To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-...To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.展开更多
To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainabili...To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.展开更多
Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems.However,the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic pro...Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems.However,the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic processing unit).Aiming at this problem,a fast weighting method for PIC simulation on GPU-accelerated systems was proposed to avoid the atomic memory operations during the weighting process.The method was implemented by taking advantage of GPU's thread synchronization mechanism and dividing the problem space properly.Moreover,software managed shared memory on the GPU was employed to buffer the intermediate data.The experimental results show that the method achieves speedups up to 3.5 times compared to previous works,and runs 20.08 times faster on one NVIDIA Tesla M2090 GPU compared to a single core of Intel Xeon X5670 CPU.展开更多
Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems ofte...Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.展开更多
基金Project(60535010) supported by the National Nature Science Foundation of China
文摘To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.
基金Project(51005238)supported by the National Natural Science Foundation of China
文摘To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.
基金Projects(61170049,60903044)supported by National Natural Science Foundation of ChinaProject(2012AA010903)supported by National High Technology Research and Development Program of China
文摘Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems.However,the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic processing unit).Aiming at this problem,a fast weighting method for PIC simulation on GPU-accelerated systems was proposed to avoid the atomic memory operations during the weighting process.The method was implemented by taking advantage of GPU's thread synchronization mechanism and dividing the problem space properly.Moreover,software managed shared memory on the GPU was employed to buffer the intermediate data.The experimental results show that the method achieves speedups up to 3.5 times compared to previous works,and runs 20.08 times faster on one NVIDIA Tesla M2090 GPU compared to a single core of Intel Xeon X5670 CPU.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of ChinaProjects(012BAF10B11,2012BAF10B06)supported by the National Key Technologies R&D Program of China+1 种基金Project(F11-264-1-08)supported by the Shenyang Science and Technology Project,ChinaProject(2011BY100383)supported by the Cooperation Project of Foshan and Chinese Academy of Sciences
文摘Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
