funded by National Natural Science Foundation of China Key Program(12431014)Key Project of Hunan Education Department(22A0126)+1 种基金Natural Science Foundation of Hunan Province(2022JJ30555)Postgraduate Scientific Research Innovation Project of Xiangtan University(XDCX2024Y172)。
文摘Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.
文摘Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
基金Supported by the High Technology Research and Development Programme of China(2006AA12A106)~~
文摘Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.
基金supported by the National Natural Science Foundation of China(61673077)。
文摘By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.
基金supported by the Brain Korea 21 PLUS Project,National Research Foundation of Korea(NRF-2013R1A2A2A01068127NRF-2013R1A1A2A10009458)Jiangsu Province University Natural Science Research Project(13KJB510003)
文摘The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.
文摘Bilateral telerobot can help human operator feel and control the remote pre unknown environment as if he/she is at the remote site, which is usually called “telepresence”. However, the transmission time delay between master manipulator and slave manipulator causes instability of the telerobot system and bad “telepresence” of human operator. In this paper, a new adaptive and passive control scheme based on active impedance matching is proposed to guarantee stability and transparency of the bilateral telerobot system. A genetic algorithm (GA) is used in this control scheme to directly identify parameters of the environment impedance.
文摘On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.
基金Supported by the National Natural Science Foundation of China(U1504613,U1504602)the Research Foundation for the Doctoral Program of China(2015M582622)
文摘In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have either high computational complexity or relatively poor performance. Delay scheduling is a constraint optimization problem with non-deterministic polynomial（ NP） hard feathers. In this paper,we proposed an immune algorithm-based suboptimal method to solve the problem. Suitable immune operators have been designed such as encoding,clone,mutation and selection. The simulation results show that the proposed algorithm yields near-optimal performance and operates with much lower computational complexity.
文摘In recent years, since airspace restrictions and the volume of passenger traffic are increasing, the rate of flight delay is rising rapidly and the contradiction about it is outstanding. Flight delay event not only holds up passengers’ time, but also makes the airlines suffer a lot. So a scientific and reasonable guidance is necessary to reduce the delay effect. This paper firstly establishes a method to assess the degree of airport delays and get all factors which caused the flight delays quantification, and ultimately we offer a proposal to deal with the flow factor, which is the principal reason for flight delays.
文摘In this paper, we made a detail analysis for the ESAMPH algorithm, and proposed ESAMPH_D algorithm according to the insufficient of ESAMPH algorithm. The ESAMPH_D algorithm does not consider those paths that do not satisfy the delay constraint, so we can ensure that all paths be taken into account will meet the limit of delay constraint, then we find the least costly path in order to build a minimum cost multicast tree. Simulation results show that the algorithm is better than ESAMPH algorithm in performance.
文摘Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (2005). However, the acceptance rate of a proposed value will always be less than in the standard Metropolis-Hastings. We can fix this problem by using the Metropolis-Hastings algorithm with delayed rejection (MHDR) proposed by Tierney and Mira (1999). In this paper, we combine the ideas of MHDA and MHDR to propose a new MH algorithm, named the Metropolis-Hastings algorithm with delayed acceptance and rejection (MHDAR). The new algorithm reduces the computational cost by division of the prior or likelihood functions and increase the acceptance probability by delay rejection of the second stage. We illustrate those accelerating features by a realistic example.
文摘Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.
文摘A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four different factors are selected to establish the flight classification model and a method is given to calculate the delay cost for each class. Finally, the proposed method is implemented in the sequencing problems of flights in a terminal area, and results are compared with that of the traditional classification method（TCM）. Results show that the new classification model is effective in reducing the expenses of flight delays, thus optimizing the sequences of arrival and departure flights, and improving the efficiency of air traffic control.
基金supported by Ministry of the Higher Education and Scientific Research in Tunisia
文摘A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector.The gradient algorithm is used to deal with the identification problem.The effectiveness of this method is illustrated through simulation.
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
基金Projects(51309067,51679057,51609048)supported by the National Natural Science Foundation of ChinaProject(JC2016007)supported by the Outstanding Youth Science Foundation of Heilongjiang Province,ChinaProject(HEUCFX041401)supported by the Fundamental Research Funds for the Central Universities,China
文摘The new method which uses the consensus algorithm to solve the coordinate control problems of multiple unmanned underwater vehicles （multi-UUVs） formation in the case of leader-following is adapted. As the communication between the UUVs is difficult and it is easy to be interfered under the water, time delay is assumed to be time-varying during the members communicate with each other. Meanwhile, the state feedback linearization method is used to transfer the nonlinear and coupling model of UUV into double-integrator dynamic. With this simplified double-integrator math model, the UUV formation coordinate control is regarded as consensus problem with time-varying communication delays. In addition, the position and velocity topologies are adapted to reduce the data volume in each data packet which is sent between members in formation. With two independent topologies designed, two cases of communication delay which are same and different are considered and the sufficient conditions are proposed and analyzed. The stability of the multi-UUVs formation is proven by using Lyapunov-Razumilkhin theorem. Finally, the simulation results are presented to confirm and illustrate the theoretical results.
基金This work is supported by the National Science Foundation of China under grant No.61901403,61790551,and 61925106,Youth Innovation Fund of Xiamen No.3502Z20206039 and Tsinghua-Foshan Innovation Special Fund(TFISF)No.2020THFS0109.
文摘In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.
基金supported by Zhejiang Provincial Key Laboratory of Communication Networks and Applications and National Natural Science Foundation of China under Grant No.60872020
文摘In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.
基金supported by the National High Technology Research and Development Program of China(2012AA050801)
文摘In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of power-distribution communication networks, connecting users to control centers. Hence, they are vital for the reliable, safe, and economical operation of a power system. This paper proposes a planning method for AP allocation that takes into consideration economics, reliability, network delay, and （n-l） resilience. First, an optimization model for the AP location is established, which minimizes the cost of installing APs, while satisfying the reliability, network delay, and （n-1） resilience constraints. Then, an improved genetic algorithm is proposed to solve the optimization problem. The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily. Furthermore, it can be applied effectively with high flexibility and scalability.
