• Asia pacific journal of information systems
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• v.2 no.1
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• pp.35-56
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• 1992

This paper proposes a synergism of neural networks (NN) and knowledge-based decision support system (KBDSS) to effectively design an intelligent strategic planning system. Since conventional KBDSS becomes inoperative partially or totally when problem deviates slightly from the expected problem-domain, a new DSS concept is needed for designing an effective strategic planning system, where strategic planning environment is usually turbulent and consistently changing. In line with this idea, this paper developes a NN-based DSS, named ConDSS, incorporating the generalization property of NN into its knowledge base. The proposed ConDSS was extensively operated in an experimentally designed environment with three models: BCG matrix, Growth/Gain matrix, and GE matrix. The results proved very promising when encountered with unforeseen situations in comparisons with conventional KBDSS.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.213-217
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• 1999

An expert system is developed for rational and efficient design of multi-component injection molding which is a fairly new manufacturing technique to produce plastic parts by injecting two or more materials sequentially using multiple injection units in a single machine into a single rotary mold. The knowledge base used in the present design system is primarily composed of two parts ; knowledge from domain expert and knowledge from CAE analysis. The present expert system has hour main modules ; general design guidelines for injection molding specific guidelines for multi-component injection molding redesign guidelines from the result of the CAE analysis and finally troubleshooting for multi-component injection molding. To show the validity of the present design methodology two shop floor design problems were tested ; design and fabrication of timing belt cover and power window's assist knob by using multi-component injection molding.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.609-610
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• 2006

This paper introduces a methodology for construction of ontology-based product knowledge Map. For CPC(Collaborative Product Commerce) environment, engineering application of ontology has been studied . However, there are no generic and comprehensive methodologies for ontology construction yet because of such problems: dependency on experience of ontologist and domain experts and insufficiency of detail stages or rules. To solve those problems, we propose a methodology to construct ontology from engineering documents in semi-automatic. We use middle-out strategy and term's axioms, sub-definitions, to build ontology map. 5-turple ontology structure, semantic network and First order logic (FOL) are used for ontology definition in this study.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.793-795
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• 2005

지식 시스템(knowledge system)을 구축한다는 것은 구조적인 지식(structured knowledge)과 추론 모델(reasoning model)을 개발하기 위한 활동을 모델링하는 것을 말한다. 특별히 모델을 재사용한다는 것은 새로운 응용(application)을 만드는 데 있어서 비용과 시간을 감소시켜 준다. 서로 다른 응용과 영역(domain)에서 지식 구성요소를 재사용 한다는 것은 전문가 지식의 습득과 추론 과정을 정확하게 기술하는데 도움을 주게 된다. 본 연구에서는 문제해결방법 재사용에 대한 기존의 몇 가지 방법들을 함께 사용함에 의해서 급성복통과 관련된 질환 진단 태스크를 모델링할 수 있는 진단시스템을 제안하여 기존 시스템과 비교했을 때 지식 획득율을 향상시키게 되었다.

• Journal of the Korea Society for Simulation
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• v.10 no.4
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• pp.51-64
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• 2001

It is quite necessary that an organization's information network should be equipped with a proper security system based on its scale and importance. One of the effective methods is to use the simulation model for deciding which security policy and mechanism is appropriate for the complex network. Our goal is to build a foundation of knowledge-based modeling and simulation environment for the network security. With this environment, users can construct the abstracted model of security mechanisms, apply various security policies, and quantitatively analyze their security performance against possible attacks. In this study, we considered security domain from several points of view and implemented the models based on a systematic modeling approach. We enabled the model to include knowledge in modular fashion and provided well-defined guidelines for transforming security policy to concrete rule set.

• IE interfaces
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• v.20 no.4
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• pp.488-497
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• 2007

In order to provide appropriate decision supports in medical domain, it is required that clinical knowledge should be implemented in a computable form and integrated with hospital information systems. Healthcare organizations are increasingly adopting tools that provide decision support functions to improve patient outcomes and reduce medical errors. This paper proposes a process centric clinical decision support system based on medical knowledge. The proposed system consists of three major parts - CPG (Clinical Practice Guideline) repository, service pool, and decision support module. The decision support module interprets knowledge base generated by the CPG and service part and then generates a personalized and patient centered clinical process satisfying specific requirements of an individual patient during the entire treatment in hospitals. The proposed system helps health professionals to select appropriate clinical procedures according to the circumstances of each patient resulting in improving the quality of care and reducing medical errors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.74-82
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• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.1
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• pp.16-21
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• 1990

A Knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. Expert system has the potential to solve a wide range of problems which require knowledge about the problem rather than a purely analytical approach. This papaer presents the application of knowledge based expert system to power system fault diagnosis. The contents of expert system develpped in this paper is judgement of fault section from a given alarm sets and production of all possible hypothesis for the single fault. Both relay failures and circuit breaker failures are considered simultaneously. Although many types of relay are used in actual system, experts recognize ones as several typical signals corresponding to the fault types. Therefore relays are classified into several types. The expert system is written in an artificial intelligence language "PROLOG" . Best-first search method is used for problem solving. Both forward chaining and backward chaining schemes are used in reasoning process. The application to a part of actual power system proves the availability of the developed expert system.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.93-104
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• 1997

The major economic health problem of dairy cattle is mastitis which can affect 10 to 50% of cow-quarters. This health problem is difficult for many dairy farmers and health advisors to understand, diagnose and control. Without special laboratory testing, most mastitis is overlooked. Estimates of annual mastitis cast per cow vary from $50 to $200. For the nearly 9 million cows in the United States, annual loss to the dairy industry amounts to over one billion. A knowledge-based decision aid has been developed to evaluate mastitis data retrieved electronically from two of nine U. S. regional dairy records processing centers. Heuristic rules to diagnose herd mastitis problems were collected and incorporated into the system from various domain experts. This system information. It allows users to select mastitis control schemes with various degrees of aggressiveness and teaches commonly accepted mastitis control practices.

• Knowledge Management Research
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• v.13 no.1
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• pp.41-52
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• 2012

Service innovation has been considered to improve the value of enterprises, so that various research issues have been studied on innovating the services. Thus, in order to increase understandability of new service innovation mechanism, this study surveys the existing research results on service innovation factors, e.g., success factors, process, and performance. Consequently, we have found that service innovation can be reflected to service quality and value by interactive process between customers and enterprises. Also, such interactions are based on internal success factors (e.g., innovation strategy of organizations, domain knowledge, collaboration) and external success factors (e.g., customer participation, partnership).