• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.243-248
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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 eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The 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 feature extraction and classification problem on nonlinear data set.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Pallet News
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• s.54
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• pp.19-25
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• 2009

아시아통합물류시스템을 구축하기 위해서 한국의 (사)한국파렛트컨테이너협회는 아시아유닛로드스쿨 운영 및 아시아파렛트시스템연맹을 창설하여 아시아 통합물류시스템 구축을 위하여 노력하고 있습니다. 본고는 제3회 아시아유닛로드스룰 연수교육(07.14$\sim$07.17 말레이시아 쿠알라룸푸르)의 개강식에 이어 KPCA 박은규 상근 부회장의 특강 내용입니다.

• Pallet News
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• s.68
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• pp.5-14
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• 2013

9회째를 맞이하는 한국파렛트컨테이너산업대상은 물류의 기본 핵심인 표준파렛트와 컨테이너의 생산 및 사용에 있어 기장 모범이 되는 우수한 업체와 이러한 활동에 헌신한 개인 또는 단체를 발굴, 시상함으로써 유닛로드 시스템의 보급 확산을 촉진하기 위해 제정된 국내 유일의 시상식이다. 또한 유닛로드시스템 컨퍼런스를 통하여 국내 우수기업의 유닛로드 시스템 개선사례 및 성공사례를 발표하는 시간을 가졌다.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Journal of IKEEE
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• v.23 no.4
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• pp.1157-1165
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• 2019

This paper discussed the results of the development and application of the machine learning algorithm to the induction motor for the preventive diagnostic system using current and magnetic flux signals. The optimal 29 features were extracted for identifying faulted types of induction motor. In particular, any load rate was derived using the tendency of the difference value from the center of the 7th harmonic frequency to the sideband of the current signal, and the corresponding classification accuracy showed about 84.6% by the KPCA feature reduction technique and the k-NN determination algorithm.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• Pallet News
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• s.72
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• pp.5-15
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• 2014

물류에 있어 가장 효율적 시스템인 유닛로드 시스템(Unit Load System, ULS)의 구축과 보급 확산은 국내 물류시장의 시급한 과제로 ULS는 일관파렛트화와 컨테이너화가 그 핵심이며, 일관파렛트화와 컨테이너화는 파렛트와 컨테이너의 표준화로 가능하다. 10회째를 맞이하는 한국파렛트컨테이너산업대상은 물류의 기본 핵심인 표준파렛트와 컨테이너의 생산 및 사용에 있어 가장 모범이 되는 우수한 업체와 이러한 활동에 헌신한 개인 또는 단체를 발굴, 시상함으로써 유닛로드 시스템의 보급 확산을 촉진하기 위해 제정된 국내 유일의 시상식이다. 또한 유닛로드시스텀 컨퍼런스를 통하여 국내 우수기업의 유닛로드 시스템 개선사례 및 성공사례를 발표하는 시간을 가졌다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Power System Engineering
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• v.14 no.2
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• pp.40-47
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• 2010

This paper proposes a fault detection system to detect the strip rupture in six-high stand Cold Rolling Mills based on transient current signal of an electrical motor. For this work, signal smoothing technique is used to highlight precise feature between normal and fault condition. Subtracting the smoothed signal from the original signal gives the residuals that contains the information related to the normal or faulty condition. Using residual signal, discrete wavelet transform is performed and acquire the signal presenting fault feature well. Also, feature extraction and classification are executed by using PCA, KPCA and SVM. The actual data is acquired from POSCO for validating the proposed method.