• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.159-164
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• 1993

초음파 흡수에 의한 단백질에서의 온도 변화 특성을 관측하기 위하여, 간단한 조직의 형태인 계란 흰자와 계란 흰자의 주성분인 물과 알부민 수용액을 시료로 선택하여, 초음파 흡수에 의한 온도 변화 특성을 물리 음향학적으로 관측하였다. 수 MHz의 초음파를 집속형 변환기에서 연속파의 형태로 시료에 투사하였을 때, 한정된 시료에서의 온도 변화를 측정하였다. 측정결과, 물과 알부민 성분에 의한 온도 증가 효과는 미미함을 알 수 있었다. 초음파 흡수에 의한 계란 흰자에서의 온도 변화는 단백질의 주성분인 알부민에 의한 효과보다는, 단백질을 구성하고 있는 각 성분들의 결합에 의한 효과가 큼을 알 수 있다. 이들 결과에 대한 이론적 검토로부터 초음파 흡수에 의한 인체내 온도 변화 및 부작용에 대한 초음파 강도의 안전기준의 토대 마련이 가능함을 제시하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.113-118
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• 1996

Principal component analysis(PCA) is an essential technique for data compression and feature extraction, and has been widely used in statistical data analysis, communication theory, pattern recognition, and image processing. Oja(1992) found that a linear neuron with constrained Hebbian learning rule can extract the principal component by using stochastic gradient ascent method. In practice real data often contain some outliers. These outliers will significantly deteriorate the performances of the PCA algorithms. In order to make PCA robust, Xu & Yuille(1995) applied statistical physics to the problem of robust principal component analysis(RPCA). Devlin et.al(1981) obtained principal components by using techniques such as M-estimation. The propose of this paper is to investigate from the statistical point of view how Xu & Yuille's(1995) RPCA works under the same simulation condition as in Devlin et.al(1981).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.6
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• pp.642-653
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• 2008

This study deals with modeling of head-related transfer functions(HRTFs) using principal components analysis(PCA) in the time and frequency domains. Four PCA models based on head-related impulse responses(HRIRs), complex-valued HRTFs, augmented HRTFs, and log-magnitudes of HRTFs are investigated. The objective of this study is to compare modeling performances of the PCA models in the least-squares sense and to show the theoretical relationship between the PCA models. In terms of the number of principal components needed for modeling, the PCA model based on HRIR or augmented HRTFs showed more efficient modeling performance than the PCA model based on complex-valued HRTFs. The PCA model based on HRIRs in the time domain and that based on augmented HRTFs in the frequency domain are shown to be theoretically equivalent. Modeling performance of the PCA model based on log-magnitudes of HRTFs cannot be compared with that of other PCA models because the PCA model deals with log-scaled magnitude components only, whereas the other PCA models consider both magnitude and phase components in linear scale.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.431-445
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• 2006

In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.

• Journal of Korea Game Society
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• v.13 no.2
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• pp.59-70
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• 2013

Although the history of the game of Go is more than 2,500 years, the theoretical studies of Go are still insufficient. In recent years a lot of studies using Artificial Intelligent(AI) have been conducted, but they do not provide the prominent theoretical reality. We applied Principal Component Analysis(PCA) to the professional Go openings, which are the early stage in Go, to analyze them especially focused on the Go game records of the professional 9-dan player Lee Sedol who is the world's top professional Go player. The results showed that among the 361 eigenvectors the 48 most significant eigenvectors capture most of the variance (99.9%) and the 30 most significant eigenvectors enable to possess 90.5 percent of the total variance. This result would be expected to considerably contribute to pattern recognition research of the professional Go openings in the near future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.255-263
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• 2016

This study conducted empirical tests on contemporary finance theories for corporate cash holdings, such as trade-off, pecking order, and agency theory. There is ongoing debate on the possibility of excess cash savings by domestic firms, including chaebols in the Korean capital markets. Thus, it may be worthy to identify any financial characteristics based on each aforementioned theory as an extension of previous studies on similar subjects. Two primary hypotheses were postulated and tested, and the following empirical results were obtained. First, principal component analysis (PCA) provides evidence that nine out of the twenty explanatory variables showed a significant influence on the level of corporate cash holdings, such as cash conversion cycle in trade-off theory and leverage in pecking order theory. Second, the chaebol firms that decreased cash holdings after global financial turmoil may be affected by financial factors that include investment opportunities and foreign ownership according to the PCA. The results may reinforce the outcomes derived from previous research on corporate cash holdings. Based on the robust results, large firms in advanced or emerging capital markets could approach the optimal level of the cash reserves.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.437-444
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• 2012

This study features an analysis of the socio-economic factors of ten cities on the Honam-line that affect the number of train passengers. The 3 main factors based on the principal component analysis were the population, the distance between two regions, and the area size of each region while the number of automobiles has been conventionally used instead of the area size of each region. A formula to predict the train passengers by the regression analysis was developed and showed a good agreement to the number of real passengers. When Honam highspeed railway is opened, the traveling time between two regions as well as the area size of each regions should be more precisely considered.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.207-229
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• 1995

The characteristics of weather and climate in South Korea has great influences on the annual variation pattern and the appearance of the prevailing weather. The purpose of this paper is to induce the quantity of the weather entropy and annual variation pattern using the information theory and the principal component analysis. And author tried to classify the region according to the variation of its space scale, The raw materials used for this study are the daily cloudiness and precipitation during the years 1990-1994 at 69 stations in South Korea. It is divided into four classes of fine, clear, cloudy and rainy. The rcsults of this study can be summarized as follows: 1. Thc characteristics of annual variation pattern of weather entropy can be chiefly divided into five categories and the accumulated contributory rate of these is 73.1%. 2. Annual variation pattern of the first principal component reaches smaller in May, April and September than national average, and becomes greater when the winter comes. This weather entropy's quantity(Rs1) is positive in most area to the western sife of Soback Mountains and negative in most seaside area to the eastern side of Soback Mountains. 3. The characteristics of annual variation pattern of the second principal component shows that the entropy is more smaller in summer than national average and the rest of seasons shows larger, especially in January, May and September. This weather entropy's quantity(Rs2) is positive in most Honam Inland area to the western side of Soback Mountains and negative in most Youngnam Inland area to the eastern side of Soback Mountains. 4. Eight type regions (S1-S11) are classified based on the occurrences of minimum weather entropy in South Korea, and annual variation pattern of weather entropy by principal component analysis may be classified into sixteen type regions (Rs1-Rs9). Putting these things together, South Korea can be classifieed into thirty one type regions (Rs1S7-Rs9S10).

• Journal of the Korean Geographical Society
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• v.33 no.1
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• pp.17-40
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• 1998

The purpose of this study is to classify climatic environment according to its characteristics in Korea using GIS. The necessary condition of climatic division is that it is able to indicate climatic phenomena systematically and it has scientific persuasive power. Precipitaiton, rainfall days, temperature and weather entropy which are consist of Korean climatic elements are of advantage to indicate climatic phenomena systematically. GIS(Geographic Information System)has scientific persuasive power. This paper shows the time-spatial variations of each climatic elements, using GIS to precipitation, rainfall days, Temperature and weather entropy in Korea. And writers tried to know these regional characteristics and to divide the detailed climatic environment objectively and systematically. The main result of this study is that the regional division of climatic environment in Korea can be classified into 8 types, in details, 26 or 48 types.