• 동의신경정신과학회지
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• 제30권3호
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• pp.153-164
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• 2019

Objectives: The purpose of this study was to examine the correlation between CSEI (The Core Seven-Emotions Inventory) and MMPI-2 (Minnesota Multiphasic Personality Inventory-2). Methods: We analyzed the correlation between students' demographic characteristics, blood type, MMPI-2, and CSEI using the SPSS (Statistical Package for the Social Science) 24.0. Descriptive Statistical Analysis, independent t-test, analysis of variance (ANOVA), subsequent analysis duncan (post hoc multiple comparison), and correlation analysis were conducted. Results: 1. According to the demographic characteristics of 91 participants in this study: 60 males (65.9%), 88 unmarried (96.7%), 58 without religion (63.7%), 54 who have experienced stress in the last three months (59.4%), and 82 in their 20s (90.1%) comprised the majority. 2. As a result of verifying correlation by subfactors of CSEI, Hui (喜) showed statistically significant negative correlation with U (憂), Bi (悲), and Gong (恐). Six emotions except Hui (喜) showed statistically significant positive correlation, except for the relationship between U (憂) and Kyeong (驚). 3. Hui (喜) of CSEI had negative correlation with eight factors of MMPI-2 Clinical Scales except Pa (Paranoia) and Ma (Hypomania), whereas Bi (悲) had positive correlation with nine factors of MMPI-2 clinical scales except Mf (Masculinity-Femininity). Sa (思), Bi (悲), and Gong (恐) had similar tendency of positive correlation with six factors of MMPI-2 Restructured Clinical Scales. Conclusions: Based on the above results, we concluded that CSEI's sub factors had consistent correlations with MMPI-2. Thus, CSEI could contribute to psychiatry clinical use.

• 정보보호학회논문지
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• 제17권4호
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• pp.89-95
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• 2007

상관면역함수 f를 만드는 방법으로 Siegenthaler의 방법, Camion의 방법과 Seberry의 방법 등이 있다. 이 중에서 Seberry의 방법은 Hadamard 행렬이론을 이용하여 상관면역함수를 만드는 것으로, 임의의 상관면역도의 균형상관면역함수를 만드는 방법을 제공하였다. 본 논문에서는 저차원의 벡터공간에서 만들어진 여러 개의 상관면역함수를 조합하여 고차원 벡터공간위에서 상당히 비도가 높은 함수를 설계하는 Seberry의 방법들을 연구하였고, 그 함수들의 비선형도를 계산하였다. 즉, 두 개의 함수의 직합으로 설계된 새로운 상관면역함수와 네 개의 함수의 조합으로 설계된 새로운 상관면역함수의 비선형도가 각각의 이전 함수들과 비교하여 더 높은 비선형도를 갖는다는 것을 보였다. 또한 위의 방법을 응용하여 상대적으로 비도가 높은 상관공격으로부터 안전한 스트림암호에서 사용되는 함수들을 설계하였다.

• 한국의류학회지
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• 제11권1호
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• pp.29-42
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• 1987

This study was an attempt to survey, the relationships between women's values and preference in clothing design. For the measurement of values scale was to relect the theoretical, economic aesthetic, social. political and religious values explained by Allport-vernon- Lindzey. The preference rate in clothing design consisted of 50 items and 3 factors including color, form and texture. The questionnaire were administered to a sample of women (between college students and adult) who lined in Seoul, Korea. The date for 910 respondents were analyzed by person's carrelation coefficient, to-test and $x^2$ test. Through this study, the followings were founds; 1. Aesthetic value was the mast important of female college students values and economic value was the most important of adult's values. 2. There were significant relation between values and the preference for clothing form in line and style; 1) In line preference, adult women indicated negative correlation with religious values but positive correlation with aesthetic and political values. 2) In style preference, adult women indicated negative correlation with economic values positive correlation with social values, for female college students indicated positive correlation with aesthetic social and political values. 3. There were significant relation between values and preference for texture in touch, thickness, weight, light and luster; 1) In relationship between texture and values, adult women indicated touchness preference negative correlation with theoretical and religious values, for female college students indicated positive correlation with aesthetic and social values. 2) In thickness preference adult women indicated negative correlation with theoretical values but female college students indicated positive correlation with political values. 3) In weight preference, adult women indicated negative correlation with theoretical values. But female college students indicated positive correlation with religious values. 4) In brightness preference, only adult women indicated positive correlation with social values, negative correlation with economic values. In transparency preference, adult women indicated positive correlation with aesthetic values, negative correlation with religious values. 4. There were significant correlation among color and economic, aesthetic; political and religious values, that is, evacuation and economic political values in adult women there were positive correlation with religious values in female students. 1) In chroma only adult women indicated negative correlation with religious values. In warm and cold, positive correlation with economic values in both of them. 2) In coloration, adult women like a complement color indicated negative correlation with aesthetic values, but positive correlation with political values. 5. There were significant differences in the preference of clothing design between female college students and adult women; correlationship with color variables. Evacuation, both of them indicated negative correlation with chroma positive correlation with warm and cold and coloration. In chroma, adult women indicated negative correlation with coloration and warm and cold, for female students indicated negative correlation with coloration. 6. Comparison with clothing preference: 1) In color preference, adult women liked the most white, brown, blue and black in sequence. Female college students liked the most white, also the next is blue and pink. 2) Clothing design preference, there was significant difference in warm and cold and coloration, adult women more liked than female students. In shape of clothing, both of them indicated significant difference in line and style, female students more liked sporty style. In texture, adult women more liked soft thin light weight and bright. 3) In silhouette preference, the most is H-line, female college students more liked.

