• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.17-23
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• 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 Korea Society of Computer and Information
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• v.18 no.4
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• pp.35-43
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• 2013

As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The proposed algorithm selects suitable blocks to embed a watermark using the energy and homogeneity properties of the grey level co-occurrence matrices as inputs for the fuzzy c-means clustering algorithm. To embed the watermark, we first perform a discrete wavelet transform (DWT) on the selected blocks and choose one of DWT subbands. Then, we embed the watermark into discrete cosine transformed blocks with a gain factor. In this study, we also explore the effects of the DWT subbands and gain factors with respect to the imperceptibility and robustness against various watermarking attacks. Experimental results show that the proposed algorithm achieves higher peak signal-to-noise ratio values (47.66 dB to 48.04 dB) and lower M-SVD values (8.84 to 15.6) when we embedded a watermark into the HH band with a gain factor of 42, which means the proposed algorithm is good enough in terms of imperceptibility. In addition, the proposed algorithm guarantees robustness against various image processing attacks, such as noise addition, filtering, cropping, and JPEG compression yielding higher normalized correlation values (0.7193 to 1).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.1
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• pp.21-24
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• 2017

Passive millimeter wave (MMW) imaging penetrates clothing to detect concealed objects. The distances extraction to the concealed objects is critical for the security and defense. In this paper, we address a passive stereo 3 mm MMW imaging system to extract the longitudinal distance to the concealed object. The concealed object area is segmented and extracted by the k-means clustering algorithm with splitting initialization. The distance to the concealed object is estimated by the corresponding centers of the segmented objects. In the experimental two pairs (each pair for horizontal and vertical polarization) of stereo MMW images are obtained to estimate distances to concealed objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.109-112
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• 2006

최근 대용량 추천시스템에 대한 필요성이 증가하고 있고, 특히 대규모 인터넷 쇼핑몰을 위한 개인화 추천 시스템 구조에 대한 관심이 높아지고 있다. 본 논문에서는 k-means 클러스터링과 순차 패턴 기법을 이용한 인터넷 쇼핑몰 상품 추천 시스템을 설계 및 구현한다. 사용자 정보의 일괄처리와 카테고리의 계층적 특성을 반영하면서 데이터 마이닝 기법을 활용하여 개인화된 추천 엔진을 대형 시스템에서 동작하도록 설계 하였다. 설계 구현한 시스템의 평가를 위해, 대형 쇼핑몰의 데이터를 이용하여 추천 예측 정확율(PRP: Predictive Recommend Precision), 추천 예측 재현율(PRR: Predictive Recommend Recall), 정확도 인수(PF1 : Predictive Factor One-measure)를 구하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1642_1643
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• 2009

일반적으로, 퍼지 예측 시스템의 성능은 데이터의 특성과 퍼지 집합을 생성하기 위한 클러스터일 기법에 매우 의존적이다. 하지만, 예측을 위한 시계열 데이터들은 자연현상에 기인하는 강한 비선형적 특성을 가지고 있으므로 적합한 시스템을 구현하는 것에 많은 제약이 따른다. 따라서 본 논문에서는 시계열의 비선형적 특성을 적절히 취급하기 위하여, 그들로부터 생성 가능한 차분 데이터 중, 유효한 차분데이터를 이용하여 다중 모델 퍼지 예측 시스템을 구현함으로써, 보다 우수한 예측이 가능하도록 하였으며, 퍼지 시스템의 모델링에는 교차 상관분석기법에 따른 계층적 구조의 클러스터링 기법 (Hierarchical Cross-correlation and K-means Clustering Algorithms: HCKA)을 적용하여, 시스템을 위한 규칙기반의 적합성을 높일 수 있도록 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.48-51
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• 2018

컴퓨터 및 기술의 발전으로 힘입어 수많은 논문이 오프라인뿐 아니라 온라인으로 발행되고 있고, 새로운 분야들도 계속 생기면서 사용자들은 방대한 논문들 중 자신이 필요로 하는 논문을 검색하거나 분류하기에 많은 어려움을 겪고 있다. 이러한 한계를 극복하기 위해 본 논문에서는 유사 내용의 논문을 분류하고 이를 군집화하는 방법을 제안한다. 제안하는 방법은 TF-IDF를 이용하여 각 논문의 초록으로 부터 대표 주제어를 추출하고, K-means 클러스터링 알고리즘을 이용하여 추출한 TF-IDF 값을 근거로 논문들을 유사 내용의 논문으로 군집화한다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.52-58
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• 2007

This paper presents the wind-power model for generation adequacy assessment. Both wind-power and system load depend on time of a year and show their periodic nature with similar periods. Therefore, the two quantities have some probabilistic relations, and if one of them is given, the other can be decided with some probability. In this paper, the two quantities are quantized by k-means clustering algorithm and related probabilities among the cluster centers are calculated using sequential wind-power and system load data. The proposed model is highly expected to be applied for generation adequacy assessment by Monte-Carlo simulation with state sampling method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3689-3696
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• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.