• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2158-2165
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• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1924-1925
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• 2011

본 논문에서는 비선형 기동을 하는 기동표적의 추정된 잡음을 분석하여 표적의 기동패턴을 분석하는 알고리즘을 제시하고자 한다. 기동표적의 추정위치와 측정치에서 발생하는 잡음을 가속도와 순수 잡음으로 분리하고 분리된 성분을 분석하여 표적의 기동 패턴을 인식하고 동시에 추적을 실시하는 알고리즘을 구성한다. 잡음의 분리는 퍼지 클러스터링(FCM : Fuzzy C-means Clustering) 기법을 이용하여 적절한 추정값을 이용한다. 추정된 표적의 속도와 가속도, 잡음을 재 구성하여 기동표적의 기동패턴을 분석하고, 동시에 추적을 실시한다. 위의 과정을 통해 가속도를 분리한 후 비선형성을 지닌 기동표적의 기동패턴을 선형화 하여 칼만필터를 이용 잡음을 분리하고 가속도를 다시 보상하여 추적 알로리즘을 구성한다. 그리고 제안된 알고리즘의 수행 가능성을 보여 주기 위하여 몇 가지 예를 제시하였다.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.261-263
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 각 노드의 후반부 파라미터들은 최소자승법을 이용하여 최적화 하였다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.221-237
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• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.135-138
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• 2008

본 논문은 의료 영상에 대한 효과적인 분류와 검색을 위한 알고리즘을 제안한다. 영상 분류와 검색을 위해서 MPEG-7 표준 기술자인 색 구조 기술자와 경계선 히스토그램 기술자를 사용해 영상들에 대한 특징 값을 추출한다. 이렇게 구해진 특징 값들을 의료 영상의 분류와 검색에 적용해 본 결과 비교적 낮은 성능을 보여줌을 확인하고 앞서 구해진 특징 값들을 교사 학습 방법인 SVM(Support Vector Machine)과 비교사 학습 방법인 FCM(Fuzzy C-means Clustering)에 적용시켰다. 기존 연구에서는 SVM과 FCM의 통합으로 의료 영상에 대한 분류와 검색을 시행하였지만 본 논문에서 실험한 결과 SVM과 MPEG-7 시각 기술자 중에 하나인 EHD(Edge Histogram Descriptor)를 가중치 선형 결합하여 실험한 결과가 더 정확한 분류와 높은 검색 성능을 나타냄을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.1-4
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• 2000

본 논문에서는 패턴 데이터(pattern data) 의 분할(partitioning)위하여, 계산량의 단축할 수 있는 효과적인 퍼지 클러스터링 알고리즘(fuzzy clustering algorithm)을 제시한다. 본 논문에 제시된 알고리즘은 두 단계로 수행된다. 첫번째 단계는, 개선된 FCM(Fuzzy C-means)방법에 의해 입력 패턴틀에 대해, 단지 두 번의 반복 수행과정만을 거쳐, 충분히 많은 개수의 초기 클러스터 중 심(center)를 결정한다. 다음 단계에서는, 본 논문에 제시될 클러스터 합치기 알고리즘(cluster merging algorithm)을 통해 각 클러스터의 부피(volume)에 따라 클러스터들을 합치는 과정(merging process)을 하게 된다. 결과적으로 일정한 제한된 개수의 무정형(amorphous)의 클러스터틀의 효과적으로 표현될 수 있다. 본 논문의 마지막에 제시될 실험 결과들은 제시된 방법의 유용성을 보여줄 것이다.

• Journal of Navigation and Port Research
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• v.42 no.2
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• pp.117-126
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• 2018

Container port is one of the most vital link of the transportation chain that plays an important role in trading with other countries. Having a proper understanding of port operations could change the role of the port from a transportation node to an efficient point in a transportation chain. Development of transportation chains, logistics and progress of these networks have enhanced the sustainable condition and level of transportation. Therefore, evaluating the competitiveness of ports is obligatory for port users to make a decision in investment or exploitation. This paper introduces the use of Fuzzy C-means and TOPSIS for competitiveness comparison among a sample of container terminals in Korea and China and determine how to improve Korean port competitiveness and particularly in Busan port.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.146-153
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• 2010

For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.