针对空谱信息中普遍存在的异常干扰现象,提出了基于空谱联合聚类的自适应核协同表示高光谱异常目标探测算法.算法充分发挥了基于密度的聚类算子(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)对于异常点的筛选...针对空谱信息中普遍存在的异常干扰现象,提出了基于空谱联合聚类的自适应核协同表示高光谱异常目标探测算法.算法充分发挥了基于密度的聚类算子(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)对于异常点的筛选特性,在DBSCAN聚类去除异常波谱的基础上,采用分波段子集随机投影变换对数据降维处理,以减少谱噪声和谱冗余,并采用DBSCAN聚类消除了局部背景像元中的杂乱点对协同探测算法结果的干扰.研究了背景离散度对核参选择的影响,比较了不同的核估计方法,并提出基于平均差的自适应核协同算法.采用该方法对AVIRIS和ROSIS的三组数据进行仿真实验并与现有算法进行了对比,结果表明该算法表现出较好的探测性能.展开更多
In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is prop...In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.展开更多
As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algori...As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.展开更多
Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timesc...Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.展开更多
This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the ...This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.展开更多
文摘针对空谱信息中普遍存在的异常干扰现象,提出了基于空谱联合聚类的自适应核协同表示高光谱异常目标探测算法.算法充分发挥了基于密度的聚类算子(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)对于异常点的筛选特性,在DBSCAN聚类去除异常波谱的基础上,采用分波段子集随机投影变换对数据降维处理,以减少谱噪声和谱冗余,并采用DBSCAN聚类消除了局部背景像元中的杂乱点对协同探测算法结果的干扰.研究了背景离散度对核参选择的影响,比较了不同的核估计方法,并提出基于平均差的自适应核协同算法.采用该方法对AVIRIS和ROSIS的三组数据进行仿真实验并与现有算法进行了对比,结果表明该算法表现出较好的探测性能.
基金High Education Research Project Funding(No.2018C-11)Natural Science Fund of Gansu Province(Nos.18JR3RA107,1610RJYA034)Key Research and Development Program of Gansu Province(No.17YF1WA 158)。
文摘In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.
文摘As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
基金supported by Shenzhen Science and Technology Innovation Committee(JCYJ20170413173837121)the Hong Kong Research Grant Council(HKUST C6009-15G,14203915,16302214,16304215,16318816,and AoE/P-705/16)+2 种基金King Abdullah University of Science and Technology(KAUST) Office of Sponsored Research(OSR)(OSR-2016-CRG5-3007)Guangzhou Science Technology and Innovation Commission(201704030116)Innovation and Technology Commission(ITCPD/17-9and ITC-CNERC14SC01)
文摘Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.
文摘This paper presents a fuzzy C- means clustering image segmentation algorithm based on particle swarm optimization, the method utilizes the strong search ability of particle swarm clustering search center. Because the search clustering center has small amount of calculation according to density, so it can greatly improve the calculation speed of fuzzy C- means algorithm. The experimental results show that, this method can make the fuzzy clustering to obviously improve the speed, so it can achieve fast image segmentation.
