• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정 (knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스 (data warehouse)를 기반으로 하는 OLAP환경에서의 데이터와 인터넷을 통한 웹 (web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지 (explicit knowledge)외에 암묵지 (tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스 (prior knowledge base)와 선행지식관리시스템 (prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템 (knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정(Knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스(data warehouse)를 기반으로 하는 OLAP 환경에서의 데이터와 인터넷을 통한 웹(web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식 채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지(explicit knowledge)외에 암묵지(tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스(prior knowledge base)와 선행지식관리시스템(prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템(knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.412-417
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• 2005

The Internet has been widely applied to the remote control system. The network-based control system, however, has a random time delay and an inherent weak point of the network, when the data ate transmitted. The network delay may result in performance degradation or even system instability in teleoperation. In this paper a prediction model of network delay using TSK (Takagi-Sugeno-Kang) fuzzy model is presented. An adaptive scheme is developed to update the prediction model according to the current network status. The prediction model is applied to the control of an Internet-based mobile robot to show its usefulness. In the computer simulation the TSK Prediction model of network delay is proven superior to the conventional algorithms.

• Economic and Environmental Geology
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• v.38 no.4 s.173
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• pp.395-409
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• 2005

To explore submarine groundwater discharge (SGD) into the coastal zone of Jeju Island, the water quality analysis with seasonal remotely sensed data was carried out. If the groundwater is directly discharged into the ocean, the water quality of coastal zone is influenced. Therefore sea surface temperature (SST), the transparency, and Chlorophyll-a's concentration were analyzed for extracting the anomaly zone related with SGD using Landsat Thematic Mapper (TM) data acquired on April, August, and December. Then the spatial characteristics of springs, which located along the coastal area, were analyzed by CIS data integration based on Fuzzy logic. The integration results were compared with the anomaly zone extracted from Landsat TM data, and it is considered that springs has close relationship with SGD.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.13-22
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• 2005

To improve the anomaly IDS using system calls, this study focuses on Neural Networks Learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm with fuzzy membership function. The back-propagation neural networks and Neuro-Fuzzy technique are applied for anomaly intrusion detection of system calls using Sendmail Data of UNM to demonstrate its aspect of he complexity of time, space and MDL performance.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.485-492
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• 2007

In this paper, we propose the receiver block error detection of the video codec and the image concealment algorithm using fuzzy inference. The proposed error detection and concealment algorithm gets SSD(Summation of Squared Difference) and BMC(Boundary Matching Coefficient) using the temporal and spatial similarity between corresponded blocks in the two successive frames. Proportional constant, ${\alpha}$, for threshold value, TH1 and TH2, is decided after fuzzy data is generated by each parameter. To examine the propriety of the proposed algorithm, random errors are inserted into the QCIF Susie standard image, then the error detection and concealment performance is simulated. To evaluate the efficiency of the algorithm, image quality is evaluated by PSNR for the error detection and concealed image by the existing VLC table and by the proposed method. In the experimental results, the error detection algorithm could detect all of the inserted error, the image quality is improved over 15dB after the error concealment compare to existing error detection algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.37-47
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• 1998

This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.287-292
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• 2015

The aim of this study was to provide a method to assume the value of the continuous and possible management part to investigate the relationship between component of evaluation of Disaster Management and Sustainability Management in enterprise by obtaining Fuzzy Relation Matrix in view of necessity and possibility when there are new data. The index of Disaster Management was yielded from 100 small and medium-sized enterprises for 4 component of evaluations of based on ISO standard, and that of Sustainability Management was obtained from same enterprises for 6 component of evaluations in GRI G4 version of Sustainability Management guideline. The above results suggest that this model is significant by using 80 data as a training data and 20 data as a checking data from the 100 data obtained in this study.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1284-1294
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• 2007

In this paper, a blind nonlinear channel equalization is implemented by using a Modified Fuzzy C-Means (MFCM) algorithm. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.