• Korean Journal of Logic
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• v.17 no.2
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• pp.323-349
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• 2014

In this paper, we show that the standard completeness for the extension of UL with compensation-free reinforcement (cfr) $(({\phi}&{\psi}){\rightarrow}({\phi}{\wedge}{\psi})){\vee}(({\phi}{\vee}{\psi}){\rightarrow}({\phi}&{\psi}))$ can be established. More exactly, first, the compensation-freely reinforced uninorm logic $UL_{cfr}$ (the UL with (cfr)) is introduced. The algebraic structures of $UL_{cfr}$ are then defined, and its algebraic completeness is established. Next, standard completeness (i.e. completeness on [0, 1]) is established for $UL_{cfr}$ by using the method introduced in Yang (2009).

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.2
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.178-180
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• 2005

Recently, electrical demands increase rapidly in metropolitan areas according to the extension of urban areas. Therefore underground transmission lines are getting expanded. This paper presents the rapid and accurate algorithm for fault discrimination and fault location in underground transmission lines. This paper uses fuzzy logic method using voltage and zero sequence for fault discrimination. And this paper uses travelling wave and wavelet transform for fault location. To prove the performance of the algorithm, it test algorithm with signal obtained from ATPDraw simulation.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.350-352
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• 2005

Recently, electrical demands increase rapidly in metropolitan areas according to the extension of urban areas. Therefore underground transmission lines are getting expanded. This paper presents the rapid and accurate algorithm for fault discrimination and fault location in underground transmission lines. This paper uses fuzzy logic method using voltage and zero sequence for fault discrimination. And this paper uses travelling wave and wavelet transform for fault location. To prove the performance of the algorithm, it test algorithm with signal obtained from ATPDraw simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.251-254
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• 2003

데이터는 어떤 특성을 나타내는 언어적 또는 수치적 값들의 표현이다. 이러한 데이터들을 목적에 따라 구성한 것이 정보이며, 문제 해결이나 패턴 분류, 또는 의사 결정을 위해 정보들간의 관계를 규칙으로 체계화하는 것이 지식이다. 현재 대부분의 산업 분야에서 시스템에 대한 이해를 높이고 시스템의 성능을 향상시키기 위해 지식을 추출하고, 적용시키는 작업들이 활발히 이루어지고 있다. 지식 정보의 추출은 지식의 획득, 표현, 구현의 단계로 구성되며 이렇게 추출된 지식 정보는 규칙으로 도출된다. 본 논문에서는 여러 산업 분야에 걸쳐 다양하게 적용되는 지식 정보 추출 방법들에 대해 그 영역별로 알아보고 여러 시험 데이터들과 실제 시스템에 클러스터링(CL), 입력공간 분할(ISP), 뉴로-퍼지(NF), 신경망(NN), 확장 행렬(EM) 등의 방법들을 적용시킨 결과들을 비교 분석하고자 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• Journal of KIISE:Software and Applications
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• v.28 no.3
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• pp.272-284
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• 2001

웹 기반 검색 시스템에서사용자의 관심이 많은 문서를 선별하여 제공하기 위해 프로파일이나 시소러스에 관한 연구가 이루어지고 있다. 그러나, 프로파일이나 시소러스를 구축하고 유지보수 하는데 많은 시간과 노력이 필요하다. 특히 구축된 시소러스에 대해 구조화 및 적합성의 문제가 있다. 따라서, 이러한 문제점을 극복하고자 본 논문에서는 문서에서 추출한 용어 빈도를 문서에서 용어의 중요 정도로 사상시키기 위해 시그모이드 멤버 쉽 함수를 적용한다. 또한, 이 중요 정도에 따라 질의어를 확장하고 의미적으로 연결된 문서를 동일한 문서 집단으로 분류할 수 있는 알고리즘을 제안하여 사용자의 선호도가 반영된 문서를 선별하고 제공하고자 한다.

• Journal of Oral Medicine and Pain
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• v.47 no.2
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.28-36
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• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.