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16篇 您的检索式:作者名="XUE Anke"
    题名 作者 年代 出处 被引量
1The Global Landscape of SARS-CoV-2 Genomes, Variants, and Haplotypes in 2019nCoVR显示文摘On January 22,2020,China National Center for Bioinformation(CNCB)released the 2019 Novel Coronavirus Resource(2019nCoVR),an open-access information resource for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates,which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline.Of particular note,2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale.It provides all identified variants and their detailed statistics for each virus isolate,and congregates the quality score,functional annotation,and population frequency for each variant.Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available.Moreover,2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019(COVID-19),including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC.Furthermore,by linking with relevant databases in CNCB,2019nCoVR offers data submission services for raw sequence reads and assembled genomes,and data sharing with NCBI.Collectively,SARS-CoV-2 is updated daily to collect the latest information on genome sequences,variants,haplotypes,and literature for a timely reflection,making 2019nCoVR a valuable resource for the global research community.2019nCoVR is accessible at http://gffzzdbc7b6aaae734bddsnf9fw0qbx0kf6npf.ffgz.tsg.suse.edu.cn/ncov/.Shuhui Song Lina Ma Dong Zou Dongmei Tian Cuiping Li Junwei Zhu Meili Chen Anke Wang Yingke Ma Mengwei Li Xufei Teng Ying Cui Guangya Duan Mochen Zhang Tong Jin Chengmin Shi Zhenglin Du Yadong Zhang Chuandong Liu Rujiao Li Jingyao Zeng Lili Hao Shuai Jiang Hua Chen Dali Han Jingfa Xiao Zhang Zhang Wenming Zhao Yongbiao Xue Yiming Bao 2020Genomics, Proteomics & Bioinformatics2020,18,6:4
2Feedback structure based entropy approach for multiple-model estimation显示文摘The variable-structure multiple-model(VSMM)approach,one of the multiple-model(MM)methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation(MSA)is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy(ME)based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter(PF)and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.Shen-tu Han Xue Anke Guo Yunfei 2013Chinese Journal of Aeronautics2013,26,6:3
3Geometrical entropy approach for variable structure multiple-model estimation显示文摘The variable structure multiple-model(VSMM) estimation approach, one of the multiple-model(MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation(MSA) plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy(GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy(MGE) principle.Consequently, the minimum geometrical entropy multiple-model(MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm(PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm(PF-AMGEMM), are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.Shen-tu Han Xue Anke Peng Dongliang 2015Chinese Journal of Aeronautics2015,28,4:2
4Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity显示文摘Zhang R Xue Anke Wang Jian-zhong 2009Journal of Process Control2009,19,1:1
5H∞ filtering for singular systems with communication delays 显示文摘LU Renquan XU Yong XUE Anke 2010Signal Processing2010,16,4:1
6Partially decoupled approach of extended non-minimal state space predictive functional control for MIMO processes显示文摘Zhang Ridong Xue Anke Wang Shuqing 2012Journal of Process Control2012,22,5:1
7Fuzzy H∞ Filtering of Discrete-Time Fuzzy Systems via Basis-Dependent Lyapunov Function Approach显示文摘Zhou Shaosheng Lam J Xue Anke 2007Fuzzy Sets and Systems2007,158,2:1
8An improved model predictive control approach based on extended non-minimal state space formulation显示文摘Ridong Zhang Anke Xue Shuqing Wang 2011Journal of Process Control2011,21,8:1
9On robustly expo- nential stability of uneertain neutral systems with time-var- ying delays and nonlinear perturbations显示文摘Chen Yun Xue Anke Lu Renquan 2008Nonlinear A- nalysis2008,68,:1
10Penalty dynamic program- ming algorithm for dim targets detection in sensor systems 显示文摘Huang Dayu Xue Anke Guo Yunfei 2012Sensors2012,12,4:1
11A New Result on Stability Analysis for Stochastic Neutral Systems 显示文摘CHEN YUN ZHENG WEIXING XUE ANKE 2010Au- tomatiea2010,46,12:1
12A Recursive Algorithm for Bearings-Only Tracking with Signal Time Delay显示文摘Yunfei Guo Anke Xue Dongliang Peng 2008Signal Processing2008,88,:1
13On ro- bustly exponential stability of uncertain neutral sys- tems with time-varying delays and nonlinear perturba- tions显示文摘CHEN Yun XUE Anke LU Renquan 2008Nonlinear Analysis2008,68,8:1
14A recursivealgorithm for bearings-only tracking with signal time delay 显示文摘GUO Yunfei XUE Anke PENG Dongliang 2008Signal Processing2008,88,6:1
15Parameter-dependent Lyapunov function approach to stability analysis and design for uncertain systems with time-varying delay 显示文摘CAO Yongyan XUE Anke 2005European Journal of Control2005,11,1:1
16Distributed H_(∞)filtering of nonlinear systems with random topology by an event-triggered protocol显示文摘Applying an event-triggered protocol,this paper proposes a distributed H_(∞)filter design for nonlinear perturbed systems under fading measurements with random topology.Nonlinearities in this system obey the one-sided Lipschitz constraint,which embraces the conventional Lipschitz condition as a special case.The sensor network allows random variations of the interconnection strengths between adjacent nodes,and the connection coefficient is determined as the product of a constant and a stochastic variable with a known probabilistic feature.To reduce the unnecessary data transmission and efficiently use the limited bandwidth,the transmissions are orchestrated by an event-triggered regulating strategy.A stochastic bounded real lemma is established for the resulting error dynamics.Based on the presented matrix decomposition,which removes the direct coupling between the statistical information of interconnection strengths and the filter gain,the distributed H_(∞)filter gain can be explicitly expressed and easily solved.The usefulness of the theoretical method is demonstrated in a simulation study.Yun CHEN Mengze ZHU Renquan LU Anke XUE 2021Science China(Information Sciences)2021,64,10:0
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