• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권3_4호
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• pp.251-258
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• 2003

Support Vector Machine(SVM)은 이론상으로 좋은 일반화 성능을 보이지만, 실제적으로 구현된 SVM은 이론적인 성능에 미치지 못한다. 주 된 이유는 시간, 공간상의 높은 복잡도로 인해 근사화된 알고리듬으로 구현하기 때문이다. 본 논문은 SVM의 분류성능을 향상시키기 위해 Bagging(Bootstrap aggregating)과 Boosting을 이용한 SVM 앙상블 구조의 구성을 제안한다. SVM 앙상블의 학습에서 Bagging은 각각의 SVM의 학습데이타는 전체 데이타 집합에서 임의적으로 일부 추출되며, Boosting은 SVM 분류기의 에러와 연관된 확률분포에 따라 학습데이타를 추출한다. 학습단계를 마치면 다수결 (Majority voting), 최소자승추정법(LSE:Least Square estimation), 2단계 계층적 SVM등의 기법에 개개의 SVM들의 출력 값들이 통합되어진다. IRIS 분류, 필기체 숫자인식, 얼굴/비얼굴 분류와 같은 여러 실험들의 결과들은 제안된 SVM 앙상블의 분류성능이 단일 SVM보다 뛰어남을 보여준다.

• International Journal of Internet, Broadcasting and Communication
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• 제9권2호
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• pp.20-26
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• 2017

Process control is crucial in many industries, especially in semiconductor manufacturing. In such large-volume multistage manufacturing systems, a product has to go through a very large number of processing steps with reentrant) before being completed. This manufacturing system has many machines of different types for processing a high mix of products. Each process step has specific quality standards and most of them have nonlinear dynamics due to physical and/or chemical reactions. Moreover, many of the processing steps suffer from drift or disturbance. To assure high stability and yield, on-line quality monitoring of the wafers is required. In this paper we develop a real-time fault detection system on semiconductor manufacturing process. Proposed system is superior to other incremental fault detection system and shows similar performance compared to batch way.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.345-354
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• 2003

An incremental kernel principal component analysis(IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis(KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenvectors should be recomputed. IKPCA overcomes this problem by incrementally updating the eigenspace model. IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the classification problem on nonlinear data set.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (B)
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• pp.175-177
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• 2003

This paper addresses a problem of classifying human breast cancer into its subtypes. A main ingredient in our approach is kernel machines such as support vector machine (SVM). kernel principal component analysis (KPCA). and kernel partial least squares (KPLS). In the task of breast cancer classification, we employ both SVM and KPLS and compare their results. In addition to this classification. we also analyze the patterns of clinical outcomes in the feature space. In order to visualize the clinical outcomes in low-dimensional space, both KPCA and KPLS are used. It turns out that these methods are useful to identify correlations between clinical outcomes and the nonlinearly protected expression profiles in low-dimensional feature space.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Computers and Concrete
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• 제21권5호
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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• pp.995-997
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• 2014

뇌파는 최근에 가장 많이 연구되고 있는 생체신호이다. 본 연구에서는 오픈 감정뇌파데이터인 DEAP Dataset를 활용한 데이터 분석 실험을 시행하였다. DEAP Dataset는 총 32개의 데이터이며, 32채널로 구성되어 있다. 전처리 과정에서는 디지털 필터인 IIR(Infinite Impulse Response) Filter를 사용하여 잡음을 제거하였고, 인공산물인 안구잡파(EOG: Electrooculograms) 제거에는 LMS(the Least Mean squares) 알고리즘을 사용하였다. 감정분류는 Valence-Arousal 평면을 사용하여 네 개의 감정으로 구분하였고, 분류 실험으로는 패턴인식 알고리즘인 SVM(support Vector Machine)를 사용하였다. 실험결과 SVM이 70%대의 결과를 도출하여 이전 실험결과보다 높은 정확도를 도출하였다.

• 분석과학
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• 제33권2호
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.