• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10a
• /
• pp.115-117
• /
• 2004

인간과 인간 사이에 컨텍스트의 역할이 중요한 것처럼 기계가 컨텍스트를 인식할 수 있는 능력을 갖추는 것은 중요하다. 특히 지능적인 서비스를 제공하기 위해서는 고수준 컨텍스트를 추출하는 것이 필요하고, 최근 베이지안 네트워크를 이용해 컨텍스트를 추출하려는 연구가 많이 있었다. 그러나 대부분은 단순한 컨텍스트를 추출하는 연구들이고, 상황이나 사용자에 따라 다른 특성을 보이는 경우에 대한 처리는 하지 못하고 있다. 본 논문은 퍼지 소속 함수를 통해 각 센서에서 오는 정보를 전 처리하고, 이를 베이지안 네트워크를 이용해 고수준 컨텍스트로 추출하는 방법을 제안한다. 특히 여러 개의 퍼지 노드가 있을 경우 퍼지 소속값의 곱을 사용하여 베이지안 추론에 적용하였다. 각 센서의 정보를 처리하는 퍼지 소속 함수는 사용자가 쉽게 설계할 수 있고, 컨텍스트 추출모듈과 별개로 설계가 가능하기 때문에 베이지안 네트워크의 유연하고 적응적인 특성을 유지하면서 개인화가 가능하다. 제안한 방법의 유용성을 보이기 위해 실제 세계의 문제를 모델링한 베이지안 네트워크의 예를 보이고 이를 분석한다.

• Economic and Environmental Geology
• /
• v.50 no.3
• /
• pp.195-214
• /
• 2017

Statistical landslide susceptibility analysis, which is widely used among various landslide susceptibility analysis approaches, predicts the unstable area by analyzing statistical relationship between landslide occurrence locations and landslide controlling factors. However, uncertainties are involved in the procedures of the susceptibility analysis and therefore, fuzzy approach has been used to deal properly with uncertainties. The fuzzy approach used fuzzy set theory and fuzzy membership function to quantify uncertainties involved in landslide controlling factors. Various fuzzy approaches were suggested in the procedure of the membership value determination and fuzzy operation in the previous researches. However, few studies were carried out to compare the analysis results obtained from various approaches for membership function determination and fuzzy operation. Therefore, in this study, the authors selected Jinbu area, which a large number of landslides were occurred at in 2006, to apply two most commonly used methods, the frequency ratio and the cosine amplitude method to derive membership values for each controlling factor. In addition, the integration of different thematic layers to produce landslide susceptibility map was performed by several fuzzy operators such as AND, OR, algebraic product, algebraic sum and Gamma operator. The results of the landslide susceptibility analysis using two different methods for the determination of fuzzy membership values and various fuzzy operators were compared on the basis of ROC graph to check the feasibility of the fuzzy based landslide susceptibility analysis.

• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.39 no.4
• /
• pp.363-369
• /
• 2002

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.6
• /
• pp.1269-1274
• /
• 2014

In computer vision research, binarization procedure is one of the most frequently used tools to discriminate target objects from background in grey level binary image. Fuzzy binarization is a reliable technique in environment with high uncertainty such as medical image analysis by setting the threshold as the average of minimum and maximum brightness with triangle type fuzzy membership function. However, this technique is also known as contrast sensitive method thus its discrimination power is not so great when the image has low contrast difference between objects and backgrounds and suffer from information loss as a result. Thus, in this paper, we propose a fuzzy binarization using ART2 algorithm to handle such low contrast image analysis. Proposed ART2 algorithm is applied to determine the medium point of membership function in the fuzzy binarization paradigm. The proposed methods shows low information loss rate in our experiment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.5
• /
• pp.599-604
• /
• 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
• /
• v.10B no.6
• /
• pp.587-592
• /
• 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 Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.397-404
• /
• 2002

