• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.449-454
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• 2000

A new fuzzy logic control design algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is widely used for design of experiments in statistics and industrial engineering. The essence of the algorithm is to introduce Nth-certainty factor defined from the F-value of the ANOVA(analysis of variance) table, in order to effectively exclude the less confident rules. The proposed algorithm with multi-objective decision table(MODT) is found to be capable of the detection of inconsistency and the rule classification, reduction and modification. It is also shown that the algorithm can be successfully applied to the fuzzy controller design of an active magnetic bearing system.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.14-18
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• 1997

In this paper, we suggested and made a classifier or qualitative and quantitative analysis in urine analysis system. Input variable number and fuzzy membership function was made from determination of standard sample, and the fuzzy rules were determined by the analysis of spectroscopic properties of pads in strip. Fuzzy classifier used in urine analysis system was evaluated or the standard samples in each items and degrees. Negative and positive response of urine test was classified in good property, but detail classification or quantitative analysis had 8% maximum error in each items. If fuzzy membership unction and generation of rule are supplemented, suggested fuzzy classifier can be applied to the clinical test.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.127-135
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• 2007

As the Internet is used widely, criminal offense that use computer is increasing, and an information security technology to remove this crime is becoming competitive power of the country. In this paper, we suggest network-based intrusion detection system that use fuzzy expert system. This system can decide quick intrusion decision from attack pattern applying fuzzy rule through the packet classification method that is done similarity of protocol and fixed time interval. Proposed system uses fuzzy logic to detect attack from network traffic, and gets analysis result that is automated through fuzzy reasoning. In present network environment that must handle mass traffic, this system can reduce time and expense of security

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.1-9
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• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.103-115
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• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.212-217
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• 2009

There are rule-based learning method and statistic based learning method and so on which represent learning method for hierarchy relation between domain term. In this paper, we propose to leveling and similarity measure using the extended AHP of fuzzy term in Information system. In the proposed method, we extract fuzzy term in document and categorize ontology structure about it and level priority of fuzzy term using the extended AHP for specificity of fuzzy term. the extended AHP integrates multiple decision-maker for weighted value and relative importance of fuzzy term. and compute semantic similarity of fuzzy term using min operation of fuzzy set, dice's coefficient and Min+dice's coefficient method. and determine final alternative fuzzy term. after that compare with three similarity measure. we can see the fact that the proposed method is more definite than classification performance of the conventional methods and will apply in Natural language processing field.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.6
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• pp.79-88
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• 1997

New technologies, systems, and services in telecommunication have increased the need for an efficient and robust control mechanism to protect switching systems from overload. To achieve proper control, it is necessary to find a set of parameters that can describe the system. However, it is difficult to find types of data that can form a suitable basis for control. In this paper, we categorize the load status of a switching system into three classes (i.e., normal state, pre-overload state, and overload state) and formulate the overload detection as a classification problem. We find the relationships between the load classes and a set of monitored switching system parameters by applying a fuzzy rule-generation method. The rules are automatically generated from training data. Simulation results involving a switching system is given.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.69-72
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• 2006

급격한 정보화 산업의 발달로 인하여 혁신적인 기술 진화와 함께 이에 기반한 새로운 환경적, 기술적 패러다임이 변화되고 있다. 공간 간 융합과 조화를 극대화 시키고 공간속에서의 충돌과 문제점을 최소화시키기 위한 유비쿼터스 공간의 출현이다. USN에서 많은 수의 작고 다양하고 이질적인 센서 데이터 들이 발생하고 있다. 센서 데이터베이스 시스템에서 수많은 데이터들을 융합하기 위하여 에이전트 기술을 이용하고, 방대하고 애매모호한 데이터를 퍼지이론을 적용하여 데이터를 분류하여 적절한 장소에서 사용자의 욕구에 알맞은 정보를 제공함으로써 효율성과 융통성을 지원하는 방법을 제안한다. 본 논문에서는 이러한 애매모호한 데이터를 적절하게 분류함으로써 시간과 비용을 절약하고 빠른 응답을 사용자에게 전달할 수 있으며 유효적절한 서비스를 사용자의 기호에 맞게 제공함으로써 공간과 사물에 주어진 센서 데이터를 효율적으로 관리 할 수 있는 방법을 제안한다.