检索结果-内蒙古大学图书馆

Reliability evaluation method of PMFSM system based on hidden Markov

INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2022年第3期70卷 229-251页

作者： Zhou, Yongqin Huang, Jianxin Zhou, Lei Tian, Hongbo Harbin Univ Sci & Technol Engn Res Ctr Minist Educ Automot Elect Drive Control & Syst In Harbin Peoples R China State Grid Hangzhou Linan Power Supply Co Hangzhou Peoples R China Jiangsu Fangyang Energy Sci & Technol Ltd Lianyungang Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin Peoples R China

To solve the complex calculation in the reliability evaluation of the motor system with the Markov model, this study developed a reliability evaluation method based on the hidden Markov model (HMM). Next, the developed method was employed in a type of permanent magnet hybrid segmented flux-switching machine (PMFSM). By analyzing and simplifying the components of the PMFSM system, the reliability measurement standard and the failure modes of the respective component were examined, and the relationships between the failure modes of each component and the system parameters were built. The optimal parameter observation sequence was determined by calculating the Minkowski distance, and the HMM was iteratively calculated by using the baum-welch algorithm. Moreover, the state transition probability matrix of the system was built, and the system reliability was evaluated. As revealed from the calculation results, the reliability of the PMFSM system is significantly higher than that of the conventional structure at the same time, and the mean time to failure (MTTF) of the PMFSM system is 14.8% higher than that of the conventional structure. Compared with the conventional evaluation method based on the Markov model, the two methods have similar reliability results for conventional and novel PMFSM systems, and the variation trend is the same. It shows that the proposed reliability evaluation method is feasible and effective, and the calculation is simple. The work in this study provides a basis for the design of the novel PMFSM system, and a practical engineering method to efficiently evaluate the reliability of other motor systems.

关键词： Permanent magnet flux-switching machine (PMFSM) reliability evaluation hidden Markov model baum-welch algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis of body sensor networks using hidden Markov model

引用

PEER-TO-PEER NETWORKING AND APPLICATIONS 2017年第6期10卷 1285-1298页

作者： Zhang, Haibin Liu, Jiajia Li, Rong Le, Hua Xidian Univ Sch Cyber Engn Xian 710071 Shaanxi Peoples R China

In this paper, we focus on medical body sensor networks collecting physiological signs to monitor the health of patients. We propose a Hidden Markov Model (HMM) based method for fault diagnosis of measured data transmitted from sensors. We firstly verify the Markov property of temporal data sequences from medical databases. Then we improve the baum-welch algorithm at two aspects to estimate parameters of HMMs by history training data, and use the Viterbi algorithm to determine whether the new sensor reading is faulty. Finally, we do experiments on both real and synthetic medical datasets to study the performance of the fault diagnosis method. The result shows that the proposed approach possesses a good detection accuracy with a low false alarm rate.

关键词： Fault diagnosis Body sensor networks Hidden Markov model baum-welch algorithm Viterbi decoding

来源：评论

学校读者我要写书评

暂无评论

Detection of steering events based on vehicle logging data using hidden Markov models

引用

INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2016年第3期70卷 278-295页

作者： Maghsood, Roza Johannesson, Par Chalmers Univ Technol Math Sci SE-41296 Gothenburg Sweden SP Tech Res Inst Sweden POB 24036 SE-40022 Gothenburg Sweden

In vehicle design it is desirable to model the loads by describing load environment, customer usage and vehicle dynamics. In this study a method will be proposed for detection of steering events such as curves and manoeuvring using on-board logging signals available on trucks. The method is based on hidden Markov models (HMMs), which are probabilistic models that can be used to recognise patterns in time series data. In an HMM, 'hidden' refers to a Markov chain where the states are not observable. However, observations depending on the hidden Markov chain can be observed. The idea here is to consider the current driving event as the hidden state, while the on-board logging signals generate the observed sequence. Examples of curve detection are presented for both simulated and measured data on a truck. The classification results indicate that the method can recognise left and right turns with small misclassification errors.