In this paper, it is proposed wavelet neural network using the fuzzy concept with the fuzzy and the multi-resolution analysis(MRA) of wavelet transform. Also, it wishes to improve any nonlinear function learning approximation using this system. Here, the fuzzy concept is used the bell type fuzzy membership function. And the composition of wavelet has a unit size. It is used the backpropagation algorithm for learning of wavelet neural network using the fuzzy concept. It is used the multi-resolution analysis of wavelet transform, the bell type fuzzy membership function and the backpropagation algorithm for learning. This structure is confirmed to be improved approximation performance than the conventional algorithms from one dimension and two dimensions function through simulation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.6
• /
• pp.575-583
• /
• 2000

본 논문에서 임의의 데이터가 입력되면 기준 영상 중에서 가장 유사도가 큰 영상을 찾아 국부 승리자로 선택하고, 그 국부 승리자 중에서 전체 승리자를 선택하여 최종 출력값을 얻는 계층적 FGNN(Fuzzy Genetic Neural Network)을 제안하고, 이에 하이브리드 퍼지 소속함수와 유전자 알고리즘을 적용하였다. 하이브리드 퍼지 소속함수는 입력 값을 0~1 사이의 값으로 함으로써 시스템의 속도를 빠르게 하고 유전자 알고리즘을 입력값을 일정한 오차 이내로 하여 최적의 영상을 얻도록 하였다. 위의 계층적 FGNN 알고리즘을 회로 설계 및 검증하였다. 또한 제안한 FGNN을 이용하여 영상에 포함된 잡음을 제거하고, 이와 유사한 구조를 가진 FDNN(Fuzzy Decision Neural Network) 성능보다 FGNN의 성능이 우수함을 여러 가지 영상을 통하여 확인하였다. 또한 모의 실험 결과 영상에 대한 평균자승오차(MSE : Mean Square Error)를 비교하였으며, 그 결과 하이브리드 퍼지 함수와 유전자 알고리즘을 적용한 FGNN이 메디안 필터, OC, CO, FDNN 등에 비해 우수함을 확인하였다. FGNN 알고리즘을 Top-Down 방식으로 VHDL(VHSIC Hardware description Language)을 이용하여 코딩(Coding)하고, Synopsys 툴을 이용하여 하드웨어를 설계하였다. 이 알고리즘의 하드웨어는 총 5개의 블록으로 가지고 있고 각각의 블록은 파이프라인 형태로 구성하고, 이는 Synopsys 툴을 이용하여 동작 및 성능을 검증하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.3
• /
• pp.11-20
• /
• 2006

This paper describes the technique which utilizes a fuzzy neural network to sketch feature extraction in digital images. We configure an artificial neural network and make it learn fuzzy membership functions to decide a local threshold applying to sketch feature extraction. To do this. we put the learning data which is membership functions generated based on optimal feature map of a few standard images into the artificial neural network. The proposed technique extracts sketch features in an images very effectively and rapidly because the input fuzzy variable have some desirable characteristics for feature extraction such as dependency of local intensity and excellent performance and the proposed fuzzy neural network is learned from their membership functions, We show that the fuzzy neural network has a good performance in extracting sketch features without human intervention.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2001.10a
• /
• pp.527-530
• /
• 2001

영상이 시각적인 해석을 위해 처리될 때, 퍼지 이론이 영상 대비 강화에 많이 사용되고 있다. 적응적 퍼지 클러스터링 기법을 사용하여 자동적으로 영상의 명암도에 대한 다중 클래스를 형성하고 여기에 각각의 명암도를 속성 공간으로 전환시키는 퍼지함수를 사용하여 각 픽셀의 명암도에 부합하는 퍼지 소속도를 구한다. 영상 대비 향상을 위하여 구한 퍼지 소속도에 강화 연산자를 반복적 적용한다. 본 논문에서 제안한 방법을 히스토그램 평활화와 비교하기 위해 흑백 영상에 적용하였다.