• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.100-109
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• 1999

The bearing design includes the steps of selection bering type, selection bearing subtype, and determining the peripheral equipments. In this paper decision making methodologies are compared to propose a stepwise decision methodology to the bearing selection problem. An artificial neural network trained with design cases is used for selecting a bearing type in the first step. Then the subtype of the bearing is selected using the weighting method, high is a kind of multi-criteria decision making method. Finally, the types of peripheral equipments such as lubrication devices, seals and bearing housings are determined using a rule-based expert system.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.100-114
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• 2007

Decision makers in the service industry must effectively cope with queuing problems, service capacity optimization, service efficiency and service quality problems. This study proposes a computer simulation-enabled MCDM framework that integrates computer simulation analysis, Taguchi method, expert opinion and multiple criteria decision making (MCDM) to assist decision makers in coping with decision problems. In this framework, Taguchi method is adopted to reduce the time required for the simulation experiment. Computer simulation analysis is adopted to obtain useful information for rapid decision-making without interrupting actual production. MCDM is used to select the optimal alternative. The illustrative result is extremely promising.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.1-16
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• 1995

The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using the method, valid nonlinearity in human decision behavior can be successfully detected. Two linear(statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors(valid nonlinearity) that contribute to the expert's predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models cab be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.

• The Journal of Korean Physical Therapy
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• v.24 no.3
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• pp.171-180
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• 2012

Purpose: The purpose of this study was to investigate the clinical decision making (CDM) development process throughout the comparison between novice and expert physical therapist as well as develop a CDM model for physical therapists under the fee-for-service (FFS) and physicians' prescriptions. Methods: Purposive sampling techniques were used to select 10 clinical physical therapists paired into five groups (each pair consisted of 1 novice and 1 expert physical therapist). The coding schemes were extracted from interviews and through within- and across-case analyses, cases were summarized. The reliability of coding schemes was confirmed by checking of case summaries by the participants. Results: Novice and expert physical therapists were influenced by two themes, internalized theme and external forces or information. Novice clinicians depended more on external forces or information. Although clinicians should care patients under the FFS and physician's prescription, expert clinicians were more likely to rely on internalized knowledge. Conclusion: The findings of the present study may be used by educators or association officials enhance CDM abilities and knowledge pools of student or novices as well as develop a guide to suitable novices or students under the specific context limiting the development of their CDM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.1-7
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• 2011

Most of expert systems use the text-oriented knowledge bases. However, knowledge management using the knowledge bases is considered as a huge burden to the knowledge workers because it includes some troublesome works. It includes chasing and/or checking activities on Consistency, Redundancy, Circulation, and Refinement of the knowledge. In those cases, we consider that they could reduce the burdens by using relational database management systems-based knowledge management infrastructure and convert the knowledge into one of easy forms human can understand. Furthermore they could concentrate on the knowledge itself with the support of the systems. To meet the expectations, in this study, we have tried to develop a general-purposed knowledge conversion tool for expert systems. Especially, this study is focused on the knowledge conversions among text-oriented knowledge base, relational database knowledge base, and decision tree.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.319-326
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• 2000

Expert systems using uncertain and ambiguous knowledge are not of the recent interests about uncertainty problem for performing inference similar to the decision making of a human expert. Human factors on rule-based systems often involve uncertain information. Expert systems had been used the methods of conflict resolution in a rule conflict situation, but this methods not properly solved the rule conflict. If a human expert appends a new rule to an original rule base, the rule base rightly causes a rule conflict. In this paper, the problem of rule conflict is regarded as one in which uncertainty of information is fundamentally involved. In the reduction of problem with uncertainty, we propose an enhanced rule ordering method, which improve the rule ordering method using Dempster-Shafer theory. We also propose a combinative index, which involve human factors of experts decision making.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.103-110
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• 2007

An efficient operational strategy using expert system for metro water supply systems in case of emergency situations is developed in this study. The emergency situations of the water supply systems are classified into three categories : pipeline system accident, machinery and electric facility accident and water quality accident. A PC-based expert system is developed using CLIPS for Seoul metro water supply system, Phase 1 & 2 system and Phase 3 & 4 system. Broad professional knowledges and experiences from the experts in the water supply systems have been collected systematically to construct the knowledge base. Decision-making in case of an emergency is based upon the professional knowledge so that a rational and efficient operational management can be available even in the absence of experienced expert. Especially the expert model developed in this study also provides a guide for pumping operation in case of pipeline accident to confirm that the proper pressure to all nodes in the system is supplied. The pipe network simulator KYPIPE has been consecutively executed by trial and error fashion for each pipeline in the system. The results from KYPIPE were included in the knowledge base to supplement the knowledge of the field engineers.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.117-135
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• 2004

Since human decision making behavior is likely to follow nonlinear strategy, it is conjectured that the human decision making behavior can be modeled better by nonlinear models than by linear models. All that linear models can do is to approximate rather than model the decision behavior. This study attempts to test this conjecture by analyzing human decision making behavior and combining the results of the analysis with predictive performance of both linear models and nonlinear models. In this way, this study can examine the relationship between the predictive performance of models and the existence of valid nonlinear strategy in decision making behavior. This study finds that the existence of nonlinear strategy in decision making behavior is highly correlated with the validity of the decision (or the human experts). The second finding concerns the significant correlations between the model performance and the existence of valid nonlinear strategy which is detected by Lens Model. The third finding is that as stronger the valid nonlinear strategy becomes, the better nonlinear models predict significantly than linear models. The results of this study bring an important concept, validity of nonlinear strategy, to modeling human experts. The inclusion of the concept indicates that the prior analysis of human judgement may lead to the selection of proper modeling algorithm. In addition, lens Model Analysis is proved to be useful in examining the valid nonlinearity in human decision behavior.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.83-93
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• 2010

In this paper, we propose a new expert system for close combat in military war game model for training. Simulation logic for damage assesment is one of the main simulation functions in military war game. In Changcho 21's model which is the war game model for Republic of Korea Army corps and division, the main function of close combat's damage assessment has not been calculated by Changcho 21's model, but by COBRA which was made by US Army and has been the expert system for close combat. Results which were calculated in COBRA were sent to Changcho 21's model through a cable network. And Changcho 21's model finally calculated the value of damage assessment with the results. In this paper, we develop an new expert system for close combat using decision tree. The experimental results show that the proposed expert system has similar performance to COBRA and has less computing complexity. And it can substitute for COBRA and be applicable to battlefield.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1074-1077
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• 1993

The objective of this paper is to develop a fuzzy logic based decision-making system to detect low current faults using multiple detection algorithms. This fuzzy system utilizes a fuzzy expert model which executes an operation without complicated mathematical models. This fuzzy system decides the performance weights of the detection algorithms. The weights and the turnouts of the detection algorithms discriminate faults from normal events. This system can also be a generic group decision-making tool for other areas of power system protection.