• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.147-156
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• 2013

Each field of modern society, industrialization and the development of science and technology are rapidly changing. However, as a side effect of rapid social change has caused various problems. Crime of the side effects of rapid social change is a big problem. In this paper, a model for predicting crime and Markov chains applied to the crime, predictive modeling is proposed. Markov chain modeling of the existing one with the overall status of the case determined the probability of predicting the future, but this paper predict the events to increase the probability of occurrence probability of the prediction and the recent state of the entire state was divided by the probability of the prediction. And the whole state and the probability of the prediction and the recent state by applying the average of the prediction probability and the probability of the prediction model were implemented. Data was applied to the incidence of crime. As a result, the entire state applies only when the probability of the prediction than the entire state and the last state is calculated by dividing the probability value. And that means when applied to predict the probability, close to the crime was concluded that prediction.

• Journal of Information Management
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• v.20 no.1
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• pp.66-85
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• 1989

The purpose of this study is to verify the following two hypotheses by using a test collection of 3.815 documents on the subject of chemistry. First hypothesis is that a Markov chain model can be used t9 describe and predict authors' movements among subareas of a discipline. Second hypothesis is that a transition matrix of the Markov chain can be applied to describ the intellectual structure of a discipline en the multidimensional space. The results of this study have shown that the Markov chain is a good model to be used to study the movement of korean chemists in 7 subtopics in chemistry and understand the intellectual structure of chemistry.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.448-458
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• 1999

This paper proposes sequential control charts with an upper bound on sample size. Existing sequential control charts have no restriction on the number of observations at a sampling point. For situations where sampling and testing an item is time-consuming or expensive, sequential control charts may not be directly applied. When the number of observations in a sampling point reaches the upper bound and there is no out-of-control signal, the proposed cumulative sequential control chart defers the decision to the next sampling point of which starting value is the value of the current statistic. Two Markov chains, inner and outer chains, are used to derive the formulas for evaluating the performance of the proposed chart. It is compared with $\bar{X}$ and cumulative sum control charts with fixed and variable sample sizes. The fast initial response (FIR) feature is studied. Guidelines for the design of the proposed charts are also given.

• Journal of Korean Society of Forest Science
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• v.31 no.1
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• pp.62-77
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• 1976

One of the most important tasks of planned forest management is the determination of the allowable cut. There are therefore many methods of forest regulation which have been applied by the various administrations of Germany, France, and other countries. However, most of the older methods of forest regulation have little or no application in Europe. In this paper, a new method of forest regulation is introduced. It has been developed mainly by Dr. T. Suzuki who is a prefessor of Nagoya University. It is felt that the material covered in this paper is appropriate for use in forest management courses at the senior or first-year-graduate level.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.09a
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• pp.99-107
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• 1995

The Phase-type(PH) distribution, defined as a distribution of the time until the absorption in a finite continuous-time Markov chain state with one absorbing state, has been widely used for various stochastic modelling. But great computational burdens often make us hesitate to apply PH methods. In this paper, we propose a seemingly efficient approximation method for phase type distributions. We first describe methods to bound the first passage time distribution in continuous-time Markov chains. Next, we adapt these bounding methods to approximate phase-tupe distributions. Numerical computation results are given to verify their efficiency.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.560-567
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• 2020

The complexity of safety critical systems of Nuclear Power Plant continues to increase rapidly due its transition from analog to digital systems. It has thus become progressively more imperative to model these systems prior to their implementation in order to meet the high performance, safety and reliability requirements. Timed Petri Nets (TPNs) have been widely used to model such systems for non-functional analysis. The paper presents a novel methodology for the analysis of the performance metrics using PN modeling. The paper uses the isomorphism property of the TPNs and the Markov chains for the performance analysis of the safety critical systems. The presented methodology has been validated on a Shutdown System of a Nuclear Power Plant.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.4
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• pp.185-193
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• 2004

Reasonable prediction of bridge deterioration is the most important factor in the determination of repair time or optimized maintenance policy for bridges. To accomplish these purposes, the proposed method is composed of quantitative condition assessment, Markov chains and Bayesian estimates. Example predictions of concrete slab bridges in Korea were illustrated with higher reasonability than those of existing methods such as expert opinion and visual inspection only.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1275-1283
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• 2015

Markov chains for active tracking which assigns additional track illuminations evenly between search illuminations for a radar system are presented in this article. And some quantitative analyses on track formation range are discussed by using them. Compared with track-while-search (TWS) tracking that uses scan-to-scan correlation at search illuminations for tracking of a target, active tracking has shown the maximum improvement in track formation range of about 27.6%. It is also shown that the number and detection probability of additional track beams have impact on the track formation range. For the consideration of radar resource management at the preliminary radar system design stage, the presented analysis method can be used easily without the need of Monte Carlo simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2452-2457
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• 2012

When a quality inspection process rejects a product unit, consecutive repeated inspections are sometimes conducted for the rejected unit to reduce a false reject possibility. This paper analyzes a special inspection process that allows up to two times of consecutive testing for each product to decrease type I inspection errors. This study uses a Markov chain to model the steps of the inspection process and a product unit's quality states during inspection. Historical inspection results from a company are used as the data for the Markov chain model. Using the Markov chain model and data, this study analyzes the effect of this special inspection rule on the proportion of the final quality levels and scrap rate. The results demonstrate that this inspection process of possible double testing could help reduce unnecessary rejects and consequently decrease material and production costs.