• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.383-385
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• 2018

기존의 한방 자가 진단 방법에서는 PCM 기반의 알고리즘을 적용시켰으나 고질적인 문제점 중의 하나인 증상 수가 급격하게 증가할 경우에는 진단 결과가 정확하게 도출되지 않는 현상이 발생한다. 이러한 문제점을 개선하는데 효율적인 퍼지 의사 결정 트리 알고리즘을 적용한다. 퍼지 의사 결정 트리는 과거의 데이터를 미리 학습시킨 후에 엔트로피에 따라 경계 값을 구한 후, 사용자가 여러 증상을 입력하면 입력된 증상에 해당되는 상위 질병 5개를 도출한다. 그리고 도출된 상위 5개의 질병과 도출된 질병의 원인과 치료하기 위한 민간요법을 제공한다. 질병과 증상에 대한 데이터베이스는 한의사가 추천한 여러 한의학 전문 서적을 기반으로 증상과 질병의 데이터베이스를 설계한 후, 한의학 전문의의 검증을 거쳐 구현하였다. 제안된 한방 자가 진단 시스템은 과거의 데이터를 바탕으로 증상을 학습함으로써 기존의 질병 진단 시스템보다 정확하고 신속한 진단 결과를 도출하는 것을 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.127-136
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• 2020

This study investigated the correlation between the participation in health-related decisions and self-care competency and the factors influencing self-care competence of patients with chronic disease. The participants were 518 patients with chronic illnesses who were admitted to a public health center. Using a structured-questionnaire, the data was collected from January 1, 2019 to 31, 2019. The data was analyzed using descriptive statistics, t-tests, ANOVA, Scheffé tests, Pearson correlation coefficients and multiple linear regression with the IBM SPSS/WIN 22.0 program. The mean scores on the participation in health-related decisions and self-care competency were 3.62±0.20 and 4.57±0.34, respectively. The main variables that distinguish significant differences between participation in healthcare decisions and self-care capabilities are gender, age, education level, diagnosis, and subjective health status. Self-care competency was positively correlated with the participation in health-related decisions (r=.66, p=.043). Factors influencing self-care competence were education (β=-2.23, p<.001), health concerns (β=0.14, p=.003), subjective health statues (β=2.57, p=.011), and age (β=-0.36, p=.048). The input variables explained .56 of self-care competence. The results of the study indicate that approaches focusing on enhancing the participation in health-related decisions could potentially improve self-care competency of patients with chronic disease.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.271-284
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• 2008

Self-healing is an approach to evaluating constraints defined in target system and to applying an appropriate strategy when violating he constrains. Today, the computing environment is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, most of the existing researches are that self-healing developers need much effort and time to analyze and model constraints. Thus, this paper proposes an automated approach to determine problem arising in external and internal system environment. The approach proposes: 1) Specifying the target system through the models created in design phase of target system. 2) Automatically creating constraints for external and internal system environment, by using the specified contents. 3) Deriving a dependency model of a component based on the created internal state rule. 4) Translating the constraints and dependency model into code evaluating behaviors of the target system, and determinating problem level. 5) Monitoring an internal and external status of system based on the level of problem determination, and applying self-healing strategy when detecting abnormal state caused in the target system. Through these, we can reduce the efforts of self-healing developers to analyze target system, and heal rapidly not only abnormal behavior of target system regarding external and internal problem, but also failure such as system break down into normal state. To evaluate the proposed approach, through video conference system, we verify an effectiveness of our approach by comparing proposed approach's self-healing activities with those of the existing approach.

• Journal of KIISE
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• v.43 no.7
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• pp.801-811
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• 2016

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

• Korean Journal of Food Science and Technology
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• v.25 no.5
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• pp.411-416
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• 1993

This study was conducted to determine optimum levels of ingredients including yeast autolyzate, first for the beef flavored soup base and then for the soy paste stew mix as a final product. Response surface methodology was used for the optimization. Beef flavored soup base with optimum sensory characteristics could be prepared with 8g/l yeast autolyzate, 0.8g/l nucleotide seasoning, and 0.09g/l succinic acid. And soy paste stew mix could be produced with 8g/l beef flavored soup base, 28g/l soy paste powder, and 16g/l corn starch. Soy paste stew prepared with optimum levels of ingredients were comparable in acceptability to that with commercial product containing beef extracts. It was suggested that sufficient beef flavor could be obtained with yeast autolyzate in such products as soy paste stew mix.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.978-989
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• 1995

