Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalabi...Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.展开更多
The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Kepleri...The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Keplerian model containing J2 perturbation and the nonlinear measurements. It is proven that the minimum number of tracking satellites to assure the observability of the distributed satellites system is three. Additionally, the analysis shows that the J2 perturbation and the nonlinearity make little contribution to improve the observability for the navigation. Then, a quasi-consistent extended Kalman filter based navigation algorithm is proposed, which is quasi-consistent and can provide an on- line evaluation of the navigation precision. The simulation illustrates the feasibility and effectiveness of the proposed navigation algorithm for the distributed satellites system.展开更多
A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamic...A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.展开更多
Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typical...Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typically use a small number of seed nodes that know their locations and protocols whereby other nodes estimate their locations from the messages they receive.For the inherent shortcomings of general particle filter(the sequential Monte Carlo method) this paper introduces particle swarm optimization and weighted centroid algorithm to optimize it.Based on improvement a distributed localization algorithm named WC-IPF(weighted centroid algorithm improved particle filter) has been proposed for localization.In this localization scheme the initial estimate position can be acquired by weighted centroid algorithm.Then the accurate position can be gotten via improved particle filter recursively.The extend simulation results show that the proposed algorithm is efficient for most condition.展开更多
We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous ...We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality（LMI） equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.60473082(国家自然科学基金)the National Basic Research Program of China under Grant No.2003CB314801(国家重点基础研究发展计划(973))
文摘Collaborative Filtering (CF) technique has proved to be one of the most successful techniques in recommendation systems in recent years. However, traditional centralized CF system has suffered from its limited scalability as calculation complexity increases rapidly both in time and space when the record in the user database increases. Peer-to-peer (P2P) network has attracted much attention because of its advantage of scalability as an alternative architecture for CF systems. In this paper, authors propose a decentralized CF algorithm, called PipeCF, based on distributed hash table (DHT) method which is the most popular P2P routing algorithm because of its efficiency, scalability, and robustness. Authors also propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to improve the scalability and prediction accuracy of DHT-based CF algorithm. The experimental data show that our DHT-based CF system has better prediction accuracy, efficiency and scalability than traditional CF systems.
基金supported by the National Basic Research Program of China under Grant No.2014CB845303the National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences
文摘The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Keplerian model containing J2 perturbation and the nonlinear measurements. It is proven that the minimum number of tracking satellites to assure the observability of the distributed satellites system is three. Additionally, the analysis shows that the J2 perturbation and the nonlinearity make little contribution to improve the observability for the navigation. Then, a quasi-consistent extended Kalman filter based navigation algorithm is proposed, which is quasi-consistent and can provide an on- line evaluation of the navigation precision. The simulation illustrates the feasibility and effectiveness of the proposed navigation algorithm for the distributed satellites system.
基金supported by the National Natural Science Foundation of China under Grant Nos.61120106011,61573221,61633014National Key Technology Support Program of China under Grant No.2014BAF07B03
文摘A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.
文摘Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typically use a small number of seed nodes that know their locations and protocols whereby other nodes estimate their locations from the messages they receive.For the inherent shortcomings of general particle filter(the sequential Monte Carlo method) this paper introduces particle swarm optimization and weighted centroid algorithm to optimize it.Based on improvement a distributed localization algorithm named WC-IPF(weighted centroid algorithm improved particle filter) has been proposed for localization.In this localization scheme the initial estimate position can be acquired by weighted centroid algorithm.Then the accurate position can be gotten via improved particle filter recursively.The extend simulation results show that the proposed algorithm is efficient for most condition.
基金supported by the National Natural Science Foundation of China(No.61503335)the Key Laboratory of System Control and Information Processing,China(No.Scip201504)
文摘We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality（LMI） equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.
