Abstract Hidden Markov Model Research Articles

Student's classroom behaviour recognition method based on abstract hidden Markov model

Student's classroom behaviour recognition method based on abstract hidden Markov model

Student’s classroom behavior recognition method based on abstract Hidden Markov model

Student’s classroom behavior recognition method based on abstract Hidden Markov model

Human action recognition by fuzzy hidden Markov model

Hidden Markov model (HMM) has been widely applied in human action recognition. In this paper an extension of HMM called fuzzy hidden Markov model (fuzzy HMM) is used for action recognition. It tries to increase the classification performance and decrease the information loss due to feature vector quantisation. Using fuzzy concepts with HMM leads to better recognition of similar actions such as walking, jogging and running. Two feature extraction methods including skeleton and space-time approaches are used for action representation. Actions could be represented efficiently using skeleton features where scene background is plain. Space-time features are extracted directly from video, and therefore avoid possible failures of other pre-processing methods. We propose space-time-based features by considering temporal relation between them. Experimental results show the effectiveness of fuzzy HMM in human action recognition. Moreover, it is shown that fuzzy HMM leads to significant improvement in recognition of similar actions. The accuracy rates of fuzzy HMM in comparison to HMM are incremented 3.33% and 5.59% in Weizmann and KTH datasets respectively.

불완전 시계열 데이터를 위한 이산 HMM 학습 알고리듬

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach

Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the clustering effect of Viterbi training and Baum–Welch training, a novel clustering-based model pre-training approach is proposed. It can optimize model parameters and model structure by turns, until the representative states of all models are explored. Finally, the proposed approach is evaluated on two typical OCR applications, printed and handwritten Arabic text line recognition. And it is compared with some other optimization methods. The improvement of character recognition performance proves the proposed approach can make more precise state allocation. And the representative states are benefit to HMM decoding.

Comparison of the Effectiveness of 1D and 2D Hmm in the Pattern Recognition

Abstract Hidden Markov Model (HMM) is a well established technique for image recognition and has also been successfully applied in other domains such as speech recognition, signature verification and gesture recognition. HMM is widely used mechanism for pattern recognition based on 1D data. For images one dimension is not satisfactory, because the conversion of one-dimensional data into a twodimensional lose some information. This paper presents a solution to the problem of 2D data by developing the 2D HMM structure and the necessary algorithms.

Activity Recognition in Smart Homes Based on Second-Order Hidden Markov Model

Hidden Markov Model is an important approach applied to activity recognition. In the first-order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second-order Hidden Markov Model, the relevance between the current state and its previous two states is considered. Also, the relevance between the current observation and its previous state is considered. So second-order Hidden Markov Model has stronger performance of recognition of incorrect information. In my paper, second-order Hidden Markov Model is applied to activity recognition. The experiments show that our approach has higher precision than those approaches based on first-order Hidden Markov Model and based on Conditional Random Fields.

Information Extraction from Chinese Papers Based on Hidden Markov Model

Hidden Markov model HMM (1) is one of the important approaches for information extraction. In this paper, a model of the improved first-order hidden Markov HMM (2) is proposed. In the HMM (2), the output probability of the observation is not only dependent on the current state of the model, but also dependent on the previous state of the current state of the model. The algorithm of the ML and the algorithm of the Viterbi are analyzed. At last, experiments show that the HMM (2) is more precise than the HMM (1).

Study on Fault Diagnosis in Analog Circuit

Hidden Markov model (HMM) for the diagnosis of incipient faults in analog circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Subsequently, output voltages corresponding to the test frequencies are extracted from the response of analog circuits. Finally, the output voltage vectors are used to form the observation sequences, which are sent to training HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method is effective and has better recognition capability than the popularly used back-propagation (BP) network.

Study on audio classification based on 1-state HMM

Hidden markov model(HMM),based on statistical signal,plays an important role in content-based audio retrieval system.According to the characteristic that pays more attention to the type than to content of audio classification,1-state HMM was used for audio classification,which overcame the shortcoming of assumption of multi-state HMM model's initial state probabilities and state transition probabilities in the course of model-initializing.The experiment shows the method for audio classification based on 1-state HMM could decrease the misrecognition effectively and increase the accuracy of audio classification.

Heart Sound Classification Using Variable Number of States in Hidden Markov Model Considering Characteristics of the Signal

Hidden Markov model (HMM) is known to be one of the most powerful methods in the acoustic modeling of heart sound signals. Conventionally, we usually use a fixed number of states for each HMM. However, due to the various types of the heart sound signals, it seems that more accurate acoustic modeling is possible by varying the number of states in the HMM depending on the signal types to be modeled. In this paper, we propose to assign different number of states to the HMM for better acoustic modeling and consequently, improving the classification performance of the heart sound signals. Compared with when fixing the number of states, the proposed approach has shown some performance improvement in the classification experiments on various types of heart sound signals. (Journal of Korean Society of Medical Informatics 14-2, 179-187, 2008)

GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS

Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.

FAULT DIAGNOSIS TECHNIQUE OF ROTATING MACHINE BASED ON CHMM

Hidden Markov model(HMM) as a tool for disposing signal pattern which has great ability of building time sequence, has widely been used in speech recognition. It is especially fit for signal which is nonlinear, non-stationary, bad in repeating to analysis. Based on the comparability between vibration signal and sound signal, CHMM is introduced to fault diagnosis for rotating machine. CHMM is built by using 12 rank LPC cep-strum coefficient to extract feature vectors, scaled for-wards-backwards algorithm is introduced to calculate log-likelihood avoiding the data to underflow and k-means algorithm is also used to initialize the parameter. In the given observation sequence, optimizing every model with Viterbi algorithm, with baum-welch algorithm to re-estimate parameter, and the re-estimation formula is also provided. Last, four kinds of fault experiment have been simulated on the rotor test-bed, and four kinds of fault CHMM model are built. Machine's operating state is determined by calculating the maximal log-likelihood, and the results of experiment proves that this kind of method is effective.

은닉 마르코프 모형을 이용한 회전체 결함신호의 패턴 인식

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

Policy Recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Method and apparatus for training hidden Markov model

HMM training method comprising a first parameter predicting step, a centroid state set calculating step, a reconstructing step, a second parameter predicting step and a control step. In the first parameter predicting step, a parameter of an HMM (hidden Markov model) is predicted based on training data. In the centroid state set calculating step, a centroid state set is calculated by clustering the state of said HMM whose parameter is predicted in the first parameter predicting step. In the reconstructing step, an HMM is reconstructed with using the centroid state calculated in the centroid state set calculating step. In the second parameter predicting step, predicted a parameter of the HMM reconstructed in the reconstructing step with using the training data. And, the centroid step is reexecuted by the control step in the case that a likelihood of the HMM whose parameter is predicted in the second parameter predicting step does not satisfy a predetermined condition.