• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.63-73
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• 2009

The present study was conducted to explore how the processing of lexical ambiguity between homonymy and polysemy differs from each other, and whether the representation of mental lexicon was separated from each lexical ambiguity by a semantic priming paradigm. Homonymy (M1 means the literal meaning of '사과', i.e. apple and M2 means another literal meaning of '사과', i.e. apologize) was used in Experiment I, and polysemy (M2 means the literal meaning of '바람', i.e. wind and M2 means the figurative meaning of '바람', i.e. wanton) was used in Experiment 2. The results of both experiments showed that a significant semantic priming effect occurs regardless of the type of ambiguities (homonymy and polysemy) and the difference of their semantic processes. However, the semantic priming effect for polysemy was larger than that for homonymy. This result supports the hypothesis that the semantic process of homonymy is different from that of polysemy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.139-142
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• 1998

Missing values in data for fuzzy c-menas clustering is discussed. Two basic methods of fuzzy c-means, i.e., the standard fuzzy c-means and the entropy method are considered and three options of handling missing values are proposed, among which one is to define a new distance between data with missing values, second is to alter a weight in the new distance, and the third is to fill the missing values by an appropriate numbers. Experimental Results are shown.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.148-152
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• 1998

Gustafson and Kessel proposed a modified fuzzy c-Means algorithm based of the Mahalanobis distance. Though the algorithm appears more natural through the use of a fuzzy covariance matrix, it needs to calculate determinants and inverses of the c-fuzzy scatter matrices. This paper proposes a fuzzy clustering algorithm using pseudo mahalanobis distance, which is more easy to use and flexible than the Gustafson and Kessel's fuzzy c-Means.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.565-575
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• 2016

In this paper, we develop the noninformative priors for the linear combinations of means in the exponential distributions. We develop the matching priors and the reference priors. The matching priors, the reference prior and Jeffreys' prior for the linear combinations of means are developed. It turns out that the reference prior and Jeffreys' prior are not a matching prior. We show that the proposed matching prior matches the target coverage probabilities much more accurately than the reference prior and Jeffreys' prior in a frequentist sense through simulation study, and an example based on real data is given.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.318-323
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• 2007

국방부에서 발표한 ‘국방개혁에 관한 법률’ 에 따라 2014년까지 현역병들에 대한 복무기간이 단계적으로 단축될 예정이다. 이에 따라 좀 더 효율적인 직무교육 방안이 필요하게 되어, ‘차등제 교육’을 시행하고 있다. 이 교육의 효과를 향상시키기 위해서는 훈련병들의 예상 학업 성취도를 미리 정확하게 예측하는 것이 필수적이다. 따라서, 본 연구에서는 입교 초기에 얻을 수 있는 신병들의 제한된 자료들을 이용하여 교육 성취도 예측 모형을 개발하였다. 본 모형의 목적 변수는 ‘일반관리 인원’, ‘집중관리 인원’의 값을 갖는 이진형 성취집단 변수이며, 사용된 기법은 k-means 군집기볍과 Decision Tree 기법을 혼합한 모형, k-means 군집기법과 Neural Network 기법을 혼합한 모형, Decision Tree 모형, Neural Network 모형, Bayesian 모형, 그리고 Logistic 모형 등을 사용하였다. 그 결과 k-means 군집기법과 Decision Tree를 혼합한 모형이 가장 좋은 예측력올 보이는 것으로 나타났다. 이러한 교육 성취집단 예측 모형은 향후 군에서 이루어지는 다양한 교육 프로그램에 적극적으로 이용될 수 있을 것으로 기대된다.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.69-72
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• 2002

We propose a modified splitting method to obtain an initial codebook, which is used to design a codebook. The principle of the proposed method is that the more representative vectors are assigned to the class, which has the mere member training vectors or a lower squared error. The conventional K-means algorithm and the method provided from reference (5) are used to estimate the performance of the designed codebook. In thin work, the proposed method shows better results than the conventional splitting method in all experiments.

• MALSORI
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• no.57
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• pp.113-122
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• 2006

Recently, many studies have been done for speech recognition in noisy environments. Particularly, the Aurora DB has been built as the common database for comparing the various feature extraction schemes. However, in general, the recognition models as well as the features have to be modified for effective noisy speech recognition. As the structure of the HTK is very complex, it is not easy to modify, the recognition engine. In this paper, we implemented a baseline recognizer based on the segmental K-means algorithm whose performance is comparable to the HTK in spite of the simplicity in its implementation.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.203-218
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• 2004

In this paper, we develop the matching priors and the reference priors for linear combination of the means under the normal populations with equal variances. We prove that the matching priors are actually the second order matching priors and reveal that the second order matching priors match alternative coverage probabilities up to the second order (Mukerjee and Reid, 1999) and also, are HPD matching priors. It turns out that among all of the reference priors, one-at-a-time reference prior satisfies a second order matching criterion. Our simulation study indicates that one-at-a-time reference prior performs better than the other reference priors in terms of matching the target coverage probabilities in a frequentist sense. We compute Bayesian credible intervals for linear combination of the means based on the reference priors.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.33-36
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• 2011

클러스터링은 데이터 포인트들을 그룹으로 묶어 데이터를 분석하는데 유용하다. 특히 K-means는 가장 널리 쓰이는 클러스터링 알고리즘으로 k개의 군집(Cluster)을 찾는다. 본 논문에서는 기존의 K-means 알고리즘과 비교해 고차원 대규모데이터에 대해서 효율적으로 동작하는 K-means 알고리즘을 제안한다. 제안된 알고리즘은 기존의 알고리즘에서와 같이 거리 정보를 이용해 불필요한 계산을 줄여나가며 또한 움직임 없는 군집들을 계산에서 제외하여 수행시간을 단축한다. 제안된 알고리즘은 기존의 관련연구에서 제안된 알고리즘에 비해 공간을 적게 쓰면서 동시에 빠르다. 실제 고차원 데이터 실험을 통해서 제안된 알고리즘의 효율성을 보였다.