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RISS 인기검색어
- 검색키워드
- 저자 : ( Hongwei Ge ) (검색결과 6 건)
- 무료
- 기관 내 무료
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Convergent Stochastic Differential Evolution Algorithms
Liang Sun,Hongwei Ge,Limin Wang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.7
Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide sufficientconditions for DE algorithm to guarantee converging to the global optimality region. Finally, we design two DE algorithms that satisfy the preconditions of the two theorems, respectively. The two proposed algorithmsare tested on the CEC2013 benchmark functions, and compared with other existing algorithms.Numerical simulations illustrate the converge, effectiveness and usefulness of the proposed algorithms.
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A Coevolutionary Bacterial Foraging Model Using PSO in Job-Shop Scheduling Environments
Liang Sun,Hongwei Ge,Limin Wang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.9
The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designed to address the job shop scheduling problem and a mechanism of quorum sensing and communication are presented to improve the foraging performance. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. The proposed coevolutionary algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined using a set of benchmark instances with various sizes and levels of hardness and compared with other approaches reported in some existing literatures. The computational results validate the effectiveness of the proposed algorithm.
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Novel Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter for Extended Target Tracking
( Peng Li ),( Hongwei Ge ),( Jinlong Yang ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.11
Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.
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Target Birth Intensity Estimation Using Measurement-Driven PHD Filter
Huanqing Zhang,Hongwei Ge,Jin-Long Yang 한국전자통신연구원 2016 ETRI Journal Vol.38 No.5
The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.
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Working Conditions on the Afterglow Characteristics of Rare-earth Luminous Fibers
Xuefeng Guo,Keqin Zhang,Hongwei Zhang,Mingqiao Ge 한국섬유공학회 2018 Fibers and polymers Vol.19 No.3
To test and clarify the stability of afterglow performance of luminescent fibers can accelerate the step of the commercialized application on luminescent textiles. This work investigated the possible effects of a number of working conditions on the afterglow characteristics of luminous fibers. The fibers in our studies did not show any significant change in their afterglow brightness and duration after storage for 12 months under conditions of constant temperature and humidity, after 5 hours of light exposure, or soaking in water for 4 hours. The insignificant decay appears to follow the same mechanism. Thermal perturbation seemed to cause some changes to the initial brightness and decay time with the best performance being observed at about 80 oC. Moreover, contact with acid or base for 5 minutes only resulted in slight reduction of the afterglow brightness. Our studies thus indicate a high degree of stability of the afterglow performance of the luminous fibers used.
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An Improved ET-GM-PHD Filter for Multiple Closely-Spaced Extended Target Tracking
Jin-Long Yang,Peng Li,Le Yang,Hongwei Ge 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.1
This paper presents an enhanced version of the ET-GM-PHD algorithm, a recently developed multipleextended target tracking (METT) technique. The original ET-GM-PHD filter tends to underestimate the targetnumber, because the likelihood estimate in the state update process may poorly approximate the real one whentargets are close to each other. The proposed algorithm addresses this drawback via introducing a new penaltystrategy in estimating the measurement likelihood. Besides, Gaussian component labeling technique is adopted toobtain individual target tracks. Simulations show that for closely-spaced extended target tracking, the improvedmethod achieves track continuity and exhibits better estimation accuracy over the original ET-GM-PHD filter.
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