• 대한산업공학회지
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• 제28권3호
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• pp.331-343
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• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• 정보처리학회논문지D
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• 제15D권1호
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• pp.107-112
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• 2008

레거시소프트웨어시스템을 유지 및 보수하는 작업은 소프트웨어 공학 분야에서 중요한 화두이다. 그리고 유지 및 보수 과정에 있어 회귀 테스트는 소프트웨어의 변경에 따른 기능적 동작이 올바른지 확인한다. 그러나 기존의 회귀 테스트는 대부분 시스템 레벨에서 접근이 되었으며 단위테스트 레벨에서는 준비된 테스트 케이스가 없어서 적용이 어려웠다. 본 논문에서는 단위테스트 케이스들을 구현하고 자산화하기 위해서 기존의 레거시소프트웨어시스템을 재구성하는 기법을 제안한다. 그리고 이를 실제 개발 과제의 특정 모듈에 적용하고 그 테스트 커버리지 결과를 분석하였다. 향후 본 논문에서 제시하는 방안을 기반으로 재구성 자동화 기법 및 테스트 케이스 자동화 생성에 대한 연구가 지속된다면 레거시소프트웨어시스템의 유지 및 보수에 큰 발전을 기대할 수 있을 것이다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권5호
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• pp.353-360
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• 2009

한번 개발된 소프트웨어는 긴 수명을 가지며, 결함이나 사용자 요구사항의 변경, 환경의 변화등의 원인으로 인한 수많은 모듈의 수정을 통해 진화한다. 소프트웨어의 진화와 더불어, 이러한 많은 모듈의 추가와 수정에 의한 소프트웨어의 다양한 버전의 생성은 소프트웨어 품질 향상을 가로막는 주요 요인중의 하나이다. 한편 회귀테스트는 이러한 소프트웨어의 변경에 따른 초점을 둔 테스트이지만, 소프트웨어가 여러 버전을 거쳐 진화함에 따라 많은 수의 테스트 케이스의 추가 및 수정과 더불어 테스트 케이스의 전체 숫자가 급격히 증가함에 따라 비용이 많이 들기에 쉽게 수행하지 못하는 테스트이다. 이에 테스트케이스 우선순위화 기법이 등장하여 회귀테스트의 수행을 지원하였다. 그러나, 테스트 케이스 우선순위화기법에서 사용되는 우선순위의 기준이 가치중립적이거나 가치를 고려하더라도 특정한 단일 요인만을 고려하여 그 활용이 제한적이었다. 논문에서, 우리는 비용과 결함 심각도에 기반한 히스토리컬 가치 기반의 접근법을 제시하며, 이는 기존의 비용 인식 테스트 케이스 우선순위화 기법에서의 현재의 비용 및 결함 심각도를 예측하기 위하여 히스토리컬 정보를 사용하는 접근법이다. 본 논문의 공헌으로서, 제안된 접근법은 테스트의 이해관계자들이 어떻게 히스토리컬 가치가 가치의 관점에서 테스트 효과성의 향상을 위해 사용되고 있는가를 알 수 있도록 돕는 다는데 있다. 결과적으로, 회귀 테스트를 수행하는 소프트웨어 테스터들은 그들의 테스트 케이스를 보다 더 효과적으로 우선순위 화할 수 있기에 그들의 테스트를 통한 테스트 효과성은 향상될 수 있다.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.95-103
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• 1998

회귀모형의 선형성을 검정하는 방법으로서 Azzalini와 Bowman은 회귀모형의 오차항이 정규분포를 따른다는 가정하에서 커널회귀추정량을 이용한 유사우도비 검정이라는 비모수적 방법을 제안하였다. 붓스트랩(bootstrap)기법을 도입하여 그들의 검정방법을 변형한 커널붓스트랩검정이라는 새로운 검정법을 제시하고 모의실험을 통해 검정력을 살펴보았다. 제안된 방법은 오차항의 분포가 정규분포가 아닌 경우에도 적용이 가능하였다.

• 보건행정학회지
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• 제13권3호
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• pp.21-34
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• 2003

Patient revisit to used hospital is a key factor in determining a health care organization's competitive advantage and survival. This article examines the relationship between customer's satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient's revisit. The main findings indicate that the important factors on outpatient's revisit are physician's kindness, nurse's skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient's age. Among these ones, physician's kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient's group under the constraint on the hospital's time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.595-596
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• 2008

we present a technique of power estimation for LCD panels using regression model. We extract and model two different power sources in LCD panels, those are gray-scale power and additional power of parasitic capacitors for charge sharing effect with inversion scheme. To model the power source, we use curve fitting and approximation. The results of proposed technique show that an average accuracy of estimation is above 98%.

• 전기학회논문지
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• 제61권11호
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• pp.1682-1688
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• 2012

This paper introduces a GP(Genetic Programming) based robust technique for temperature compensation in short-range prediction. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, because forecast models do not reliably determine weather conditions. Most of MOS use a linear regression to compensate a prediction model, therefore it is hard to manage an irregular nature of prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days temperatures in Korean regions. This method is then compared to the UM model and has shown superior results. The training period of 2007-2009 summer is used, and the data of 2010 summer is adopted for verification.

• The Journal of Asian Finance, Economics and Business
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• 제9권3호
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• pp.229-234
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• 2022

The study aims to investigate whether Musharakah management has an impact on Chinese and Malaysian business partnerships. To estimate the relationship between Musharakah and the Sino-Malaysian partnership, this study uses a panel econometric technique namely pooled ordinary least squares. Ordinary Least Squares regression (OLS) is a common technique for estimating coefficients of linear regression equations which describe the relationship between one or more independent quantitative variables and a dependent variable. Data was retrieved from the annual reports (from 2009 to 2019) of non-financial firms listed on the stock exchange of China and Malaysia. Four partnership measures (i.e., Musharakah, Mudarabah, Tawuruq, and Kafalah) were used to estimate the impact of Musharakah on the Sino-Malaysian partnership. Empirical results reveal that Musharakah and Mudarabah are positively related to Kafalah but the relationship is statistically insignificant. Alternatively, Musharakah is positively and significantly related to Mudarabah. Musharakah and Mudarabah have a positive but insignificant relationship. The findings of this study suggest that management of partnership has a positive impact on firm partnership. Furthermore, it supports the hypothesis that improving partnership enhances Musharakah, which has a positive impact on the firm's partnership.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1161-1164
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• 2005

In this paper, we proposed the technique to estimate the life cycle of Internet content services based on the logistic regression model. In this paper, to define parameters of Internet contents estimating life cycle by logistic regression model, we used market size, traffic amount, page view and session-visit number as the parameters of Internet contents estimating life cycle by logistic regression model. In this paper, to compare the performance of our proposed scheme, we estimated life cycle for the download services of bell sound & character contents in mobile network. As a result, using our proposed logistic regression, we were able to estimate exactly the life cycle of the download services of bell sound & character contents.

• 한국군사과학기술학회지
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• 제16권5호
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.