• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.101-107
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• 1976

이 글에서는 다음의 내용을 다루었다. 1. 의사결정절차의 과학화 2. 계량적인 선호순위 결정 3. 의사결정기준의 과학화

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.710-714
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• 2002

학생은 미래뿐만 아니라 현재의 소비자로서 합리적인 의사결정을 통한 소비문화가 요구된다. 하지만 학생의 소비문화와 학교의 소비교육, 가정교육은 문제점을 안고 있다. 학생들은 물건의 필요성이나 용도, 금전상황, 가정상황 등을 전체적으로 고려하여 구매하기보다는 비계획적이고, 즉흥적이다. 학교에서는 교사들이 학생의 소비행위를 한 눈에 파악하여 교육적으로 피드백하기가 쉽지 않다. 가정에서는 학생들의 소비행위에 대한 의사소통의 길이 부족하다. 이에 본 연구는 소비의사결정 5단계와 Decision-Tree의 교육적인 면을 고려하여 합리적 소비 의사결정을 위한 6단계(문제정의, 정보탐색, 대안평가, 가치확립, 구매경험, 구매평가)를 새롭게 제안하고, 소비교실, 용돈 의사결정, 설문모듈을 통해 6단계를 체험하는 시스템을 설계하였다. 제안한 모델은 교사와 학생, 학부모 모두가 참여하여 학생의 계획적이고 합리적인 소비 의사결정 능력을 신장시키는데 도움을 줄 것이다.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.9-21
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• 2023

The recent conflict between Russia and Ukraine underscores the significant of military logistics support in modern warfare. Military logistics support is intricate and specialized, and traditionally centered on the mission-level operational analysis and functional models. Nevertheless, there is currently increasing demand for military logistics support even at the engagement level, especially for resupply using unmanned transport assets. In response to the demand, this study proposes a task model of the military logistics support for engagement-level analysis that relies on the logic of ammunition resupply below the battalion level. The model employs a decisions tree to establish the priority of resupply based on variables such as the enemy's level of threat and the remaining ammunition of the supported unit. The model's feasibility is demonstrated through a combat simulation using OneSAF.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.458-461
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• 2004

오존 반응 메카니즘은 상당히 복잡하고 비선형적이기 때문에 오존 농도를 예측하는 것은 상당한 어려움을 안고 있다 따라서, 신뢰성 높은 오존 예측값을 구하는데 단일 예측모델만으로는 한계가 있으며, 이를 개선하기 위하여 다중 모델을 제안하였다. 입력데이터에 퍼지 클러스터링을 사용하여 고, 중, 저농도별로 그룹핑한 후, 그룹핑된 오존농도에 대해서 의사결정 트리를 사용하여 그룹핑된 오존데이터가 어느 정도 분류능력을 갖는지 파악하여, 오차가 가장 적은 분류특성을 갖는 그룹을 설정하여, 다중모델의 입력 데이터로 사용하여 모델을 형성하였다. 의사결정 트리를 이용하여 모델의 입력 데이터를 설정하는 것은 어떤 오존농도까지의 범위를 클래스로 설정하느냐에 따라서 모델의 성능과 고, 중, 저농도의 오존을 분류하는 성능이 달라지므로 본 논문에서는 퍼지 클러스터링을 이용하여 의사결정 트리의 클래스의 범위를 설정하여 예측 시스템을 구현하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.166-166
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• 2023

본 연구는 인지과학을 통해 해기사의 행동을 분석하였다. 인지과학은 인간의 판단이 논리적이지 않으며 모든 의사결정이 기억에 의존한다는 사실을 밝혀냈다. 그래서 본 연구는 해기사의 의사 결정에 영향을 미치는 기억 유형을 확인했다. 본 연구는 해기사의 의사결정 과정을 분석하기 위한 과학적 접근과 MASS에 적용하기 위한 의사결정 모델 구축을 위한 공학적 접근을 취하였다.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.117-126
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• 2016

We suggest the C2 modeling method to develop a simulation model for training command groups which consist of commanders and staffs. By using C2 models in constructive simulation models, combat entities or units directly receive and execute orders from a command group without mediating human role players. We also compare combat results from suggested modeling method with the results of existing models by building and implementing a simulation model with C2 models. Our analysis by comparison demonstrates advantages of suggested method to model C2 for computer assisted exercises.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.251-258
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• 2013

Recently companies are confronted with a much more sophisticated business environment than before and at the same time have to be able to adapt to rapid changes. Accordingly, the need for selecting among alternatives and managing systematic decision-making has been steadily increasing to respond to a more diverse customer needs and keep up with the fierce competition. In this study, we propose a framework that consist of problem solving procedures and techniques and knowledge structure built on processes to support strategic decision making. and discuss how to utilize simulation tools as the knowledge-based problem solving tools. In addition we discuss how to build and advance the knowledge structure to implement the proposed architecture. Management decision support systems architecture consist of three key factors. The first is Problem Solving Approach which is used as reference. The second is knowledge structure on business processes that includes standard and reference business processes. The third is simulators that are able to generate and analyze alternatives using problem solving techniques and knowledge base. In sum, the proposed framework of decision-making support systems facilitates knowledge-intensive consulting processes to promote the development and application of consulting knowledge and techniques and increase the efficiency of consulting firms and industry.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.187-194
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• 2008

Recently, the constructions of widening bridges or new bridges are often undergoing as a part of road widening because traffic volumes are rapidly increasing caused by fast-growing population and urbanization. But in general, there is no rational decision process and specification to justify the validity of the bridge widening. Moreover, there are also numerous events including various uncertainties involved in widening bridges. In this paper, therefore, a decision making model is proposed for widening bridges using decision tree based on quantitative LCC analysis considering a variety of uncertainties for the rational and practical approach to a quantitative decision making for alternatives.