• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.361-368
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• 2004

The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

• Korean Journal of Agricultural Science
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• v.47 no.1
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• pp.67-81
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• 2020

The physicochemical parameters of honey are used to determine the botanic origin of honey and to specify the composition criteria for honey in regulations and standards. The parameters of honeydew and blossom honeys from Korean beekeepers were determined to investigate whether they complied with the composition criteria for honey in the food code legislated by Korean authority and to establish the parameters which should be subjected to principal component analysis for improved differentiation of honeys. The fructose and glucose contents of the honeydew honey did not comply with the composition criteria. The ash content of the honey was closely correlated with CIE a^* and CIE L^* The principal component analysis of fructose to glucose ratio, CIE a^*, CIE L^*, ash content, free acidity, and fructose and glucose contents enabled classification of honeydew, chestnut, multifloral, and acacia honeys. Additional advantage of the principal component analysis (PCA) is that the physicochemical parameters, such as fructose to glucose ratio (F/G) and color, can be determined using the analytical instruments for composition criteria and quality control of honey. This study suggested that composition criteria for honeydew honey should be established in the food code in accordance with international standards. The principal component analysis reported in this study resulted in improved classification of the honeys from Korean beekeepers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.9-16
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• 2005

In this paper, we compare face recognition rate by distance change using Principal Component Analysis algorithm being input left and right image in stereo image. Change to YCbCr color space from RGB color space in proposed method and face region does detection. Also, after acquire distance using stereo image extracted face image's extension and reduce do extract robust face region, experimented recognition rate by using PCA algorithm. Could get face recognition rate of 98.61%(30cm), 98.91%(50cm), 99.05%(100cm), 99.90%(120cm), 97.31%(150cm) and 96.71%(200cm) by average recognition result of acquired face image. Therefore, method that is proposed through an experiment showed that can get high recognition rate if apply scale up or reduction according to distance.

• Journal of KIISE
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• v.42 no.7
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• pp.919-928
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• 2015

In recent years, many research efforts have been made on privacy-preserving data mining (PPDM) in data of large volume. In this paper, we propose a PPDM solution based on principal component analysis (PCA), which can be widely used in computing correlation among sensitive data sets. The general method of computing PCA is to collect all the data spread in multiple nodes into a single node before starting the PCA computation; however, this approach discloses sensitive data of individual nodes, involves a large amount of computation, and incurs large communication overheads. To solve the problem, in this paper, we present an efficient method that securely computes PCA without the need to collect all the data. The proposed method shares only limited information among individual nodes, but obtains the same result as that of the original PCA. In addition, we present a dimensionality reduction technique for the proposed method and use it to improve the performance of secure similar document detection. Finally, through various experiments, we show that the proposed method effectively and efficiently works in a large amount of multi-dimensional data.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.125-130
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• 2008

Multivariate analysis is a rapidly expanding approach to data analysis. One specific technique in multivariate analysis is Principal Component Analysis (PCA). PCA is a statistical technique that linearly transform a given set of variables into a new set of composite variables. These new variables are orthogonal to each other and capture most of the information in the original variables. PCA is used to reduce the number of control points to be checked by measurement system. Therefore, the structure of the data set is simplified significantly It is also shown that eigenvectors obtained by conducting principal component analysis on the basis of the covariance matrix can be used to physically interpret the pattern of relative deformation for the points. This case study reveals the twisting deformation pattern of the underbody which is the largest mode of the total variation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.27-32
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• 2007

Determination of crack depth in filed using the self-calibrating surface wave transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

• MALSORI
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• no.54
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• pp.73-83
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• 2005

The goal of our research is to build a text-independent speaker identification system that can be used in any condition without any additional adaptation process. The performance of speaker recognition systems can be severely degraded in some unknown mismatched microphone and noise conditions. In this paper, we show that PCA(principal component analysis) can improve the performance in the situation. We also propose an augmented PCA process, which augments class discriminative information to the original feature vectors before PCA transformation and selects the best direction for each pair of highly confusable speakers. The proposed method reduced the relative recognition error by 21%.

• Plant Resources
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• v.5 no.3
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• pp.214-223
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• 2002

The objective of this study was to understand the conservation of gene resources and provide information for mass selection' of winter bud characters among the selected populations of Kalopanax septemlobus Koidz using analysis of variance(ANOVA) tests. The obtained results are shown below; 1. Ten populations of K. septemlobus were selected for the study of the variation of winter bud characters in Korea. The results of the analysis of variance(ANOVA) tests shows that there were statistically significant differences in all of the winter bud characters among those populations. 2. Correlation analysis shows that width between Height and DBH(Diameter at breast height) characters have negative relationship with all of the characters, as ABL(Apical branch length), ABW(Apical branch width), AWBL(Apical branch winter bud length), AWBW(Apical branch winter bud width), ABT(Apical branch No. of thorns), ABLB(Apical branch No. of lateral bud) and LBL(Lateral branch length), LBW(Lateral branch width), LBT(Lateral branch No. of thorns), LBLB(Lateral branch No. of lateral bud). 3. The result of principal component analysis(PCA) for winter buds showed that the first principal components(PC' s) to the fourth principal component explains about 78% of the total variation. The first principal component(PC) was correlated with AWBW, LWBW, and LBL and the ratio of ABL/ABW and LBL/LBW out of 16 winter bud characters. The second principal component correlated with ABL, ABW, ABLB, LWBL(Lateral branch winter bud length), and LBW and the ratio of AWBL/AWBW. The third principal component correlated with ABL, ABW, LWBL, LBL, and the ratio of LBL/LBW. The fourth principal component correlated with LBL and the ratio of LWBL/LWBW(Lateral branch winter bud width), LBL/LBW. Therefore, these characters were important to analysis of the variation for winter bud characters among selected populations of K. septemlobus in Korea. 4. Cluster analysis using the average linkage method based on 10 selected populations for the 16 winter bud characters of K. septemlobus in Korea showed a clustering into two groups by level of distance 1.1(Fig. 3). As can be seen in Fig. 3, Group I consisted of three areas(Mt. Sori, Mt. Balwang and Mt. Worak) and Group Ⅱ contisted of seven areas(Suwon, Mt. Chuwang, Mt. Kyeryong, Mt. Kaji, Mt. Jiri, Muan, and Mt. Halla). The result of cluster analysis for winter bud characters corresponded well with principal component analysis, as is shown in Fig. 2.