• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.20-30
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• 2005

In the anaerobe identification, when we develop the clinical decision support system for department of laboratory medicine, we must consider expression of an incomplete knowledge structure and addition of an evolving knowledge based on an expert's informal and heuristic knowledge is very complicated work flow. In the present study, we developed the system for anaerobe identification to advise on identification of unknown bacillus using knowledge base and inference engine. In the future, we are planning to develop the clinical decision support system for the whole bacteria not only an anaerobe but also aerobe to offer an expert's static and dynamic knowledge.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.309-314
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• 2006

As a part of study to develop a building design support system for architectural designer's design decision process, the drawbacks of fuzzy system-based design decision model suggested in the previous study have been made up for. A method of logically taking the characteristic and impact of design elements into designing HVAC type was suggested. For purpose of mirroring HAVC designer's working process, a model was developed using AHP, a way of decision making process in the inference process of optimum design values.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.46-55
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• 2005

This paper proposes a new method for choice of the best transmission system expansion plan on the side of highest satisfaction level of decision maker using fuzzy integer programming. The proposed method considers the permissibility and ambiguity of the investment budget (economics) for constructing the new transmission lines and the delivery marginal rate (reliability criteria) of the system by modeling the transmission expansion problem as a fuzzy integer programming one. It solves the optimal strategy (reasonable as well as flexible) using a fuzzy set theory-based on branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Under no or only a very small database for the evaluation of reliability indices, the proposed technique provides the decision maker with a valuable and practical tool to solve the transmission expansion problem considering problem uncertainties. Test results on the 63-bus test system show that the proposed method is practical and efficiently applicable to transmission expansion planning.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.267-277
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• 1992

본 논문은 다목적선형계획법을 위한 Decision Support System ASEOV-VIM을 소개하고 있다. ASEOV-VIM에는 1) efficient solution set전체에 대한 개괄과 search direction을 제새하는 ASEOV; 2) decision maker의 preference information을 도출해내는 VIM; 3) 위 두 부분간의 interface로 활용되는 Mediator등 3개의 하부시스템이 있다. ASEOV-VIM은 TURBO-C를 이용하여 personal computer에 구현되었다.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.47-66
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• 2008

This study is aimed at proposing a new type of ubiquitous decision support system (u-DSS) portal which is embedded with two important mechanisms like an intelligent ontology management module (i-OMM) and multi-agent coordination mechanism (MACM). The proposed portal provides timely decision support to the involved decision entities (represented as agents) by taking advantage of the two mechanisms embedded on the portal. The most important virtue of the proposed portal is that it can resolve two problems such as semantic discordance and data confliction which are occurring very often in an ubiquitous computing environment. Frequent requests of revising the already established decision information due to the rapid changes in decision entities' requirements require the extremely flexible and intelligent u-DSS vehicle like theproposed mechanism. In this sense, the i-OMM is designed to provide support to solving the semantic discordance in the way that the i-OMM virtually integrates ontology view (IOV) to integrate heterogeneous ontology among the agents engaged inubiquitous commerce situations. Then the i-OMM sends the IOV to the MACM to resolve the conflicts among the involved agents. The proposed u-DSS portal was applied to the u-fulfillment problem in which all the involved decisionagents need their own requirements to be satisfied seamlessly and timely. The experimental results show that the proposed u-DSS portal is very robust and promising in the field of u-DSS and context modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.448-455
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• 2003

A system design technique of automatic thickness measurement and defect inspection system, which measures the thickness of the ALC(Autoclaved Lightweight Concrete) block and inspects the defect on a realtime basis is proposed. The image processing system was established with a CCD camera, an image grabber, and a personal computer without using assembled measurement equipment. The image obtained by this system was analyzed by a devised algorithm, specially designed for the enhanced measurement accuracy. For the realization of the proposed algorithm, the preprocessing method that can be applied to overcome uneven lighting environment, an enhanced edge decision method using 8 edge-pairs with irregular and rough surface, the unit length decision method in uneven condition with rocking objects, and the curvature calibration method of camera using a constructed grid are developed. The experimental results, show that the required measurement accuracy specification is sufficiently satisfied using our proposed method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3124-3134
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• 1993

Diagnosis is composed of two different but interrelated steps ; retrieving the sensory responses f the system and reasoning the state of the system through the given sensor data. This paper explains the probabilistic nature of reasoning involved in the diagnosis when the uncertainties are inevitably included in experts' diagnostic decision making. Uncertainties in decision making are experts' personal experiences, preferences, and system's coherent characteristics. In order to ensure a consistent decision based on the same responses from the system, expert system technology is adopted with the Bayesian reasoning scheme.