• Journal of the Korean Magnetics Society
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• v.25 no.6
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• pp.203-207
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• 2015

This paper introduces a probabilistic analysis method in order to analyze the effect of manufacturing tolerance on induction motor performance occurring in massive production. The univariate dimension reduction method is adapted to predict probabilistic characteristics of a performance function due to certain probabilistic distributions of design variables. Moreover, the sensitivity information on mean and variance of the performance function is estimated, and then the effect of randomness of individual design variables on the probability performance function is analyzed. The effectiveness and accuracy of the method is investigated with a mathematical model and an induction motor.

• The Korean Journal of Financial Studies
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• v.13 no.1
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• pp.111-131
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• 2007

본 연구에서는 한국과 미국 두 국가에 있어 이자율 스왑시장간의 정보전달 메커니즘에 대해 분석하였다. 이를 위하여 데이터로 2003년 초부터 2006년 말까지 4년간 Bloomberg에서 집계된 3년물, 5년물, 10년물 이자율 스왑금리를 사용하였으며, 메커니즘의 동태 분석은 VAR 모형을 사용하였다. 분석 결과, 그랜저 인과관계 검정, 충격반응함수 분석 및 분산분해 분석 모두 결과적으로 미국 이자율 스왑시장의 정보가 국내 이자율 스왑시장에 상당한 영향력을 가진다는 사실을 알 수 있었다. 또한 이러한 미국 시장의 국내 시장으로의 정보의 전이 현상은 3년물, 5년물, 10년물 이자율 스왑에 같이 나타나는 현상으로 스왑계약 기간에 상관없음이 관측되었다. 한편, 충격반응함수 분석 결과, 미국의 이자율 스왑시장의 충격은 국내 이자율 스왑시장에 다음 날 바로 유의한 영향을 주는 것으로 나타났으며 그 충격은 2일간 지속되었다. 반면 국내 이자율 스왑시장의 정보는 미국 시장에 별 영향력을 발휘하지 못했다.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.189-212
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• 2005

This study examines the interactions between KTB spot and futures markets using the daily prices from March 4, 2002 to January 31, 2005. We use Granger causality test, impulse Response Analysis and Variance Decomposition through vector autoregressive analysis (VAR). However, considering the long-term relationships between the level variables of KTB spot and futures, we introduced Vector Error Correction Model. The main results are as follows. According to the results of Granger-causality test and impulse response analysis, we find that the yields of KTB forward have a great influence on the change of KTB spot but not vice versa. In terms of volatility analysis, there is no inter-dependence between KTB forward and spot markets. In the variance decomposition analysis we find that the short-term KTB forward has much more impact on the KTB spot market than the long-term KTB forward does. We think these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management and international portfolio management.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.131-135
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• 2007

참여의 멱함수 이론은 UCC를 생산, 배포, 소비, 재생산하는 과정에 참여하는 사용자들을 분석하는 유용한 도구이다. 참여의 멱함수 이론을 통해 무리는 집합적 지성과 집단 지성을 구분하고 참여의 형태를 세분화하며 참여자간의 관계 분석할 수 있다. 즉 다양한 기술, 사회, 정치 현상들에 배경 정보가 되어 영향력을 행사하는 UCC는 어떤 이유로 제작되고 어떤 형태로 나누어지며 생산한 사람들 간의 관계 또는 생산자와 생산자 고리고 생산자와 소비자 간의 관계를 설명할 때 설득력들 높인다. UCC가 웹 2.0의 어떤 기술 속에 생산되고 배포되고 소비되는지에 대한 플랫폼의 설계에도 멱함수 법칙은 도움을 주는 도구이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.858-860
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• 2019

본 논문에서 우리는 범함수 회귀모형을 이용한 양파의 성장단계별 무게를 예측할 수 있는 새로운 통계적 추정방법을 제안한다. 여기서 우리는 풍속, 평균온도, 강우량, 일조량 그리고 습도 등 나타내는 환경요인들을 설명변수들로 사용하고, 양파의 성장단계별 무게를 반응변수로 사용하여 범함수 회귀모형을 적용하였다. 먼저 그래프분석과 상관분석을 통하여 우리는 일일 평균온도는 양파의 무게 증진에 가장 큰 양의상관이 있고, 풍속이나 습도 그리고 일조량들은 양파의 성장에 약간의 영향력이 있으며 강우량은 양파의 성장에 전혀 도움이 안됨을 알 수 있었다. 두 번째로 범함수 회귀 분석을 통하여 얻어진 각 환경요인들에 대한 회귀계수들의 그림을 통하여 우리는 양파의 성장 기간 동안에 이들의 무게를 향상시키기 위해서는 어떻게 환경요인들을 관리해야 되는 가를 알 수 있는 재배방법을 유도하였다.

• Journal of Information Management
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• v.40 no.4
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• pp.135-150
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• 2009

Existing bibliometric laws have been established on the basis of well defined science journals with a long history. However, the history of technology innovation research in Korea is young and the scope of the research is diverse compared with other fields. The main purpose of this research can be summarized as follows : Can the traditional bibliometric laws be used to explain the young and diverse data derived from technology innovation studies in Korea. Second, we want to compare the explain ability of the power law, compared with the traditional laws in the field. Third, we propose a quasi index related to the well-known impact factor to measure the contribution of a journal or a group of journals to the development of innovation research in Korea. We confirmed Lotka's and Bradford's laws which are used to measure the productivity of researchers, but we could not support the validity of Price's Square Root law as Nicholls (1998) could not. On the citations to journals, Garfield's laws is not observed. However, the power law fits well the citations to author, journal, article, and book. The estimated parameters between 1.6 and 3.5 are similar to the values in the range of 1.5 and 3 in previous studies. Finally the quasi index shows that the influence of international leading journals on innovation research in Korea is weaker than on innovation studies in the world.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.257-262
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• 1996

When we are estimate the smoothing parameter in spline regression model, we deal the diagnostic of influence observations as posteriori analysis. When we use Generalized Maximum Likelihood Function as the estimation method of smoothing parameter, we propose the diagnostic measure for influencial observations in the obtained estimate, and we introduce the finding method of the proper smoothing parameter estimate.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Journal of the Korean Regional Science Association
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• v.35 no.1
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• pp.47-57
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• 2019

Daegu Metropolitan City has been continuously carrying out core functions of Yeongnam region, and especially plays a role as export base of textile and chemical products in Korea. Also Daegu Metropolitan City has contributed greatly to the expansion of Korea's import and export trade and the growth of the national economy. The purpose of this study is to analyze the influence of major factors affecting GRDP in Daegu Metropolitan City through regression analysis. For this purpose, this study uses the Vector Error Correction Model(VECM) to estimate the long-run equilibrium function that affects the GRDP in Daegu Metropolitan City. This study is meaningful in that it uses the statistics related to Daegu provided by Province of Gyeongsangbuk-do and explains the dynamic characteristics of major factors affecting the GRDP in Daegu.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.