• Journal of Information Processing Systems
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• 제7권3호
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• pp.397-412
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• 2011

Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.

• 한국멀티미디어학회논문지
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• 제3권5호
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• pp.550-556
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• 2000

러프집합에서는 식별불능관계와 근사공간 개념을 이용해서 불완전 정보로부터 최적화된 결정규칙을 유도하게 된다. 그러나, 처리 하고자 하는 정보에 정량적이거나 중복 또는 누락된 데이터가 포함된 경우에는 오류가 발생될 수 있으므로, 이러한 데이터들을 제거하거나 최소화시키는 방법이 필요하다. 정보처리 분야에서 불확실성이나 정보의 양을 측정하는데 사용되고 있는 엔트로피는 러프 관계 데이터베이스의 불완전 정보를 제거하는데 사용되었다. 그러나, 정보시스템에 포함될 수 있는 불완전 정보를 모두 다루지는 못하였다. 본 논문에서는 정보시스템의 조건속성과 결정속성에 포함될 수 있는 불완전 정보를 제거하기 위한 정보 이론적 척도로서 러프집합을 이용한 객체관계 엔트로피와 속성관계 엔트로피를 제시한다.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.881-886
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• 2007

퍼지 추론은 애매한 지식을 효과적으로 처리할 수 있는 장점이 있다. 그러나 퍼지규칙의 연관속성은 규칙을 과다하게 생성하기 때문에 유용하고 중요한 규칙을 결정하는데 여러 가지 문제점이 있다. 본 논문에서는 러프집합을 적용하여 규칙간의 상관성을 고려하여 불필요한 속성을 제거하고, 퍼지 상대농도를 이용하여 추론결과의 정확성을 유지하면서 규칙의 수를 최소화 하는 방법을 제안한다. 실험결과 규칙의 개수는 감소되었으며 추론 결과가 감축하기 이전과 일치하고 규칙간의 중복성이 제거되는 것을 확인하였다.

• Journal of Mechanical Science and Technology
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• 제19권12호
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• pp.2213-2223
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• 2005

In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.202-209
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• 2002

본 논문은 제어 시스템에서 규칙기반과 데이터 마이닝에서의 분류규칙의 명료함에 대해 다룬다. 대용량의 데이터로부터 유용한 정보를 얻어내는 데이터 마이닝은 중요한 이슈가 되고 있다. 인공지능에 기반을 둔 데이터 마이닝 분류기법에는 신경망, 의사결정나무 등 여러가지가 있지만 그 결과는 명확하고 이해하기 쉽고 분류규칙이 간단명료해야 한다. 러프집합이론은 불충분하고 비일관적인 데이터로부터 의미있는 지식을 추출하는데 효과적인 기법이고, 다양한 속성들을 효과적으로 사용함으로써 분류와 근사화에 대한 좋은 해법을 제시한다. 본 논문에서는 러프집합이론의 근사화를 이용하여 알갱이 속에 숨겨져 있는 지식들을 찾아내는데 있어 효과적인 접근을 하였으며, 최상위 레벨에 코어를 적용하여 계층적 분류를 함으로써 대량의 데이터를 효율적으로 처리할 수 있도록 하였다. 제안된 분류방법은 정보시스템의 해석을 용이하게 하고 최소의 분류규칙을 만든다.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.400-416
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• 2017

Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.463-470
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• 2008

본 논문에서는 로봇이 획득한 정보가 내포하는 '장애물의 존재성'과 '장애물 사이의 연결성'이 위치인식에 효과적으로 사용 될 수 있음을 러프집합을 이용하여 확인한다. 그리고 두 속성을 이용하여 위치인식의 애매성을 줄이고, 신뢰할 수 없는 환경 정보에서도 로봇의 위치 판단이 가능한 위치인식 방법을 제안한다. 로봇이 환경 정보를 축소하여 구성한 지도만으로도 목표점까지 이동하는 것을 모의실험을 통해 확인하였다.

• 전기학회논문지
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• 제61권8호
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• pp.1164-1171
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• 2012

Recently, machine olfactory systems as an artificial substitute of the human olfactory system are being studied actively because they can scent dangerous gases and identify the type of gases in contamination areas instead of the human. In this paper, we present an effective design method for the gas identification system. Even though dimensionality reduction is the very important part, in pattern analysis, We handled effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, we constructed the hierarchical rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.315-326
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• 2007

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 a dynamic anomaly detection scheme 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 with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.