• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.1
• /
• pp.378-384
• /
• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.73-82
• /
• 2005

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.1
• /
• pp.91-96
• /
• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1235-1243
• /
• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Proceedings of the KAIS Fall Conference
• /
• 2011.05a
• /
• pp.389-392
• /
• 2011

본 논문에서는 영상의 색 항등성을 달성하기 위해 본질 영상의 핵심인 불변 방향을 K-means 클러스터링을 이용해 검출하는 개선된 알고리즘을 제안한다. 우선, RGB 영상을 K-means 클러스터링 기법에 의해 다수의 클러스터로 분할한다. 이 때, 클러스터 간의 거리 측정은 유클리드 거리이다. 그리고 분할된 클러스터 중 가장 많은 색을 가진 클러스터만을 x-색도 공간으로 도시하여 해당되는 후보 불변 방향을 계산한다. 검출된 후보 불변 방향은 방향별로 프로젝션된 히스토그램에서 3개 이상의 프로젝션된 데이터를 가진 bin들의 개수가 가장 적은 방향이다. 그 후, 분할된 다른 여러 클러스터에 해당되는 후 보 불변 방향을 계산하여 가장 많은 빈도로 나타나는 방향을 영상의 최종 불변 방향으로 결정한다. 실험에서 Ebner에 의해 제안된 데이터집합을 실험 영상으로 사용하였고, 색항등성 측도를 평가 척도로 사용하였다. 실험 결과, 제안한 기법은 형광성 표면을 가진 형광 데이터집합에 보다 적합하였으며, 엔트로피 기법보다 색항등성이 1.5배 이상 높았다.

• Journal of Intelligence and Information Systems
• /
• v.15 no.4
• /
• pp.23-36
• /
• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.1
• /
• pp.87-92
• /
• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.36T no.4
• /
• pp.17-23
• /
• 1999

This paper described a security system using text-independent speaker recognition algorithm. Security system is based on PIC16F84 and sound card. Speaker recognition algorithm applied a k-means based model and weighted cepstrum for speech features. As the experimental results, recognition rate of the training data is 100%, non-training data is 99%. Also false rejection rate is 1%, false acceptance rate is 0% and verification mean error rate is 0.5% for registered 5 persons.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.4
• /
• pp.462-469
• /
• 2002

While the conventional K-means algorithms use a fixed weight to design a vector codebook for all learning iterations, the proposed method employs a variable weight for learning iterations. The weight value of two or more beyond a convergent region is applied to obtain new codevectors at the initial learning iteration. The number of learning iteration applying a variable weight must be decreased for higher weight value at the initial learning iteration to design a better codebook. To enhance the splitting method that is used to generate an initial codebook, we propose a new method, which reduces the error between a representative vector and the member of training vectors. The method is that the representative vector with maximum squared error is rejected, but the vector with minimum error is splitting, and then we can obtain the better initial codevectors.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11b
• /
• pp.869-872
• /
• 2002

네트워크 기반 침입탐지시스템은 연속적으로 발생하는 패킷의 무손실 축소와, 패킷으로 정상 또는 비정상 행위패턴을 정확히 모델링한 모델 생성이 전체성능을 판단하는 중요한 요소가 된다. 네트워크 기반 비정상행위 판정 침입탐지시스템에서는 이러한 탐지모델 구축을 위해 주로 감독학습 알고리즘을 사용한다. 본 논문은 탐지모델 구축에 사용하는 감독 학습 방식이 가지는 문제점을 지적하고, 그에 대한 대안으로 비감독 학습방식의 학습알고리즘을 제안한다. 감독 학습을 사용하여 탐지모델을 구축하기 위해서는 정상행위의 패킷을 취합해야 하는 사전 부담이 있는 반면에 비감독 학습을 사용하게 되면 이러한 사전작업 없이 탐지모델을 구축할 수 있다. 본 논문에서는 비감독학습 알고리즘을 비교 분석하기 위해서 COBWEB, k-means, Autoclass 알고리즘을 사용했으며, 성능을 평가하기 위해서 비정상행위도(Abnormal Behavior Level)를 계산하여 에러율을 구하였다.