• 대한상한금궤의학회지
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• 제8권1호
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• pp.67-85
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• 2016

Objectives: In this paper, two cases which showed the meaningful results on the patients' chief complaints were analyzed. The patients were treated with the Mahwang-Haengin-Gamcho-seokgo-tang herbal medication based on Shanghanlun disease pattern identification diagnostic system. Methods: The patients were diagnosed based on Shanghanlun, disease pattern identification diagnostic system. In case 1, the change of menstruation cycle was noted and pre-menstrual discomforts were measured with Menstrual Distress Questionnaire(MDQ). In case 2, Quality of life questionnaire for adult Korean asthmatics (QLQAKA) was used to estimate the quality of the patient's life. Results: All the symptoms were improved after the Mahwang-Haengin-Gamcho-seokgotang treatment. In case 1, the menstruation cycle decreased to 30 days average. MDQ score decreased 143 to 103. In case 2, the change of the QLQAKA score as 1.647 average point is considered as a meaningful improvement. Conclusion: With great difference to a 'Symptom-Medicine' diagnostic system, the disease pattern identification diagnostic system seeks the pathologic pattern through the patient's whole life. More studies and multiple cases based on the diagnostic system are needed to prove this possibility later.

• 한국지능시스템학회논문지
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• 제20권1호
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• pp.48-53
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• 2010

본 논문에서는 첨단 IT(Information Technology) 기술을 전통산업인 집진기에 융합한 지능형 원격 집진기 bag 제어 시스템을 이벤트 기반으로 개발하였다. 먼저 전력선 통신을 이용하여 집진기 bag 상태 정보를 효율적으로 전송할 수 있도록 메시지 형식을 정의하였다. 그리고 집진기와 집진기 bag을 논리적으로 모델링하기 위해 자료형을 정의하였고, 이를 이용해 집진기와 집진기 bag을 XML과 객체지향 모델링 기법을 이용해 모델링하였다. 뿐만 아니라 집진기 bag의 교체시기를 정확하게 지능적으로 파악할 수 있도록 사례들을 수집하여 XML로 표현하고, 그 사례를 기반으로 현재 집진기 bag 상태를 사례기반 추론 기법을 이용하여 추론한 후 집진기 bag의 교체시기를 관리자에게 실시간으로 제시하도록 개발하였다.

• 한국건축시공학회지
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• 제12권2호
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• pp.133-141
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• 2012

As we move from the industrial age to the information age, domestic industries are changing rapidly, and rural society is also laying the foundation to make use of information technologies. Through this kind of modernization, the size of agricultural facilities has been increasing on a significant scale. But, in reality, there are many difficulties in the maintenance of agricultural facilities in proportion to their growing number. Accordingly, this research aims to solve the fundamental problems that occur with agricultural facilities in the maintenance stage. In addition, it aims to provide information on how to maintain and manage facilities for farmers. The presentation of the maintenance information was conducted using a case-based reasoning method that solves current problems based on past cases. The tool of case-based reasoning was applied to define the establishment of the base for cases, characteristic variables and maintenance measures. The effectiveness of a CBR model was examined through the case study. The use of the case-based reasoning method is judged to be effective as a tool to support the decisions of farmers regarding maintenance. When the maintenance measures derived through the CBR model are offered to farmers, the fundamental problems of maintaining agricultural facilities will be solved, and the damage to such facilities minimized.

• 재활간호학회지
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• 제17권2호
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• pp.90-96
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• 2014

Purpose: The purpose of this study was to determine the effects of simulation based training using a post-operating rehabilitation case on learning outcomes in nursing students. Methods: A quasi-experimental research design (one group pretest and posttest design) and a questionnaire for measuring learning outcomes were used in this study. The participants were 35 students in a college of nursing. Data were collected before the program and immediately after the program that applied simulation based training using a post-operating rehabilitation case consisted of 4th running and debriefing for 26 hours. With SAS 9.2 program, descriptive statistics and paired t-test were used to analyze the data. Results: There were statistically significant increases in necessity (p=.001) and performance of learning outcome (p<.001) of simulation based training using a post-operating rehabilitation case among students in a college of nursing. Conclusion: The findings of this study demonstrate that simulation based training using a post-operating rehabilitation case for nursing students may increase performance of learning outcomes on clinical reasoning and critical thinking.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 춘계학술대회 논문집
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• pp.370-374
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• 1993

This paper describes a study on the expert system based process planning of the block division process in shipbuilding. The prototype system developed deterines the block division line of the midship of crude-oil tanker. Case-based reasoning (CBR) approach relying on previous similar cases to solve the problem is applied instead of rule-based reasoning (RBR). Similar cases are retrieved from case base according to the similarity metrics between input problem and cases. The retrieved case with the highest priority is then adapted to fit to the input problem buy adaptation rules. The adapted solution is proposed as the division line for the input problem.

• Journal of information and communication convergence engineering
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• 제3권4호
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• 한국임상약학회지
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• 제32권4호
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• pp.328-335
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• 2022

Background: Case-based learning (CBL) is becoming an important approach for improving interprofessional collaboration education. Previous studies have examined learners' satisfaction with interprofessional education (IPE) in medical institutions. However, there are few studies on the implementation of university-led CBL interventions and their direct effects on learning outcomes. The aim of this study was to evaluate the effectiveness of CBL interventions on changes in the participants' perception and knowledge acquisition ability. Methods: The CBL approach consisted of team-based case-based learning, self-directed learning, and post-feedback. It was conducted as a single course for pharmacy students in their 5th year in a university setting. Changes in the participants' perceptions and self-assessments of competence levels were evaluated using survey responses. The effect of the CBL intervention on knowledge acquisition ability was directly evaluated using the exam score. Results: The majority agreed or strongly agreed that team-based case-based learning, and self-directed learning helped them to improve their knowledge and skills to a higher level and to increase the self-assessment of competency level. The average score of knowledge acquisition ability (average score of 75.0, p=0.0098) was significantly higher in the CBL intervention group than the lecture-based learning intervention group (average score of 52.0). Conclusion: The participants positively perceived that CBL intervention helped them to effectively improve their knowledge and the self-assessment of competency level. It also enhanced knowledge acquisition ability. These data, based on the survey responses, suggest that it is necessary to implement CBL interventions in a university-led single professional education.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권7호
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• pp.255-262
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• 2014

기존 논문[1]에서는 Fillmore의 Case Grammar를 기반으로 객체 추출 및 모델링 방법을 제안하였다. 이 방법은 유스케이스 추출 및 결정 방법을 고려하지 않았다. 이러한 문제를 해결하기 위해, 본 논문에서는 요구공학에서 자연어 처리를 위해 Fillmore의 의미적 방법을 채택하였다. 즉, 고객 요구사항으로부터 유스케이스를 모델링하고 추출하기 위해 Fillmore의 Case Grammar를 개선한다. 개선된 메커니즘은 구조화된 절차를 정의하고 시각적 표기법을 수행한다. 또한 유스케이스의 복잡성과 관련된 Goal 지향 요구공학(GoRE)을 기반으로 추출된 유스케이스에서 유스케이스 크기를 식별하는 유스케이스 결정 매트릭스(Use Case Decision Matrix)를 제안한다. 이 매트릭스에서 유스케이스를 우선순위화 한다. 사례연구로 은행 ATM 시스템에 적용하였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.329-337
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• 2007

Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.