关键词： HMMs hidden Markov models Markov chain Viterbi algorithm baum-welch algorithm steering events event classification on-board logging signals lateral acceleration

来源：评论

学校读者我要写书评

暂无评论

Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid

引用

IEEE SYSTEMS JOURNAL 2015年第4期9卷 1529-1540页

作者： Li, Ding Jayaweera, Sudharman K. Univ New Mexico Dept Elect & Comp Engn Commun & Informat Sci Lab Albuquerque NM 87131 USA

In this paper, a hierarchical smart grid architecture is presented. The concept of smart home is extended in two aspects: 1) from traditional households with smart devices, such as advanced metering infrastructure, to intelligent entities with instantaneous and distributive decision-making capabilities;and 2) from individual households to general customer units of possibly large scales. We then develop a hidden mode Markov decision process (HM-MDP) model for a customer real-time decision-making problem. This real-time decision-making framework can effectively be integrated with demand response schemes, which are prediction based and therefore inevitably lead to real-time power-load mismatches. With the baum-welch algorithm adopted to learn the nonstationary dynamics of the environment, we propose a value iteration (VI)-based exact solution algorithm for the HM-MDP problem. Unlike conventional VI, the concept of parsimonious sets is used to enable a finite representation of the optimal value function. Instead of iterating the value function in each time step, we iterate the representational parsimonious sets by using the incremental pruning algorithm. Although this exact algorithm leads to optimal policies giving maximum rewards for the smart homes, its complexity suffers from the curse of dimensionality. To obtain a low-complexity real-time algorithm that allows adaptively incorporating new observations as the environment changes, we resort to Q-learning-based approximate dynamic programming. Q-learning offers more flexibility in practice because it does not require specific starting and ending points of the scheduling period. Performance analysis of both exact and approximate algorithms, as compared with the other possible alternative decision-making strategies, is presented in simulation results.

关键词： Approximate dynamic programming (ADP) baum-welch algorithm hidden mode Markov decision process (HM-MDP) incremental pruning (IP) Q-learning smart home

来源：评论

学校读者我要写书评

暂无评论

A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2000年第4期30卷 463-473页

作者： Ying, J Kirubarajan, T Pattipati, KR Patterson-Hine, A Univ Connecticut Dept Elect & Syst Engn Storrs CT 06269 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

ln this paper, we present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm Ends the most likely state evolution, given a sequence of uncertain test outcomes over time. We also present a method to estimate online the HMM parameters, namely, the state transition probabilities, the instantaneous probabilities of test outcomes given the system state and the initial state distribution, that are fundamental to HMM-based adaptive fault diagnosis, The efficacy of parameter estimation method is demonstrated by comparing the diagnostic accuracies of an algorithm with complete knowledge of HMM parameters with those of an adaptive one. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in computational complexity versus performance of the diagnostic algorithm are also discussed.

关键词： baum-welch algorithm fault diagnosis Hamming distance hidden Markov models imperfect tests Viterbi decoding

来源：评论

学校读者我要写书评

暂无评论

Obtaining the Most Likely Path in Stochastic Hidden Input Automata by Using Limited Optimal Discrete Control

引用

IEEE ACCESS 2024年 12卷 14776-14786页

作者： Ozbaltan, Mete Kurucan, Mehmet Erzurum Tech Univ Dept Comp Engn TR-25050 Erzurum Turkiye Ardahan Univ Dept Comp Engn TR-75002 Ardahan Turkiye

We propose a new modeling framework to compute the most likely path for stochastic hidden systems;where the computation is based on the control theory of discrete event systems. The main innovation in this proposed model is calculating which event will have a higher probability of occurring in the future by applying k-step to the likelihood of events occurring at discrete times, which will give us the best way to transition between situations. We encode the problem as a node built with synchronous data-flow equations;then we apply the synthesis algorithm to the node in order to generate a controller that will find the most likely state sequence;where the algorithm is limited to a sliding window of a fixed number of discrete steps. We experimentally evaluate and validate our approach by comparing it with several algorithms, which are the most common and suitable algorithms applied for the best path calculation.

