• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.195-200
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• 2014

Typical methods of active contour model for object contour extraction are snake and level. Snake is usually faster than level set, but has limitation to compute topology of objects. Level set on the other hand is slower but good at it. In this paper, a new object contour extraction algorithm to use advantage of each is proposed. The algorithm is composed of two main steps. In the first step, snake is used to extract the rough contour and then in the second step, level set is applied to extract the complex contour exactly. 5 binary images and 2 natural images with different contours are simulated by a proposed algorithm. It is shown that speed is reduced and contour is better extracted.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.22 no.3
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• pp.1-7
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• 1986

This is a basic study of further investigating the effect of oceanographic conditons, such as bottom profile, currents, and temperature, to the fish migrating course around the set-net. The survey was held at Dojang Po, southern part of Geoje Island, from July to October in 1985. There was a sea valley of which depth was 20 to 40 meters around the set-net. Near the bottom of that sea valley, there was different current pattern to the upper layer. In the sea calm condition of July and October, the vertical profiles of current and water temperature were simple. But in rough condition of September, they were complicated because of wind tuburance.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• Proceedings of the Korea Concrete Institute Conference
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• 1993.04a
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• pp.59-64
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• 1993

The flexural and horizontal shear behavior of overlaid concrete slabs is investigated in the present study. An experimental program was set up and several series of overlaid concrete slabs have been tested to study the effect of different surface preparation ; and dowels between old slab and overlay under service load. The present study indicates that the overlaid concrete slabs behave integrally with existing bottom slabs up to yield range for rough and doweled joints.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.327-330
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• 1997

패턴인식중에서 가장 기본적인 문제인 판별문제를 대상으로 러프집합을 이용한 판별분석을 행하는 신경망의 학습알고리즘을 제안한다. 어떤군에 속할 것인가의 경계영역을 명확히 하는 것을 목적으로 한다. 2군 판별의 문제를 각 데이터가 각 군에 속한 정도를 표현하는 소속함수(membership function)을 이용하며, 경계영역에 대한 문제는 소속함수를 구간치 함수로 확장하여 가능성과 필연성을 동시에 표현할 수 있는 학습 알고리즘을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.145-148
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• 2004

규칙 기반 추론 시스템에서 규칙의 속성 감축은 다양한 방법으로 제안되어 왔다. 규칙의 속성 감축은 퍼지 추론 시스템을 구현하는데 있어서 처리 시간을 단축시킬 수 있으나 규칙의 종속성 및 상관성을 고려하지 않을 경우 예상하지 못한 추론 결과를 얻을 수 있다. 따라서, 본 논문에서는 복합속성을 가진 규칙의 속성 감축과 상관성을 고려하기 위하여 러프집합의 특성 중 식별가능 행렬과 식별가능 함수를 이용하였다. 그리고 속성 감축에 사용된 규칙은 복합속성(composite attribute)을 가지는 감성 데이터를 이용하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.96-105
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• 1998

소프트웨어 재사용 분야에 있어서 우선적으로 연구되어야할 부분은 소프트웨어 부품의 품질 보증에 관한 연구이다. 그러나 기존의 연구들은 사용자 요구의 복잡, 다양화와 소프트웨어 복잡도증가등과 같은 변화하는 환경에 능동적으로 대처하지 못한다. 따라서, 본 논문에서는 재사용되고 있는 부품들, 정량적인 척도을과 분류 기준들을 이용하여 변화하는 환경에 능동적으로 대처할 수 있는 적응성이 있는 재사용성 결정 알고리즘 생성 모델을 제안한다. 이 모델은 적응성 있는 재사용 결정 알고리즘을 찾기 위해서 데이터의 숨겨진 패턴들을 발견하는 효율적인 알고리즘을 제고？는 러프 집합 이론을 이용한다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1547-1548
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• 2008

본 논문에서는 유동적 비정상 시계열의 패턴과 규칙성을 잘 반영할 수 있는 최적의 차분 간격 후보군을 이용한 TS 퍼지 모델로 다중 퍼지 모델을 구현하였고, 각각의 모델들의 예측 특성을 반영하기 위하여 러프집합을 이용한 모델선택법을 제안하였다. 또한 TS퍼지 모델의 파라미터 식별에는 적절한 오차보정 메커니즘을 추가하여 더욱 예측 성능을 향상 시켰다.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.3-6
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• 2000

In this paper, ‘gesture’ is discussed as a means of human-friendly communication between man and machine. We classify various gestures into two Categories: ‘contact based’ and ‘non-contact based’ Each method is reviewed and some real applications are introduced. Also, key design issues of the method are addressed and some contributions of soft-computing techniques, such as fuzzy logic, artificial neural networks (ANN), rough set theory and evolutionary computation, are discussed.