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markov chain approach to identifying Wiener systems

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Science China(Information Sciences) 2012年第5期55卷 1201-1217页

作者： ZHAO WenXiao 1,2 & CHEN HanFu 1,2 1 Key Laboratory of Systems and Control,Institute of Systems Science,AMSS,Chinese Academy of Sciences,Beijing 100190,China 2 National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences,Beijing 100190,China 1. Key Laboratory of Systems and Control Institute of Systems Science AMSS Chinese Academy of Sciences Beijing 100190 China2. National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing 100190 China

Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is *** recursive estimates for unknown coefficients of the linear subsystem and for the values of the nonlinear function at any fixed points are given by the stochastic approx-imation algorithms with expanding truncations (SAAWET).With the help of properties of the markov chain connected with the linear subsystem,all estimates derived in the paper are proved to be strongly *** comparison with the existing results on the topic,the method presented in the paper simplifies the convergence analysis and requires weaker conditions.A numerical example is given,and the simulation results are consistent with the theoretical analysis.

关键词： Wiener system recursive identification stochastic approximation markov chain strong consis-tency

markov chain for Reuse Strategies of Product Families

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Journal of Beijing Institute of Technology 2007年第3期16卷 262-267页

作者：罗佳姜澜 School of Mechanical and Vehicular Engineering Beijing Institute of Technology

A methodology is presented to plan reuse strategies of common modules in a product family by using the concepts of function degradation, reliability, function requirement, cost and life time. markov chain model is employed to predict function degradation and reliability. A utility model is used to evaluate the preference between used modules and new modules. An example of cascading-requirment product family illustrates the main ideas of our work. The markov models are used effectively to predict function degradation and reliability. Utility theory is helpful to evaluate the reuse options of common modules.

关键词： markov chain reuse end-of-life strategy product family

A Chan–Vese Model Based on the markov chain for Unsupervised Medical Image Segmentation

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Tsinghua Science and Technology 2021年第6期26卷 833-844页

作者： Quanwei Huang Yuezhi Zhou Linmi Tao Weikang Yu Yaoxue Zhang Li Huo Zuoxiang He Key Laboratory of Pervasive Computing(Ministry of Education)and the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Nuclear Medicine Peking Union Medical College HospitalBeijing 100730China School of Clinical Medicine Tsinghua Universityand also with Beijing Tsinghua Changgung HospitalBeijing 100084China

The accurate segmentation of medical images is crucial to medical care and research;however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan–Vese model based on the markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan–Vese model, we utilize weight maps generated by the markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel *** method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is *** performance of our method is assessed by using four Computerized Tomography(CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.

关键词： medical image unsupervised segmentation markov chain

Using markov chain Based Estimation of Distribution Algorithm for Model-Based Safety Analysis of Graph Transformation

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Journal of Computer Science & Technology 2021年第4期36卷 839-855页

作者： Einollah Pira Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz ***Iran

The ability to assess the reliability of safety-critical systems is one of the most crucial requirements in the design of modern safety-critical systems where even a minor failure can result in loss of life or irreparable damage to the *** checking is an automatic technique that verifies or refutes system properties by exploring all reachable states(state space)of a *** large and complex systems,it is probable that the state space explosion problem *** exploring the state space of systems modeled by graph transformations,the rule applied on the current state specifies the rule that can perform on the next *** other words,the allowed rule on the current state depends only on the applied rule on the previous state,not the ones on earlier *** fact motivates us to use a markov chain(MC)to capture this type of dependencies and applies the Estimation of Distribution Algorithm(EDA)to improve the quality of the *** is an evolutionary algorithm directing the search for the optimal solution by learning and sampling probabilistic models through the best individuals of a population at each *** show the effectiveness of the proposed approach,we implement it in GROOVE,an open source toolset for designing and model checking graph transformation *** results confirm that the proposed approach has a high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques in safety analysis of systems specified formally through graph transformations.

关键词： safety analysis model checking markov chain estimation of distribution algorithm graph transformation system

Utility water supply forecast via a GM (1,1) weighted markov chain

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2010年第9期11卷 677-682页

作者： Yi-mei TIAN Hai-liang SHEN Li ZHANG Xiang-rui LV College of Environmental Science & Engineering Tianjin University Tianjin 300072 China School of Engineering University of Guelph Guelph Ontario N1G 2W1 Canada

This paper describes the procedure of using the GM (1,1) weighted markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.

关键词： Dynamic adjustment interval (DAI) Forecast GM (1 1) markov chain Water supply

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and markov chain

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 757-768页

作者： Jingxin Jin Lin Ye Jiachen Li Yongning Zhao Peng Lu Weisheng Wang Xuebin Wang College of Information and Electrical Engineering China Agricultural UniversityBeijing100083China School of Engineering Cardiff UniversityCF243AAUK China Electric Power Research Institute Beijing100192China State Grid Qinghai Electric Power Company Xining810000China

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential *** this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and markov chain(ECMC)is *** ECMC method can effectively reduce redundant information in the ***,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided *** the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of ***,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation ***,based on the markov chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation *** results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

关键词： Aggregation method ensemble clustering markov chain time sequential simulations wind and photovoltaic power time series data

Consensus protocol for heterogeneous multi-agent systems: A markov chain approach

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Chinese Physics B 2013年第1期22卷 569-573页

作者：朱善迎陈彩莲关新平 Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and passive agents. The objective is to directly control the active agents such that the states of all the agents would achieve consensus. In order to obtain a computational approach, we subtly introduce an appropriate markov chain to cast the heterogeneous systems into a unified framework. Such a framework is helpful for tackling the constraints from passive agents. Furthermore, a sufficient and necessary condition is established to guarantee the consensus in heterogeneous multi-agent systems. Finally, simulation results are provided to verify the theoretical analysis and the effectiveness of the proposed protocol.

关键词： heterogeneous multi-agent system consensus protocol markov chain

Continuous time markov chain based website navigability measure

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The Journal of China Universities of Posts and Telecommunications 2011年第2期18卷 45-52页

作者： ZHANG Wei-feng LIU Xia ZHANG Ying-zhou ZHOU Guo-qiang State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China School of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China

Website navigability is acquiring a growing importance in website design and redesign,quality evaluation,and *** navigability measures mainly depend on site link structure,so that they only consider the impact of site link structure for navigability and ignore the impact of Web page content.A continuous markov chain model which depicts the user＇s surfing behavior can balance these two factors in the evaluation of website navigability,and it needs to estimate the page transition probabilities and user stay time according to user access *** this way,we can obtain more reliable results for website navigability measure than the existed *** show that our method is effective.

关键词： navigability measure markov chain

Distance Matrix and markov chain Based Sensor Localization in WSN

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Computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and markov chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a markov chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the markova chain to sustain higher energy ***,the proposed Distance Matrix and markov chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix markov chain