Coal mine belt conveyor can guarantee the coal mine production stable and efficient. On how to effectively predict abnormal accident occurrence time, this paper puts forward a method to predict the abnormal accident occurrence time based on Hidden Markov Model and Hidden Semi-Markov Model. Large amount of time series is collected through belt conveyor protection sensors. The corresponding HMM o...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...

The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical for learning from sequential and time-series data. A sticky HDP-HMM proposed to strengthen self-persistence probability in HDP-HMM. However, entangles strength prior transition together, limiting its expressiveness. Here, we propose more general mo...

The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than can the first-order hidden Markov model (HMM). The relations among the DHO-HMM, high-order HMM (HOHMM), hidden semi-Markov model (HSMM), and HMM are presented and discussed. Recursive forward and backward probability functions for the partial observation sequence were...

Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method 2484 Badreddine Benyacoub et al. based on HMM techni...

the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...

Prediction of physical particular phenomenon is based on knowledge of the phenomenon. This knowledge helps us to conceptualize this phenomenon around different models. Hidden Markov Models (HMM) can be used for modeling complex processes. This kind of models is used as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent...

A hidden Markov model method for estimating an a posteriori distribution of the amplitude of communications signals is presented. As the signal to noise ratio decreases the hidden Markov model method is shown to perform significantly better than a conventional histogram method for characterising the amplitude distribution. The HMM estimation is performed within a Expectation Maximisation method...

Denoising of real world images that are degraded by Gaussian noise is a long established problem in statistical signal processing. The existing models in time-frequency domain typically model the wavelet coefficients as either independent or jointly Gaussian. However, in the compression arena, techniques like denoising and detection, states the need for models to be nonGaussian in nature. Proba...