关键词： baum-welch algorithm discrete controller synthesis hidden systems optimal control Viterbi algorithm

来源：评论

学校读者我要写书评

暂无评论

Hidden Gauss-Markov models for signal classification

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2002年第6期50卷 1355-1367页

作者： Ainsleigh, PL Kehtarnavaz, N Streit, RL USN Undersea Warfare Ctr Newport RI 02841 USA Univ Texas Dept Elect Engn Richardson TX 75080 USA

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the baum, Viterbi, and baum-welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss-Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that the baum and Viterbi algorithms for HGMMs are implemented by two different formulations of the fixed-interval Kalman smoother. The measurement likelihoods obtained from the forward pass of the HGMM baum algorithm and from the Kalman-filter innovation sequence are shown to be equal. A direct link between the baum-welch training algorithm and an existing expectation-maximization (EM) algorithm for Gaussian models is demonstrated. A new expression for the cross covariance between time-adjacent states in HGMMs is derived from the off-diagonal block of the conditional joint covariance matrix. A parameter invariance structure is noted for the HGMM likelihood function. CS-HMMs and HGMMs are extended to incorporate mixture densities for the a priori density of the initial state. Application of HGMMs to signal classification is demonstrated with a three-class test simulation.

关键词： baum-welch algorithm continuous-state HMM EM algorithm fixed-interval smoother forward-backward algorithm hidden Markov model Kalman filter maximum likelihood classification mixture density

来源：评论

学校读者我要写书评

暂无评论

High-order extensions of the double chain Markov model

引用

STOCHASTIC MODELS 2002年第2期18卷 193-227页

作者： Berchtold, A Univ Lausanne Inst Appl Math SSP BFSH2 CH-1015 Lausanne Switzerland Univ Washington Seattle WA 98195 USA

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.

关键词： double chain Markov model (DCMM) high-order transitions mixture transition distribution model (MTD) forward-backward algorithm baum-welch algorithm viterbi algorithm DNA pewee behavior

来源：评论

学校读者我要写书评

暂无评论

Performance analysis of HMM- and ANN-based spectrum vacancy predictor behaviour for cognitive radios

引用

INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2012年第4期11卷 206-213页

作者： Karia, Deepak C. Lande, B. K. Daruwala, R. D. Univ Mumbai SPIT Dept Elect & Telecommun Bombay 400058 Maharashtra India Univ Mumbai SAKEC Dept Elect Bombay 400088 Maharashtra India Univ Mumbai VJTI Dept Elect Bombay 400019 Maharashtra India

Cognitive Radios (CRs) are devices, which should be cognizant about the Spectrum Holes upon which the idea of a CR resides, which relates to the sensing and channel management function of the device. CR, therefore, must employ channel prediction techniques so as to decide the usage of channel and also prevents interference with the primary users (or incumbent users). In this paper, we use HMM to predict the channel usage patterns and to determine the channel occupancy states. We make use of BWA to train the parameters of the HMM model, Viterbi algorithm to estimate the hidden state of the channel and BWFA to predict the next state of the sequence. In addition to this, we compare the performance of the HMM-based model with that of a neural network-based predictor, which employs a time-delay feed forward network and which uses back propagation algorithm for training.

关键词： CRs cognitive radios channel prediction HMM hidden Markov models baum-welch algorithm Viterbi algorithm real-time data distributed data neural network back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

An Approach for Automated Kannada Subtitle Generation from Kannada Video

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第SUPP01期31卷 101-119页

作者： Santosh Jenila Livingston, L. M. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India Canara Engn Coll Dept Comp Sci & Engn Mangalore India

This paper presents an automated Kannada subtitle generator from Kannada video which is implemented to assist people with auditory problems for watching videos. Henceforth the subtitle generation has become an important task for supporting such special people and it integrates an audio extraction and a speech recognition module. Three phases of the proposed technique were implemented, such as extracting audio from video, Recognition of Speech and Generation of Subtitle. An adaptive speech recognition module is implemented AMFCC for feature extraction which was an alternative to the most commonly used FFT. Hankel transform which was similar to FFT, but includes no elementary particles such as FFT. In addition to it, in the decoder acoustic module, such as Adaptive Hidden Markov Model using the baum-welch algorithm is utilized instead of a Viterbi algorithm to reduce the computational time and memory usage. The text file from the speech recognition module is rendered to synchronize the missing offset with the video using parallel processing by defining the start time, the end time, the delay time. Best outcomes are demonstrated by the experimental results of the proposed technique with 98.4% of accuracy compare with existing techniques. The proposed technique which gives 3.8% better accuracy performance compare with existing technique i.e. MFCC, DNN and CNN.

关键词： Subtitle generation MFCC Hankel transform fast fourier transform baum-welch algorithm Viterbi algorithm speech recognition Hidden Markov model GMM and neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：