Hidden Markov Model of Two-Component System with Group Instantly Replenished Time Reserve

2021 ◽  
Vol 27 (2) ◽  
pp. 64-71
Author(s):  
S. M. Sidorov ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Phase State ◽  
Markov Models ◽  
Hidden Markov ◽  
Operation Time ◽  
Two Component System ◽  
Component System ◽  
Two Component ◽  
Time Reserve

Most systems allow the construction of a semi-Markov model. However, during the operation of the system, full information contained in the state encoding is not always available, but it is possible to obtain some signal (information). Tasks arise to assess the consistency of the model with the received data (signals), to refine the model and its parameters. Such parameters can be characteristics of random values characterizing system operation, time reserve value, etc. The theory of hidden Markov models allows solving these problems. In order to move from a semi-Markov model of the system to its hidden Markov model, it is proposed to first the semi-Markov model merge using a stationary phase merging algorithm. In this paper, on the basis of the semi-Markov model with a common phase state space of a two-component system with a group instantly replenished timereserve, we construct a hidden Markov model of a two-component system with a group instantly replenished time reserve. It is used to evaluate the characteristics and predict the states of the system in question based on the received vector of signals. The influence of the time reserve value on the probability of occurrence of the obtained vector of signals is shown.

scholarly journals Hidden Markov Model of System Elements Technical Maintenance by Age

2020 ◽  
Vol 216 ◽  
pp. 01030
Author(s):  
Yuriy Obzherin ◽  
Mikhail Nikitin ◽  
Stanislav Sidorov
Keyword(s):  
Power Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Two Component System ◽  
Component System ◽  
Two Component ◽  
Technical Maintenance ◽  
Methods Of Operation

Technical maintenance is between the methods of operation reliability and effectiveness increasing for systems of different purposes including power systems. In the paper the hidden semi-Markov model of technical maintenance is built basing on the semi-Markov model of two-component system elements technical maintenance by age. The hidden Markov model is used to solve the problems of dynamics analyzing, predicting the states of a system modelled based on the vector of signals obtained during its operation.

scholarly journals Semi-Markov model of a technical system with the component-wise instantly replenished time reserve

2018 ◽  
Vol 224 ◽  
pp. 04008 ◽  
Author(s):  
Yuriy E. Obzherin ◽  
Stanislav M. Sidorov ◽  
Sergey N. Fedorenko
Keyword(s):  
Markov Model ◽  
Approximate Determination ◽  
Two Component System ◽  
Additional Time ◽  
Component System ◽  
Time Redundancy ◽  
Two Component ◽  
Technical Systems ◽  
Time Reserve

Time redundancy is one of the methods to increase the reliability and efficiency of technical systems. When it is used, the system is given additional time (a time reserve) for restoring characteristics. In this paper we construct a semi-Markov model of a two-component system with a component-wise instantly replenished time reserve. In this paper we construct a semi-Markov model of a two-component system with a component-wise instantaneous replenishment of the time reserve. For an approximate determination of the stationary characteristics of the reliability of the system, the phase merging scheme algorithm is used.

scholarly journals Analysis of reliability of systems with component-wise storages

2018 ◽  
Vol 58 ◽  
pp. 02024 ◽  
Author(s):  
Yuriy E. Obzherin ◽  
Stanislav M Sidorov ◽  
Mikhail M Nikitin
Keyword(s):  
Phase Space ◽  
Energy Systems ◽  
Two Component System ◽  
Additional Time ◽  
Component System ◽  
Time Redundancy ◽  
Reliability Characteristics ◽  
Two Component ◽  
Semi Markov Processes ◽  
Time Reserve

Time redundancy is a method of increasing the reliability and efficiency of the operation of systems for various purposes, in particular, energy systems. A system with time redundancy is given additional time (a time reserve) for restoring characteristics. In this paper, based on the theory of semi-Markov processes with a common phase space of states, a semi-Markov model of a two-component system with a component-wise instantly replenished time reserve is constructed. The stationary reliability characteristics of the system under consideration are determined.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models

2016 ◽  
Vol 19 (58) ◽  
pp. 1
Author(s):  
Daniel Fernando Tello Gamarra
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hand Movement ◽  
Gaze Behavior ◽  
Improved Method ◽  
The Relationship

We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM). We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.

scholarly journals A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

2019 ◽  
Vol 24 (1) ◽  
pp. 14 ◽  
Author(s):  
Luis Acedo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Mathematical Linguistics ◽  
Underlying Structure ◽  
Internal States ◽  
Consonant Sounds

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.

Hidden Markov model interpretations of neural networks

2000 ◽  
Vol 23 (4) ◽  
pp. 494-495
Author(s):  
Ingmar Visser
Keyword(s):  
Neural Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Distributed Representations ◽  
Hidden Layer

Page's manifesto makes a case for localist representations in neural networks, one of the advantages being ease of interpretation. However, even localist networks can be hard to interpret, especially when at some hidden layer of the network distributed representations are employed, as is often the case. Hidden Markov models can be used to provide useful interpretable representations.

scholarly journals Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Concepción Violán ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Quintí Foguet-Boreu ◽  
Jose M. Valderas ◽  
...  
Keyword(s):  
Primary Care ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Models ◽  
Cox Regression ◽  
Hidden Markov ◽  
Population Based ◽  
Specific Pattern ◽  
Mediterranean Population ◽  
Primary Care Population

Abstract This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.

SENTENCE LIPREADING USING HIDDEN MARKOV MODEL WITH INTEGRATED GRAMMAR

2001 ◽  
Vol 15 (01) ◽  
pp. 161-176 ◽  
Author(s):  
KEREN YU ◽  
XIAOYI JIANG ◽  
HORST BUNKE
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Systematic Approach ◽  
Markov Models ◽  
Hidden Markov ◽  
E Mail

In this paper, we describe a systematic approach to the lipreading of whole sentences. A vocabulary of elementary words is considered. Based on the vocabulary, we define a grammar that generates a set of legal sentences. Our lipreading approach is based on a combination of the grammar with hidden Markov models (HMMs). Two different experiments were conducted. In the first experiment a set of e-mail commands is considered, while the set of sentences in the second experiment is given by all English integer numbers up to one million. Both experiments showed promising results, regarding the difficulty of the considered task.

scholarly journals Fuzzy Hidden Markov Models for Indonesian Speech Classification

2012 ◽  
Vol 16 (3) ◽  
pp. 381-387 ◽  
Author(s):  
Intan Nurma Yulita Houw Liong The ◽  
◽  
Adiwijaya ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Speech Signal ◽  
Markov Models ◽  
Hidden Markov ◽  
New Combination ◽  
Speech Classification ◽  
Different Characteristics ◽  
Better Than

Indonesia has many tribes, so that there are many dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. The solution is a new combination of fuzzy on hidden Markov models. The result shows a new version of fuzzy hiddenMarkovmodels is better than hidden Markov model.

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