A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tes...A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.展开更多
In this letter, we briefly describe a program of self adapting hidden Markov model (SA HMM) and its application in multiple sequences alignment. Program consists of two stage optimisation algorithm.
Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is u...Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.展开更多
A novel method was proposed, which extracted video object＇ s track and analyzed video object＇ s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-...A novel method was proposed, which extracted video object＇ s track and analyzed video object＇ s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-based track sequence and orientation-based track sequence of the video object. Then the pro- posed hidden markov model （HMM） based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects＇ behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object＇ s various behaviors accurately. It provides a base for further research on video object behavior.展开更多
In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in th...In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.展开更多
Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct...Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.展开更多
基金This project is supported by National Natural Science Foundation of China(No.50075079).
文摘A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.
文摘In this letter, we briefly describe a program of self adapting hidden Markov model (SA HMM) and its application in multiple sequences alignment. Program consists of two stage optimisation algorithm.
文摘Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.
基金supported by the High Technology Research and Development Programme of China(No.2004AA742209)
文摘A novel method was proposed, which extracted video object＇ s track and analyzed video object＇ s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-based track sequence and orientation-based track sequence of the video object. Then the pro- posed hidden markov model （HMM） based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects＇ behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object＇ s various behaviors accurately. It provides a base for further research on video object behavior.
文摘In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.
文摘Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.
基金Supported by the National Natural Science Foundation of China under Grant Nos.90412010 60573166 (国家自然科学基金)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.2007108 (高等学校博士学科点专项科研基金)the HP University Collaborative Foundation of China under Grant No.HLCFY08-001 (惠普大学合作基金)
