• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.370-378
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• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.51-58
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• 2008

Wireless sensor networks are used by various application areas to implement smart data processing and ubiquitous system. In the recent research of parking management system based on wireless sensor networks, adaptive sensing and efficient data processing are not considered. The effectiveness of implementing these distributed computing devices affects the performance of the applications in parking management. This paper proposes an adaptive sensing using fuzzy wireless sensor for the ubiquitous networks of parking management system. The fuzzy inference system is encoded in the sensor for efficient car presence detection. Moreover, a rule base adaptive module is proposed which wirelessly transmit the new values to each sensor for adapting the environment of car park area. The result of experiments shows that the fuzzy wireless sensor provides more throughputs and less time delays compared to a normal method of data gathering by wireless sensors.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.159-173
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• 2004

This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2768-2770
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• 2001

본 논문은 지능형 알고리즘과 이미지 프로세싱 방법을 결합한 새로운 방법으로 칼라 매칭 시스템에 구현한다. 칼라 매칭 시스템은 이미지 프로세싱을 이용하여 칼라의 RGB 데이터를 분석한 후 얻어진 색상정보를 가지고 사용자가 원하는 칼라는 구현하는 시스템이다. 칼라 매칭 시스템의 모델링에 이용되는 지능형 모델은 퍼지 추론과 적응 퍼지 추론 시스템(Adaptive Neuro-Fuzzy Inference System: ANFIS)이며, 최소 자승법을 기반으로 한 회귀 다항식과 비교하여 제안된 지능형 모델에 대한 성능과 실용성을 검증한 후 델파이를 이용하여 구현하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.510-512
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• 2012

상황인식 서비스 분야에서 불확실한 데이터를 추론하는 것은 매우 어렵고 복잡하다. 이러한 상황정보들에서 얻어지는 데이터는 불확실성을 내포하고 있어서 불확실한 추론 결과를 초래할 수 있다. 비록 불확실성 문제들을 해결하기 위해 퍼지 이론, 뉴런 네트워크, 동적 베이지안 네트워크, 은닉 마르코프 모델과 같은 여러 종류의 방법들이 제시되었지만 이러한 방법들은 가설들을 하나의 숫자에 의해 신뢰의 정도를 표시하기 때문에 많은 어려움이 있다. 본 논문에서는 사용자들이 제공받는 서비스들에 대하여 만족도를 평가한 후 수집된 데이터를 활용하여 사용자들의 상관 관계를 분석한다. 그리고 Dempster-Shafer 이론을 사용하여 사용자들로부터 측정된 믿음 값을 융합한다. 이는 불확실성 값을 낮추어 추론결과의 정확성을 높이고 증거구간을 재설정하여 사용자들에게 신뢰성 있는 적응형 서비스를 제공하게 한다.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.97-105
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• 2014

To enhance the practical usage of solely quantitative evaluation method for each students on the current public education fields which might cause some social problems, an intelligent and adaptive fuzzy logical inference methodology for the additional qualitative evaluation technique is proposed to utilize each students personal characteristic properties to be evaluated. Proposed method uses some verbal descriptions for the linguistic qualifier in addition to the grade points. An imaginary virtual experimentation only has been implemented due to some difficulties with the critical national educational policy problems in the case of some possibly real and practical experimental environments to be utilized for the simulation.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.321-330
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• 2003

This paper proposes an agent-based adaptive tutoring system that monitors learning process of learners' and provides learning materials dynamically according to the analyzed learning character. Furthermore, it uses fuzzy concept to evaluate learners' ability and to provide learning materials appropriate to the level of learners'. For this, we design a courseware knowledge structure systematically and then construct a fuzzy level set on the basis of it considering importance of learning targets, difficulty of learning materials and relation degree between learning targets and learning materials. Using agent, monitoring continually the learning process of learners 'inferencing to offer proper hints in case of incorrect answer in learning assesment, composing dynamically learning materials according to the learning feature and the evaluation of assesment, our system implements effectively adaptive instruction system. Moreover, appling the fuzzy concept to the system could naturally consider and ideal with various and uncertain items of learning environment thus could offer more flexible and effective instruction-learning methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.343-345
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• 2001

전자상거래 시장의 규모 및 상점 수의 증가로 인하여 상품 구매자의 상품 구입을 보조해 줄 수 있는 에이전트 기술의 필요성이 증가하고 있다. 본 논문에서는 전자상거래에서 사용자의 선호도를 반영하여 상품을 추천해주는 지능형 에이전트 모델을 제안한다. 상품 정보 및 사용자의 선호도 정보를 관리하기 위하여 계층적 퍼지 그래프를 이용함으로써, 제안된 모델은 기존에 비해 더 나은 융통성과 효율성을 보일 수 있다. 에이전트에 의한 추천 상품은 사용자 선호도와 상품 기술서 사이의 비교순위에 의해 결정되며, 사용자와 에이전트 사이의 상품검색과 상품구매에 관한 정보는 지속적으로 사용자의 선호도 정보를 갱신하고 새로운 선호도 정보를 추론하는 데 사용된다. 이러한 방법에 의하여 사용자 선호도에 대한 적응성이 뛰어난 상품 추천 에이전트를 제공할 수 있으며, 전자상거래에서 구매자의 편의성을 증대시키는데 도움이 될 것으로 기대한다.