현실적으로 다속성 의사결정(Multi-Attribute Decision-Making:MADM)문제들은 대안과 속성의 수가 매우 많은 것이 보통이기 때문에 이것을 모두 합리적으로 고려하여 최적의 대안을 선정한다는 것은 매우 어려운 일이다. 지금까지 연구 개발된 기존의 수리적 방법들은 주어진 문제에 대한 제약을 가하여 최적해(optimal solution)를 구할 수 있지만 의사결정자들의 입장을 정확히 반영하지 못하는 경우가 대부분이며, 이를 개선하기 위한 기존의 대화형 접근방법은 고려해야 할 대안과 속성의 수가 많아지면 대안 간의 쌍비교등을 통하여 의사결정자가 제공해야 하는 정보의 양이 기하급수적으로 증가한다는 어려움과 함께 최적해가 보장되지 못하는 문제점이 있다. 따라서, 본 연구의 목적은 대안과 속성의 수가 매우 큰 의사결정상황 하에서 의사결정자가 중요하다고 생각하는 속성의 그룹부터 단계적으로 고려해 가면서 대안의 수를 점차적으로 감소시킬 수 있는 보다 효율적인 대화형 접근방법을 구축하는데 있다.

• International Commerce and Information Review
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• v.16 no.1
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• pp.91-116
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• 2014

This study reviews the methodology for utilizing information technology by which even non-professionalists in FTAs and commodity classification area can perform the determination of commodity classification, with ease and by themselves, by means of easy utilization of the information on commodity classification and FTAs, of importing and exporting goods. This article examines the technological elements and logics, etc. which simulate the commodity classification for utilizing FTAs. To achieve this, the author has developed the technology to support the determination of commodity classification numbers by accumulating the database of examples for classification after analyzing the classification factors by each commodity item. Utilizing this Commodity Classification Determination Supporting System, users can enjoy effects of education as well as consulting. In this regards, the advantages of this system can be enumerated as followings : Firstly, self-checking on commodity classification can be performed. Secondly, time and cost for classification can be saved. Thirdly, comprehensive competitiveness will be enhanced by allowing traders to achieve the benefit of FTA preferential tariff, for they will be able to issue the Certificate of Origins on a more accurate and precise basis of commodity classification.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.285-294
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• 2021

This study analyzes the factors that determine the attitude toward the electronic wristband(smartband) to check the position of self-quarantine subjects due to COVID-19. Furthermore, I analyze the interaction of class and age among the factors that determine attitudes toward the electronic wristband. In this study, the attitude toward self-quarantine electronic wristband is analyzed as a binary logit model, focusing on class and age. As a result of the analysis, the middle class significantly agreed with the self-quarantine electronic wristband compared to the lower class, and the older the person, the more in favor. On the other hand, the interaction between the class and the age shows that the age weakens the positive effect on the attitude of the self-quarantine electronic wristband in the middle and upper middle classes. The implication of this study is that it is necessary to push for mandatory electronic wristband in areas with high proportion of high-aged people with positive attitude toward self-quarantine electronic wristband and in the same age group, the approval rate is low, so it is necessary to promote mandatory electronic wristband in areas where the vulnerable class is dense.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.18
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• pp.17-26
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• 1988

본 논문은 전략적 계획과정에 대한 지능적인 의사결정 지원 시스템의 틀(framework)을 개발하는 것을 목적으로 한다. 전문가 시스템의 능력을 의사결정 지원 시스템에 통합한 지능적인 의사결정 지원 시스템은 사업가나 의사결정자가 전략계획을 효율적이고 효과적으로 분석ㆍ평가ㆍ통제할 수 있도록 한다.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.83-102
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• 2008

Ubiquitous computing requires timely supply of contextual information in order to upgrade decision quality. In this sense, this study is aimed at proposing a multi-dynamic decision support system for highly ill-structured problems. Especially, it is very important for decision makers in the ubiquitous computing to coordinate conflicts among local goals and global goal harmoniously. The proposed Multi-Dynamic Decision Support System (MDDSS) is basically composed of both central structure and distributed structure, in which central structure supports multi objects decision making and distributed structure supports individual decision making. Its hybrid architecture consists of decision processor, multi-agent controller and intelligent knowledge management processor. Decision processor provides decision support using contexts which come from individual agents. Multi-agent controller coordinates tension among multi agents to resolve conflicts among them. Meanwhile, intelligent knowledge management processor manages knowledge to support decision making such as rules, knowledge, cases and so on. To prove the validity of the proposed MDDSS, we applied it to an u-fulfillment problem system in which many kinds of decision makers exist trying to satisfy their own objectives, and timely adjustment of action strategy is required. Therefore, the u-fulfillment problem is a highly ill-structured problem. We proved its effectiveness with the aid of multi-agent simulation comprising 60 customers and 10 vehicles under three experimental modes.