• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.854-859
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• 1994

As powertrain noise is better and better controlled, road noise inputs become more important. The interior road noise of a car is mainly induced by the wheels rolling over the road surface. Each of the four wheels act as an independent and uncorrelated excitation input. To rank the energy transfer form each input to the interior, a Transfer Path Analysis (TPA) needs to be made-which requires operational vibration measurements. However due to the multiple uncorrelated inputs, phase relations vary continuously. It is therefore necessary to separate the operational data into set of "independent phenomena" by means of a Principal Component Analysis (PCA). A TPA can then be carried out for each independent phenomenon. Operational deflection shapes referenced to these principal components share the physical phenomena. The details of the methodology are discussed and a discussion of the results on a car shows that the method gives accurate results for full vehicle testing.e testing.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1586-1593
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• 2020

This study was conducted on the face ratio of mammals. It can also be applied to character automation by checking factors about the difference between animal and human face shapes. This paper used the face and face area data generated for Deep Learning learning. In detail, the proportion factors of the area comprising the faces of 20 species of animals and humans were defined and the average ratio was calculated. Next, the proportion of each animal was analyzed using the Principal Component Analysis (PCA). Through this, we would like to propose the golden ratio of mammals.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Korean Journal of Food Science and Technology
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• v.47 no.5
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• pp.567-573
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• 2015

In this study, volatile compounds in nine commercial Japanese distilled liquors (Shochu) were isolated by headspace solid-phase microexrraction (SPME) and analyzed by gas chromatography (GC) and GC-mass spectrometry (MS). A total of 76 volatile components, including 48 esters, 13 alcohols, and 15 miscellaneous components, were identified. Esters and alcohols constituted the largest groups of quantified volatiles. Differences in volatile components among the distilled liquors and possible sample grouping were examined by applying principal component analyses to the GC-MS data sets. The first and second principal components explained 77.92% of the total variation across the samples. The samples using barley koji showed higher overall concentrations of total volatile components. Additionally, the principal component analysis did not reveal any sample grouping based on the raw material used.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.711-716
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• 2003

This paper proposes the method to select the number of optimal mixtures in each state in Continuous Density HMM (Hidden Markov Models), Previously, researchers used the same number of mixture components in each state of HMM regardless spectral characteristic of speaker, To model each speaker as accurately as possible, we propose to use a different number of mixture components for each state, Selection of mixture components considered the probability value of mixture by each state that affects much parameter estimation of continuous density HMM, Also, we use PCA (principal component analysis) to reduce the correlation and obtain the system' stability when it is reduced the number of mixture components, We experiment it when the proposed method used average 10% small mixture components than the conventional HMM, When experiment result is only applied selection of mixture components, the proposed method could get the similar performance, When we used principal component analysis, the feature vector of the 16 order could get the performance decrease of average 0,35% and the 25 order performance improvement of average 0.65%.

• Journal of the Korean Society of Clothing and Textiles
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• v.14 no.3 s.35
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• pp.208-215
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• 1990

The purpose of this study is to provide the fundamental data of a dummy design for more suitable ready made clothing by making a pattern of somatic types and analyzing their morphological characteristics in accordance with different pattern of somatic types. The side view silhouettes of 90 junior high school girls of age $13\~16$ in seoul urban area were measured by means of the plan photographing and the low data were examined by principal component analysis, while the principal component analysis was applied and three components were extracted and then interpreted to explain to variation of the form of the body. Using three components respectively the cluster analysis was carried out and the subject classified into 4 cluster The following outcomes are obtained. . The results of principal component analysis of this study would be turned out the three; 1) The first principal component shows the degree of erectness or stoop of the figure. 2) The second principal component was a stature length or a growth rate. 3) The third principal component was the obesity component. 2. The results of cluster analysis by using three principal component analysis would be turned out the four cluser; 1) Cluster 1 ($29\%$ of the total) is characterized with lower stature. 2) Cluster 2 ($21\%$ of the total) is characterized with backward somatotype, and the highest leg. 3) Cluster 3 ($23\%$ of the total) is thicked back of neck. 4) Cluster 4 ($27\%$ of the total) is characterized with forward somatotype, and highest stature, height.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.24-33
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• 2004

Advances in remote sensing technologies are resulting in the rapid increase of the number of spectral channels, and thus, growing data volumes. This creates a need for developing faster techniques for processing such data. One application in which such fast processing is needed is the dimension reduction of the multispectral data. Principal component transformation is perhaps the mostpopular dimension reduction technique for multispectral data. In this paper, we discussed the processing procedures of principal component transformation. And we presented and discussed the results of the principal component transformation of the multispectral data. Moreover principal components image data are classified by the Maximum Likelihood method and Multilayer Perceptron method. In addition, the performances of two classification methods and data reduction effects are evaluated and analyzed based on the experimental results.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.285-292
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• 2008

Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.11-17
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• 2008

Typical wireless sensor networks used in structural monitoring are continuous types wherein data transmission is progressive at all time that may include irrelevant and insignificant data and information. Continuous types of wireless monitoring systems often pose problems of handling large-sized data that may deteriorate the performance of the system. The proposed method is to suggest an event-triggered monitoring system that captures and transmits relevant data only. An error signal generated by the Principal Components Analysis (PCA) is utilized as an index for event detection and selective data transmission. With this new monitoring scheme, the remote server is relieved of unwanted data by receiving only relevant information from the wireless sensor networks. The performance of the proposed scheme was verified with simulation studies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.7 s.124
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• pp.638-648
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• 2007

A principal components analysis of the entire median HRIRs in the CIPIC HRTF database reveals that the individual HRIRs can be adequately reconstructed by a linear combination of several orthonormal basis functions. The basis functions represent the inter-individual and inter-elevation variations in median HRIRs. There exist elevation-dependent tendencies in the weights of basis functions, and the basis functions can be ordered according to the magnitude of standard deviation of the weights at each elevation. We propose a HRIR customization method via tuning of the weights of 3 dominant basis functions corresponding to the 3 largest standard deviations at each elevation. Subjective listening test results show that both front-back reversal and vertical perception can be improved with the customized HRIRs.