scholarly journals Markov model of the operations & maintenance process of vehicles scheduled to be Operated

2021 ◽  
Vol 51 (1) ◽  
pp. 213-223
Author(s):  
Zygmunt Kruk
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Operation Process ◽  
Markov Chain Theory ◽  
Vehicle Operation ◽  
Failure Intensity ◽  
Chain Theory ◽  
The Military ◽  
The Impact ◽  
Maintenance Process

Abstract The article is dedicated to the modelling of operations & maintenance of vehicles scheduled to be operated. This specific feature is illustrative of the vehicle operation process in the military system. The presented model of the operation process of vehicles scheduled to be operated, using the Markov chain theory, contains indicators and measures essential for the vehicle operation, i.e. repair defectiveness, repair intensity, usage intensity and failure intensity. This model enables to quantify the impact of the introduced changes in operational practice or changes planned as a forecast, which is shown in the examples.

scholarly journals Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

2020 ◽  
Vol 12 (1) ◽  
pp. 626-636
Author(s):  
Wang Song ◽  
Zhao Yunlin ◽  
Xu Zhenggang ◽  
Yang Guiyan ◽  
Huang Tian ◽  
...  
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Driving Mechanism ◽  
Economic Factors ◽  
Chain Model ◽  
The Impact ◽  
County Scale

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

scholarly journals Generation method for the PV power time series combining the decomposition technique and Markov chain theory

2017 ◽  
Vol 2017 (13) ◽  
pp. 2026-2031
Author(s):  
Shenzhi Xu ◽  
Xiaomeng Ai ◽  
Jiakun Fang ◽  
Jinyu Wen ◽  
Pai Li ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Decomposition Technique ◽  
Markov Chain Theory ◽  
Chain Theory

scholarly journals An ergodic algorithm for generating knots with a prescribed injectivity radius

2019 ◽  
Vol 28 (06) ◽  
pp. 1950045
Author(s):  
Kyle Leland Chapman
Keyword(s):  
Markov Chain ◽  
Lattice Model ◽  
Probability Distributions ◽  
Positive Probability ◽  
Bending Angle ◽  
Markov Chain Theory ◽  
Countable Space ◽  
Chain Theory ◽  
Random Reflections ◽  
Planar Polygon

The first provably ergodic algorithm for sampling the space of thick equilateral knots off-lattice, as a function of thickness, will be described. This algorithm is based on previous algorithms of applying random reflections. It is an off-lattice generalization of the pivot algorithm. This move to an off-lattice model provides a huge improvement in power and efficacy in that samples can have arbitrary values for parameters such as the thickness constraint, bending angle, and torsion, while the lattice forces these parameters into a small number of specific values. This benefit requires working in a manifold rather than a finite or countable space, which forces the use of more novel methods in Markov–Chain theory. To prove the validity of the algorithm, we describe a method for turning any knot into the regular planar polygon using only thickness non-decreasing moves. This approach ensures that the algorithm has a positive probability of connecting any two knots with the required thickness constraint which is used to show that the algorithm is ergodic. This ergodic sampling allows for a statistically valid method for estimating probability distributions of arbitrary functions on the space of thick knots.

Markov Chain Models of Grinding Profiles

1964 ◽  
Vol 86 (4) ◽  
pp. 383-387 ◽  
Author(s):  
H. T. McAdams
Keyword(s):  
Markov Chain ◽  
Stochastic Model ◽  
Recurrent Event ◽  
Grinding Process ◽  
Kolmogorov Equations ◽  
First Order ◽  
Markov Chain Models ◽  
Event Theory ◽  
Markov Chain Theory ◽  
Chain Theory

Profiles of abrasive surfaces are analyzed by means of Markov chain theory. The Chapman-Kolmogorov equations, together with recurrent-event theory, are used to deduce theoretical distributions for such important statistics as the distances between effective cutting points and the lengths of lands on a worn grinding surface. Both first-order and second-order Markov chains are examined for their applicability to a stochastic model of the grinding process.

Towards consensus: some convergence theorems on repeated averaging

1977 ◽  
Vol 14 (01) ◽  
pp. 89-97 ◽  
Author(s):  
S. Chatterjee ◽  
E. Seneta
Keyword(s):  
Markov Chain ◽  
Consensus Problem ◽  
Stochastic Matrices ◽  
Convergence Theorems ◽  
Strong Ergodicity ◽  
Markov Chain Theory ◽  
Inhomogeneous Markov Chain ◽  
Chain Theory ◽  
Equivalent Conditions

The problem of tendency to consensus in an information-exchanging operation is connected with the ergodicity problem for backwards products of stochastic matrices. For such products, weak and strong ergodicity, defined analogously to these concepts for forward products of inhomogeneous Markov chain theory, are shown (in contrast to that theory) to be equivalent. Conditions for ergodicity are derived and their relation to the consensus problem is considered.

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