• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.142-148
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• 2004

In this paper, we propose the color fuzzy decision algorithm to face segmentation in a color image. Our algorithm can segment without the user's interaction by fuzzy decision marking. And it removes small parts such as a noise using wavelet morphology in the image obtained by applying the fuzzy decision algorithm. Also, it merges and chooses the face region in each quantization image through rough sets. This video object division algorithm is shown to be superior to a conventional algorithm.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1153-1166
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• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.87-95
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• 2015

In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets). We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes. The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity. The emergence and construction of granular models of type-2 and type-n (in general) is discussed. It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.375-383
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• 1999

사용자가 최소의 이해와 수정 노력으로 적합한 컴포넌트를 선택할수 있는 방안이 요구된다, 따라서 본 논문에서는 컴포넌트의 재사용도 측정을 위한 혼합적 척도를 제안한다. 현업에서의 연구와 경험을 통해서 증명된 객관성 있는 척도들을 측정인자로 설정한다. 러프집합을 이용하여 각측정인자들이 컴포넌트 재상요에 미치는 영향의 정도를 평가하고 각 측정인자들의 상대적 중요도를 구한다, Sugeno의 퍼지적분을 이용하여 측정인자들의 중요도와 측정값들을 종합함으로써 컴포넌트들의 재사용도를 측정한다. 마지막으로 제안된 ordinal scale과 ratio scale에 따름을 보여준다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.11-15
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• 1998

일반적으로 러프집합은 지식베이스 시스템에서 근사공간을 이용한 불확실한 데이터의 분류, 추론 및 의사결정 등에 사용된다. 지식베이스 시스템의 데이터 중에서 연속적인 구간 특성을 갖는 정량적 속성값이 불연속적일 때 중복 또는 불일치 등의 불확실성이 발생된다. 본 논문은 러프집합의 정량적 속성값들의 정성적 속성으로 변환시킬 때 식별 불가능 영역에 있는 정량적 속성값들을 명확한 경계를 갖는 보조구간으로 분리하여 불확실성을 제거함으로써 러프집합의 분류능력을 향상시키는 방법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.311-314
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• 2000

컴포넌트의 재사용도 측정은 컴포넌트가 재사용되는 시점에서 컴포넌트의 이해와 적용을 위해 소요되는 노력의 정도를 측정한다. 여러 연구들이 컴포넌트의 재사용도 측정 방법을 제시하였지만 측정 속성(척도)들과 컴포넌트들의 삽입 삭제의 어려움, 가정된 지식의 요구, 각 측정 속성들에 대한 중요도 제시의 부재 등의 문제점들이 있다. 따라서, 본 연구에서는 이러한 문제점들을 해결하기 위해서 실제로 재사용되고 있는 객체지향 컴포넌트들과 여러 연구에서 제시되고 있는 메트릭스들을 종합하고, 퍼지 적분과 러프 집합을 이용하여 클래스의 재사용도를 측정한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.145-149
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• 1998

소프트웨어의 유지보수면에서, 재사용이 매우 중요시되고 있는 가운데, 사용자가 최소 노력으로 필요한 컴포넌트들을 선택 및 유사 컴포넌트들을 평가할 수 있는 방법이 필요하다. 따라서, 본 논문에서는 현업에서의 많은 연구와 실험을 통해서 그 타당성이 검증된 척도들을 측정 인자로 설정, 여기에 러프 집합으로써 각 측정 인자들의 중요도를 측정하고, 이러한 측정값들을 Sugeno의 퍼지 적분으로써 종합하여 컴포넌트의 재사용 가능도를 평가하여 재사용이 용이한 컴포넌트 순으로 사용자에게 제공할 수 있는 재사용 가능도 평가 방법을 제안한다.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.