• Proceedings of the KSR Conference
• /
• 2003.10b
• /
• pp.454-459
• /
• 2003

The process of determining whether, when, where and how to intervene, of deciding on optimum allocation of resources and minimizing the cost is a very complex problem: different track sections tend to behave differently under the effects of loading; decision-making processes for maintenance work are closely interrelated technically and economically; decision-making for maintenance plans is based on a large quantity of technical and economic information, extensive knowledge and above all experience. For that reason, It is considered very important to develope objective and computer-aided decision-making support system for track maintenance plan. On this paper, we reviewed ECOTRACK system and present the plan of develope decision-making support system for track maintenance appropriate to local condition.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1992.04b
• /
• pp.267-277
• /
• 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 the Korean Operations Research and Management Science Society
• /
• v.19 no.2
• /
• pp.129-151
• /
• 1994

Recently, Decision Support Systems (DSS) research has seen a more to combine Artificial Intelligence (AI) including neural network techniques with traditional DSS concepts and technologies to build an intelligent DSS or a knowledge-based DSS. This article proposes a Management Evaluation and its Support System (MESS) as a knowledge-based DSS. The management evaluation of a firm means the performance of all managerial operations is appraised by considering the situations of the firm. A neural network is used to represent the management evaluation structure as a suitable means of management knowledge representation. Finally a case study in a telecommunication corporation is presented.

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

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.28 no.1
• /
• pp.113-121
• /
• 2014

With the development of information technology, knowledge information-oriented and information systems are being rapidly paced. In addition, doctor's needs of the system that assist decision making is gradually increasing. Because the complex process of decision-making should be a lot. We propose a web based system for supporting medical treatment based on Korean medicine ontology. There are three kinds of processes. First, a pattern is decided for patient' symptoms, a formula for the pattern is selected and medicinal materials constituting the formula is added or removed. Second, a formula is decided for patient' symptoms, medicinal materials constituting the formula is added or removed. Third, a Treat method is decided for patient' symptoms, medicinal materials constituting the formula is added or removed. We have designed and implemented the clinical decision support system that supports flexible processes and necessary information and functions. The system shows the appropriate form of ontology knowledge as interrelated and provide analysis and processing, does not show simply search. The system is one of the systems utilizing ontology and a web based system that can be used in anywhere. Therefore, This system Will be useful as for doctors to make decision.

• Journal of Digital Convergence
• /
• v.10 no.10
• /
• pp.97-106
• /
• 2012

In the past, executive managers made a decision based on personal experience and knowledge due to lack of the appropriate and timely information. With the development of information systems and technologies, efficiency and productivity of business operation has been enhanced. In this study, we propose a system design and architecture blue-print related to strategic decision making support system. The proposed system consists of 3 key parts; individual business feasibility test, business portfolio feasibility test, business portfolio management. The three key parts are comprised of 11 components to generate information and knowledge based on various data input from inside and outside of firm. This system is expected to provide objective and reliable output to users. In addition, the proposed strategic decision support system would help respond to a rapidly changing business environment.

• Environmental Engineering Research
• /
• v.14 no.2
• /
• pp.111-119
• /
• 2009

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.1
• /
• pp.35-40
• /
• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Journal of the Korean Regional Science Association
• /
• v.6 no.2
• /
• pp.169-180
• /
• 1990

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2003.11a
• /
• pp.239-250
• /
• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).