• 품질경영학회지
• /
• 제25권1호
• /
• pp.31-43
• /
• 1997

In this study, the pros and cons of TPM and RCM were comparatively evaluated at various aspects : a, pp.ication process, objectives, maintenance items, organizations, analysis of maintenace methods, etc. It is found that TPM can be considered as a management discipline. However, in TPM, there seldom exist concrete rules or guidelines to select a maintenance scheme. RCM, which is a widely used maintenance scheme for aircrafts or power plants, has a good analysis and decision logic for maintenance planning. In the paper, similar decision rules are adopted to TPM deployment to get an effective and effecient maintenance Planning.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
• /
• pp.237-240
• /
• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

• 대한산업공학회지
• /
• 제15권2호
• /
• pp.33-44
• /
• 1989

This paper presents stochastic-statistical dominance rules which eliminate dominated alternatives thereby reduce the number of satisficing alternatives to a manageable size so that the decision maker can choose the best alternative among them when neither the utility function nor the probability distribution of outcomes is exactly known. Specifically, it is assumed that only the characteristics of the utility function and the value function are known. Also, it is assumed that prior probabilities of the mutually exclusive states of nature are not known, but their relative bounds are known. First, the notion of relative risk aversion is used to describe the decision maker's attitude toward risk, which is defined with the acknowledgement that the utility function of the decision maker is a composite function of a cardinal value function and a utility function with-respect to the value function. Then, stochastic-statistical dominance rules are developed to screen out dominated alternatives according to the decision maker's attitude toward risk represented in the form of the measure of relative risk aversion.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
• /
• pp.97-101
• /
• 1997

In recent years, there have been a number of applications of fuzzy logic in fuzzy reasoning system. The main objective of these applications is to approximate a decision making using the fuzzy reasoning system. This paper designs a fuzzy reasoning model for the decision making of registration fee at a private school, implements it applying for linguistic variables and fuzzy rules, and evaluates the practical availability of the model. The system accepts fuzzy rules, the type of membership functions, the domain of fuzzy sets and hedge, and fuzzifies the linguistic variables to generates fuzzy sets. The fuzzy sets generated are combined to constructs a solution fuzzy set. Finally, the system defuzzifies the solution fuzzy set to calculate a scalar value which is used for decision making.

• Journal of the Korean Data and Information Science Society
• /
• 제16권4호
• /
• pp.1027-1039
• /
• 2005

Consider the problem of estimating a $p{\times}1$ mean vector $\theta(p\geq4)$ under the quadratic loss, based on a sample $X_1$, $X_2$, $\cdots$, $X_n$. We find a Lindley type decision rule which shrinks the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm $\parallel\;{\theta}-\bar{{\theta}}1\;{\parallel}$ is restricted to a known interval, where $bar{{\theta}}=\frac{1}{p}\;\sum\limits_{i=1}^{p}{\theta}_i$ and 1 is the column vector of ones. In this case, we characterize a minimal complete class within the class of Lindley type decision rules. We also characterize the subclass of Lindley type decision rules that dominate the sample mean.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제3권1호
• /
• pp.44-51
• /
• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• 통합자연과학논문집
• /
• 제10권1호
• /
• pp.33-39
• /
• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p-q{\geq}3)$, $q=rank(P_V)$ with a projection matrix $P_v$ under the quadratic loss, based on a sample $X_1$, $X_2$, ${\cdots}$, $X_n$. We find a James-Stein type decision rule which shrinks towards projection vector when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}{\theta}-P_V{\theta}{\parallel}$ is restricted to a known interval, where $P_V$ is an idempotent and projection matrix and rank $(P_V)=q$. In this case, we characterize a minimal complete class within the class of James-Stein type decision rules. We also characterize the subclass of James-Stein type decision rules that dominate the sample mean.

• 한국항만학회지
• /
• 제13권2호
• /
• pp.255-266
• /
• 1999

The purpose of this paper is to describe the design of the decision support system for container terminal ship planning and to introduce the implemented system. The ship planning in container terminals consists of three major decision processes -the working schedule of gantry cranes the discharging sequence of inbound containers the loading position and sequence of outbound containers. For making these decision the proposed system can provide two ship planning modes the interactive planning mode with user-friendly GUI and the automated planning made. To implement the automated planning routine we acquired the planning rules from the expert planner in container terminals and developed an expert system based on the rules. Finally we evaluated the system developed and the potential for commercialization by using container terminal data.

• 대한산업공학회지
• /
• 제40권4호
• /
• pp.375-381
• /
• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.

• 한국콘텐츠학회논문지
• /
• 제18권5호
• /
• pp.265-279
• /
• 2018

본 연구에서는 데이터 마이닝 기법을 활용하여 근로자의 이직준비 여부에 관한 예측모형을 구축하는 실험을 진행하였다. 이를 위해, 한국고용정보원 주관으로 수집된 "2015년 대졸자 직업 이동경로조사" 데이터를 사용하였다. 이직준비 여부 예측모형에는 의사결정나무, 베이즈넷, 인공신경망 알고리즘이 사용되었다. 전체 직종을 대상으로 한 분석에서는 의사결정나무 기반 예측모형에서 최고 예측률을 기록하였으며, 이직준비 여부에 영향을 주는 요인은 '근로시간 형태', '종사상 지위', '정규직 여부', '주당 정규 근로시간', '주당 정규 근로일', '개인의 발전가능성'으로 나타났다. 의사결정나무 기반 예측모형의 결과를 활용하여 근로자 전반에 관한 12개의 이직준비 여부 규칙을 최종 도출하였고, 도출된 규칙을 바탕으로 근로자의 고용유지 강화에 도움을 주는 방안들을 제안하였다. 또한 직종별 영향 요인을 분석하기 위해 직종을 사무, 문화예술, 건설, 정보기술 분야로 구분하여 실험을 진행하였다. 그 결과 사무 분야는 10개, 문화예술 분야는 9개, 건설 분야는 4개, 그리고 정보기술 분야는 6개의 이직준비 규칙이 도출되었고 이를 토대로 직종별 맞춤화된 고용유지 강화 방안을 제시하였다.