• 설비공학논문집
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• 제19권6호
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• pp.457-463
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• 2007

Various correlations of saturated vapor density in a truncated power series form are tested and compared in this study. Saturated vapor density correlation can be expressed relating logarithmic reduced density to the reduced temperature. Five types of correlation has been investigated using saturated vapor density data for 22 pure substance refrigerants from ASHRAE (American Society of Heating, Reftigerating and Air-Conditioning Engineers, Inc.) property tables and NIST (National Institute of Standards and Technology) Chemistry Webbook. Correlations are fitted to the data points by least squares method. Data points are equally weighted. The best type of correlation among the five types is suggested. The results obtained indicate that the best correlations with 3, 4, and 5 terms yield average AAD's (Average Absolute Deviation) of 0.27%, 0.04%, and 0.02%, respectively, while widely used conventional correlations with 3, 4, and 5 terms yield those of 1.19%, 0.61%, and 0.17%. The suggested type of correlation could reduce the number of terms while improving performance.

• 전기전자학회논문지
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• 제22권3호
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• pp.810-815
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• 2018

커널 상관 필터 알고리듬은 객체 추적에 대해 정확도에서 의미 있는 성과를 거두었다. 그러나 고정된 크기의 템플릿을 사용하기 때문에 추적 대상의 스케일 변화에 대처할 수 없었다. 본 논문에서는 최근접 보간법과 표준 가우시안 정규화를 이용한 다중 스케일에서의 상관 필터링 응답 값을 이용하여 프레임별로 가장 적합한 스케일을 찾아 객체를 추적하는 방식을 제안한다. 다음 프레임의 스케일 값들은 이전 프레임의 최적 스케일 값을 이용해 갱신하고 다시 해당 프레임에서의 최적의 스케일 값을 찾는다. 정확도 비교를 위해 기존 커널 상관 필터 알고리듬에서 사용된 VOT2014 데이터를 사용하여 제안된 방법의 유효성을 검증한다.

• 한국임상수의학회지
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• 제27권4호
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• 한국대기환경학회지
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• 제22권3호
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• pp.383-391
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• 2006

In Korea, the major source of serious air pollution is motor vehicles. Air pollution from vehicles has been annually increased. Then the government will try to control the vehicle emission by applying the effective emission management policy for the manufactured and in-used car. It is necessary to correctly calculate the emission factor for successful propulsion of the vehicle emission control policy. In this study, correlation analysis of exhaust emissions from vehicles between CVS-75 mode and Korea mode was conducted. A total of 25 light-duty buses were tested on the chassis dynamometer system in order to measure CO, HC, NOx PM and fuel efficiency (F.E.). For the test modes, 10 different Korea modes and CVS-75 mode were used. As the result of correlation analysis between those modes, most of the correlation coefficients were higher than 0.90. On the basis of high correlation between those modes, correction factors by driving conditions were estimated. Through the results of this study, we obtained essential basic data to correct difference from those modes.

• 한국환경과학회지
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• 제24권4호
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• pp.403-414
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• 2015

This study is daytime and nighttime runoff image data caused by heavy rain on May 27, 2013 at Oedo Water Treatment Plant of Oedo-Stream, Jeju to compute runoff by applying Surface image velocimeter (SIV) and analyzing correlation according to current. At the same time, current was comparatively analyzed using ADCP observation data and fixed electromagnetic surface current meter (Kalesto) observed at the runoff site. As a result of comparison on resolutions of daytime and nighttime runoff images collected, correlation coefficient corresponding to the range of 0.6~0.7 was 6.8% higher for nighttime runoff image compared to daytime runoff image. On the contrary, correlation coefficient corresponding to the range of 0.9~1.0 was 17% lower. This result implies that nighttime runoff image has lower image quality than daytime runoff image. In the process of computing current using SIV, a rational filtering process for correlation coefficient is needed according to images obtained.

• 천문학회보
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• 제41권1호
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• pp.79.3-80
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• 2016

We have investigated the correlation analysis between global temperature anomaly and two main factors: geomagnetic activity (aa index) of Earth external factor and CO2 of Earth internal factor. For this, we used NOAA Global Surface Temperature anomaly (Ta) data from 1868 to 2015. The aa index indicates the geomagnetic activity measured at two anti-podal subauroral stations (Canberra Australia and Hartland England) and the CO2 data come from historical ice core records and NOAA/ESRL data. From the comparison between (Ta) and aa index, we found several interesting things, First, the linear correlation coefficient between two parameters increases until 1985 and then decreases rapidly. Second, the scattered plot between two parameters shows a boundary of the correlation tendency (positive and negative correlation) near 1985. A partial correlation of (Ta) and two main factors (aa index, CO2) also shows that the geomagnetic effect (aa index) is dominant until about 1985 and the CO2 effect becomes much more important after then. These results indicate that the CO2 effect become very an important factor since at least 1985. For a further analysis, we simply assume that Ta = Ta(aa)+Ta(CO2) and made a linear regression between (Ta) and aa index from 1868 to 2015. A linear model is then made from the linear regression between energy consumption (a proxy of CO2 effect) and Ta-Ta(aa) since 1985. Our results will be discussed in view of the prediction of global warming.

• 대한원격탐사학회지
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• 제25권3호
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• pp.263-270
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• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.