scholarly journals Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Gabriel Pino ◽  
José Roberto Ribas ◽  
Luciana Fernandes Guimarães
Keyword(s):  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Power Plants ◽  
Hidden Markov ◽  
Wavelet Packet ◽  
Transition Probability ◽  
Peak Load ◽  
Wavelet Packet Transform ◽  
Hydro Power

The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components’ degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines’ nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings’ mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

Prediction of Facies Distribution in a Clastic Reservoir Using a Hidden Markov Model Combined With an Expectation-Maximization Algorithm

2016 ◽  
Author(s):  
Hwasoo Suk ◽  
Baehyun Min ◽  
Joe M. Kang ◽  
Cheolkyun Jeong
Keyword(s):  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Expectation Maximization ◽  
Hidden Markov ◽  
Transition Probability ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Facies Distribution ◽  
Clastic Reservoir

This study determines facies distribution in a clastic reservoir using a hidden Markov model combined with an Expectation-Maximization algorithm. Iterating expectation and maximization steps of the algorithm builds the hidden Markov model by tuning the model parameters including initial state distribution, state transition probability distribution, and observable symbol probability distribution. Optimized model parameters contribute to improving the predictability of facies distribution along the well trajectory using core and logging data.

Analysis of Consumption of Rural Residents in Jilin Province Based on Hidden Markov Model

2014 ◽  
Vol 971-973 ◽  
pp. 2281-2284
Author(s):  
Xin Zhao ◽  
Qian Sun ◽  
Yan Hong Huang Fu ◽  
Chao Ran Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Jilin Province ◽  
Probability Estimation ◽  
Rural Residents ◽  
Future Situation ◽  
Estimation Function ◽  
Sample Interval

Analysis status consumption of residents according to the statistical data in the recently twenty years of rural residents in Jilin province the Engel Coefficient.Select the sample interval properly based on hidden markov model,modeled using MATLAB and estimate the transition probability between states using probability estimation function of MATLAB’s hidden markov model toolbox, contact probability estimation in Markov model toolbox function, and predicting the Engel Coefficients of rural residents in the province for the next ten years (2013-2022). Studies have shown that, using the hidden Markov model established by MATLAB can accurately predict the future situation of residents consumption.

scholarly journals Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy

2007 ◽  
Vol 11 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Ji-Young Ho ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Online Game ◽  
Classification Approach ◽  
Game Players ◽  
Better Than

Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

A HIDDEN MARKOV MODEL FOR PREDICTING PROTEIN INTERFACES

2007 ◽  
Vol 05 (03) ◽  
pp. 739-753 ◽  
Author(s):  
CAO NGUYEN ◽  
KATHELEEN J. GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Protein Interactions ◽  
Protein Function ◽  
Structural Information ◽  
Protein Complexes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Accessible Surface Area ◽  
Protein Chain

Protein–protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.

Map-matching approach based on link factor and hidden Markov model

2021 ◽  
pp. 1-17
Author(s):  
Haiyan Zhang ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Xiaoyao Zheng ◽  
Xuejing Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Map Matching ◽  
The Road ◽  
Positioning Errors ◽  
Road Section ◽  
Gps Devices

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates.

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