• ETRI Journal
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• v.36 no.6
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• pp.1023-1031
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• 2014

Failure analysis is necessary to clarify the root cause of a failure, predict the next time a failure may occur, and improve the performance and reliability of a system. However, it is not an easy task to analyze and interpret failure data, especially for complex systems. Usually, these data are represented using many attributes, and sometimes they are inconsistent and ambiguous. In this paper, we present a scalable approach for the analysis and interpretation of failure data of high-performance computing systems. The approach employs rough sets theory (RST) for this task. The application of RST to a large publicly available set of failure data highlights the main attributes responsible for the root cause of a failure. In addition, it is used to analyze other failure characteristics, such as time between failures, repair times, workload running on a failed node, and failure category. Experimental results show the scalability of the presented approach and its ability to reveal dependencies among different failure characteristics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.117-120
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• 2005

퍼지 규칙 기반 시스템에서 분류 및 경계를 결정하기 위한 방법으로 퍼지 규칙을 학습하는 다양한 방법들이 제안되고 있다. 그리고 추론 규칙간의 상관성을 고려하여 불필요한 속성을 제거함으로써 좀 더 효율적인 추론 결과를 얻을 수 있다. 따라서 본 논문에서는 퍼지 규칙 기반 시스템에서 각 규칙에 따른 결정 테이블를 작성하고 러프집합을 이용하여 불필요한 속성을 제거하였으며 규칙의 확신도에 퍼지 네이브 베이스 이론을 적용한 추론 방법을 제안한다.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.144-147
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• 2006

A proportional intergral (PI) control strategy is commonly used for constant current and extinction angle control in a HVDC (High Voltage Direct Current) system. A PI control strategy is based on a stactic design where the gains of a PI controller are fixed. Since the response of a HVDC plant dynamically changes with variations in the operation point a PI controller performance is far from optimum. The contribution of this paper is the presentation of the design of a rough set based, fuzzy adaptive control scheme. Experimental results that compare the performance of the adaptive control and PI control schemes are also given.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.161-169
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• 2010

The requirement elicitation is the task of eliciting requirements from needs of stakeholders, and preparing for information for negotiation. However, there are methods for gathering needs, but there is no specific method for classifying them, generating information for negotiation. Therefore, To solve the problems, this paper proposes a method to classify requirement and to generate information for negotiation. The proposed methods use Kano model, statistical technique, and identifying relationship between requirements and problems within scope. Finally, we validate the proposed method on simulations, Rough Set Theory, and case study of model.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.188-193
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• 2000

This paper presents an approach to obtain a reduced neuro-fuzzy model for a plant. The Neuro-Fuzzy Network are compose of the Radial Basis Function Networks with Gausis membership and learned by using temporal back propagation. The dependency in rough set theory is used to eliminate rules. Dependency between the condition membership value of each rule in a model and the output of the plant can allow us to see how much contribution the rule is to identify the plant. While the reduced model maintains the same performance as the original one, the selection algorithm can minimize its complexity and redundancy of the structure.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.72-78
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• 2015

Alexandrov topologies are the topologies induced by relations. This paper addresses the properties of Alexandrov topologies as the extensions of strong topologies and strong cotopologies in complete residuated lattices. With the concepts of Zhang's completeness, the notions are discussed as extensions of interior and closure operators in a sense as Pawlak's the rough set theory. It is shown that interior operators are meet preserving maps and closure operators are join preserving maps in the perspective of Zhang's definition.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.157-164
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• 2015

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.257-264
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• 2014

This paper presents the core competencies diagnosis system which targeted our collegiate students in an attempt to induce the core competencies for reinforcing the learning and employment capabilities. Because these days data give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. So as to address the measurement of uncertainties from the classification of categorical data and the implementation of its analytic system, an uncertainty measure of rough entropy and information entropy is defined so that similar behaviors analysis is carried out and the clustering ability is demonstrated in the comparison with the statistical approach. Because the acquired and necessary competencies of the collegiate is deduced by way of the results of the diagnosis, i.e. common core competencies and major core competencies, they facilitate not only the collegiate life and the employment capability reinforcement but also the revitalization of employment and the adjustment to college life.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.