• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.805-812
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• 2020

High-frequency data are now prevalent in financial time series. As a functional data arising from high-frequency financial time series, we are concerned with the intraday volatility to which functional principal component analysis (FPCA) is applied in order to achieve a dimension reduction. A review on FPCA and R function is made and high-frequency KOSPI volatility is analysed as an application.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.65-70
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• 2007

Red tide is becoming hot issue of environmental problem worldwide since the 1990. Advanced nations, are progressing study that detect red tide area on early time using satellite for sea. But, our country most seashores bends serious. Also because there are a lot of turbid method streams on coast, hard to detect small red tide area by satellite for sea that is low resolution. Also, method by sea color that use one feature of satellite image for sea of existent red tide area detection was most. In this way, have a few feature in image with sea color and it can cause false negative mistake that detect red tide area. Therefore, in this paper, acquired texture information to use GLCM(Gray Level Co occurrence Matrix)'s texture 6 information about high definition land satellite south Coast image. Removed needless component reducing dimension through principal component analysis from this information. And changed into 2 principal component accumulation images, Experiment result 2 principal component conversion accumulation image's eigenvalues were 94.6%. When component with red tide area that uses only sea color image and all principal component image. displayed more correct result. And divided as quantitative,, it compares with turbid stream and the sea that red tide does not exist using statistical feature analysis about texture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1461-1470
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• 2016

In side channel analysis, signal processing techniques can be used as preprocessing to enhance the efficiency and performance of analysis by reducing the noise or compressing the dimension. As signal processing techiniques using singular value decomposition can increase the information of main signal and reduce the noise by using the variance and tendency of signal, it is a great help to improve the performance of analysis. Typical techniques of that are PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) and SSA(Singular Spectrum Analysis). PCA and LDA can compress the dimension with increasing the information of main signal, and SSA reduces the noise by decomposing the signal into main siganl and noise. When applying each one or combination of these techniques, it is necessary to compare the performance. Therefore, it needs to suggest methodology of that. In this paper, we compare the performance of the three technique and propose using Sinal-to-Noise Ratio(SNR) as the methodology. Through the proposed methodology and various experiments, we confirm the performance and efficiency of each technique. This will provide useful information to many researchers in the field of side channel analysis.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.521-528
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• 2002

We propose a new approach to the feature selection for the classification of handwritten characters using two-dimensional(2D) wavelet packet bases. To extract key features of an image data, for the dimension reduction Principal Component Analysis(PCA) has been most frequently used. However PCA relies on the eigenvalue system, it is not only sensitive to outliers and perturbations, but has a tendency to select only global features. Since the important features for the image data are often characterized by local information such as edges and spikes, PCA does not provide good solutions to such problems. Also solving an eigenvalue system usually requires high cost in its computation. In this paper, the original data is transformed with 2D wavelet packet bases and the best discriminant basis is searched, from which relevant features are selected. In contrast to PCA solutions, the fast selection of detailed features as well as global features is possible by virtue of the good properties of wavelets. Experiment results on the recognition rates of PCA and our approach are compared to show the performance of the proposed method.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.2
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• pp.225-242
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• 2006

This study aims to analyze the spatial distribution pattern of international trade. The method is to analyze the principal components by changing interaction attribute matrix of four dimensions (hinterland, gateway, foreland and commodities) into two dimension matrix. The study area is the territory region of Cheongju clearance depot in inland. The result are as follows : Major spatial patterns of regional connections by hinterland, gateway and foreland are, in the case of exports, ten patterns and in the case of imports come to nine. Composition of major export and import commodities in Cheongju clearance depot are similar, but precision instrument manufactured commodity and nonmetal mineral are remarkable in export and mineral manufactured commodity machinery and electronic manufactured commodity are remarkable in import. Gateway are similar to export and import, but Incheon international airport is used more in the case of import. And Cheongiu international airport is used for some commodities and is remarkable as a foreland of import for the areas outside of Chungcheongbuk-do.

• Journal of the Korean Wood Science and Technology
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• v.45 no.1
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• pp.20-27
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• 2017

The aim of this paper is to present the modulus of elasticity of $E_L$, $E_R$, $E_T$ along three principal axis of softwood dimension lumber by nondestructive method. Ultrasonic measurement was carried out on defect free wood samples taken by the Japanese Larch, SPF (spruce-pine-fir) and Hem-fir $2{\times}4s$. The ultrasound velocities were measured to calculate young's moduli and it was derived elastic constants for each wood samples using the ultrasound velocities and densities of wood. From the test, $E_L$ was much greater than $E_R$ and $E_T$. $E_R/E_T$ ratios were about 1.3. The high density wood had high young's moduli in three principal axis and the difference in young's moduli between species was greater in transverse direction than longitudinal direction. The anisotropy of the lumber was presented through the calculated elastic moduli and compliances matrix in diagonal term were determined by inverting the stiffness matrix.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.253-262
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• 2000

This paper proposes a new RBF neural network that determines the number and the center of hidden neurons based on the adaptive feature extraction for the input data. The principal component analysis is applied for extracting adaptively the features by reducing the dimension of the given input data. It can simultaneously achieve a superior property of both the principal component analysis by mapping input data into set of statistically independent features and the RBF neural networks. The proposed neural networks has been applied to classify the 200 breast cancer databases by 2-class. The simulation results shows that the proposed neural networks has better performances of the learning time and the classification for test data, in comparison with those using the k-means clustering algorithm. And it is affected less than the k-means clustering algorithm by the initial weight setting and the scope of the smoothing factor.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.452-456
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• 2003

We discuss how to reduce the number of inverse matrix and its dimensions requested in MLLR framework for speaker adaptation. To find a smaller set of variables with less redundancy, we adapt PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible. The amount of additional computation when PCA or ICA is applied is as small as it can be disregarded. 10 components for ICA and 12 components for PCA represent similar performance with 36 components for ordinary MLLR framework. If dimension of SI model parameter is n, the amount of computation of inverse matrix in MLLR is proportioned to O(n⁴). So, compared with ordinary MLLR, the amount of total computation requested in speaker adaptation is reduced by about 1/81 in MLLR with PCA and 1/167 in MLLR with ICA.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.11a
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• pp.7-12
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• 2002

The motor boat which is the principal equipment of marine leisure depends heavily an imported motor boats in Korea But because the demand of marine leisure and motor boat increases, it is necessary to develope a cheap and safe domestic-manufactured motor boat. However the development of motor but for marine leisure is not performed at all in Korea in this paper, we consider the concept design of public motor but for marine leisure. The present status and possession prediction of leisure boat are investigated at first. And the benchmarking of the overseas motor bout for marine leisure is performed. And next the required performance, the range of principal dimension and the needed equipment of the public motor boat are investigated based on the benchmarking results of the overseas motor boat. Finally, the example of hull form design for Public motor boat is showed