scholarly journals Gaussian mixture model-hidden Markov model based nonlinear equalizer for optical fiber transmission

2020 ◽  
Vol 28 (7) ◽  
pp. 9728 ◽  
Author(s):  
Fukui Tian ◽  
Qingyi Zhou ◽  
Chuanchuan Yang
Keyword(s):  
Optical Fiber ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Optical Fiber Transmission ◽  
Model Based ◽  
Fiber Transmission

scholarly journals Human action recognition based on mixed gaussian hidden markov model

2021 ◽  
Vol 336 ◽  
pp. 06004
Author(s):  
Jiawei Xu ◽  
Qian Luo
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Action Recognition ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Gaussian Mixture ◽  
Human Action ◽  
Model Based

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance

2018 ◽  
Vol 18 (3) ◽  
pp. 853-868 ◽  
Author(s):  
Shenfang Yuan ◽  
Jinjin Zhang ◽  
Jian Chen ◽  
Lei Qiu ◽  
Weibo Yang
Keyword(s):  
Fatigue Crack ◽  
Markov Model ◽  
Crack Propagation ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Guided Wave ◽  
Time Varying ◽  
Damage Evaluation ◽  
Model Based

During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the performance improvement of hidden Markov model itself. In this article, the training stability problem when constructing the guided wave–hidden Markov model initialized by usually adopted k-means clustering method and its influence to damage evaluation were researched first by applying it to fatigue crack propagation evaluation of an attachment lug. After illustrating the problem of stability induced by k-means clustering, a novel uniform initialization Gaussian mixture model–based guided wave–hidden Markov model was proposed that provides steady and reliable construction of the guided wave–hidden Markov model. The advantage of the proposed method is demonstrated by lug fatigue crack propagation evaluation experiments.

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