• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.10a
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• pp.144-147
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• 1994

본 연구에서는 진도우파수 성분과 진동진폭에 대한 이상진동의 멤버쉽함수(membership function)를 고려하여, 멤버쉽정도(membership grade)를 구하고, 퍼지연산에 의하여 회전동기와 비동기진동을 구별하는 1차 진단을 한후, 각각에 대한 진동진폭의 멤버쉽함수와 인과매트릭스(decision table)를 이용하여 보다 세분된 2차 진단을 수행하는 2단계의 진단수법을 제안한다. 그리고 실험장치에서 여러가지의 결함을 인위적으로 만들고, 이 계측자료와 관련자료를 이용하여 본 진단법의 유용성을 검토하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.123-129
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• 2001

본 연구에서는 전문가와 운전자의 제어 지식을 더 정확하게 표현하여 퍼지 논리 제어기의 성능을 향상시킬 수 있는 소속함수 수정 알고리즘을 제안한다. 제안된 알고리즘은 제어지식을 더 정확히 표현할 수 있도록 직관적인 지식과 경험으로부터 유추된 대략적인 제어지식을 평가기준으로 하고 입출력 데이터 클러스터링에 의해 소속함수의 형태와 위치를 수정한다. 제안된 방법을 수위 조절 모델과 교통신호 제어 모델에 적용한 실험을 통해서, 제안된 알고리즘이 기존 제어기의 성능을 향상시킬 수 있고, 퍼지 제어기에서 언어적 변수에 대한 구간 설정의 어려움을 해결할 수 있음을 알 수 있었다.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.7-13
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• 1999

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input -output pair. First, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.595-604
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• 2004

By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.2 s.40
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• pp.43-49
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• 2007

In this study, a GIS spatial analysis method adopted fuzzy concept was introduced and land suitability analysis of waste landfill were conducted through this method. Previous studies conducted site evaluation and land suitability analysis by appling spatial overlay of conventional GIS that based on the boolean logic of crisp set. However these method can not consider the uncertainty of spatial data and the incongruity of data classification criteria, because these method handle spatial data based on the boolean logic of crisp set. As not provided trustable analysis result, conventional GIS spatial overlay method lacks opportunity for expanding use in reality. This study selected waste landfill as facility for analysis and applied fuzzy spatial analysis method as an objective approach. In the concrete contents of study, a series process with regard to the definition procedure of membership function for continuous data and the fuzzy input value generation of spatial data for fuzzy analysis is established. As a result, in this study we proposed a method that derive parameters for deciding the membership function of spatial data by considering the criterion of data classification and factor selection for land suitability analysis of waste landfill.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.587-592
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• 2003

Many real world control problems have continuous states and actions. When the state space is continuous, the reinforcement learning problems involve very large state space and suffer from memory and time for learning all individual state-action values. These problems need function approximators that reason action about new state from previously experienced states. We introduce Fuzzy Q-Map that is a function approximators for 1 - step Q-learning and is based on fuzzy clustering. Fuzzy Q-Map groups similar states and chooses an action and refers Q value according to membership degree. The centroid and Q value of winner cluster is updated using membership degree and TD(Temporal Difference) error. We applied Fuzzy Q-Map to the mountain car problem and acquired accelerated learning speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1625-1631
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• 2020

With the development of IoT technology, various digital equipment is being spread, and accordingly, the importance of data processing is increasing. The importance of data processing is increasing as it greatly affects the reliability of equipment, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN according to the characteristics of the fuzzy membership function. The proposed algorithm calculates the estimated value according to the correlation between the value of the fuzzy membership function between the input image and the pixel value inside the filtering mask, and obtains the final output by adding or subtracting the output of the spatial weight filter. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and analyzed using difference image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves the important characteristics of the image, and shows the performance of efficiently removing noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.720-725
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• 2001

Derivation of TSK fuzzy model from nonlinear differential equation is fundamental issue in the field of theoretical fuzzy control. The method which does not yield affine local differential equations at off-equilibrium points is proposed in this paper. A prototype TSK fuzzy model which has triangular membership functions for linguistic terms of the antecedent part is derived systematically. And then GA is used to modify the membership functions optimally. Simulation results show the validity of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.37-40
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• 1997

This paper presents a performance improvement of distributed fuzzy control system by changing the triangular membership function widths according to the input variables. The control region consists of 4 parts according to the sign of error and change of error terms. Each control part is operated by the suitable nonuniform triangular membership function. Through the simulation for the boiler-turbin model of a fossil power plant using decentralized contol, it is verified this proposal