• Proceedings of the Korean Environmental Sciences Society Conference
• /
• 1997.10a
• /
• pp.1.2-4
• /
• 1997

• Proceedings of the KSCN Conference
• /
• 2001.12a
• /
• pp.945-949
• /
• 2001

• 대한예방의학회:학술대회논문집
• /
• 2003.04a
• /
• pp.22-28
• /
• 2003

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2001.11a
• /
• pp.21-29
• /
• 2001

• Journal of the Ergonomics Society of Korea
• /
• v.25 no.1
• /
• pp.27-33
• /
• 2006

• Computational Structural Engineering
• /
• v.28 no.3
• /
• pp.7-12
• /
• 2015

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.30 no.1
• /
• pp.87-94
• /
• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Radiation Oncology Journal
• /
• v.26 no.4
• /
• pp.289-294
• /
• 2008

Purpose: To improve the quality of the statistical analysis of papers published in the Journal of the Korean Society for Therapeutic Radiology and Oncology (JKOSTRO) by evaluating commonly encountered errors. Materials and Methods: Papers published in the JKOSTRO from January 2006 to December 2007 were reviewed for methodological and statistical validity using a modified version of Ahn's checklist. A statistician reviewed individual papers and evaluated the list items in the checklist for each paper. To avoid the potential assessment error by the statistician who lacks expertise in the field of radiation oncology; the editorial board of the JKOSTRO reviewed each checklist for individual articles. A frequency analysis of the list items was performed using SAS (version 9.0, SAS Institute, NC, USA) software. Results: A total of 73 papers including 5 case reports and 68 original articles were reviewed. Inferential statistics was used in 46 papers. The most commonly adopted statistical methodology was a survival analysis (58.7%). Only 19% of papers were free of statistical errors. Errors of omission were encountered in 34 (50.0%) papers. Errors of commission were encountered in 35 (51.5%) papers. Twenty-one papers (30.9%) had both errors of omission and commission. Conclusion: A variety of statistical errors were encountered in papers published in the JKOSTRO. The current study suggests that a more thorough review of the statistical analysis is needed for manuscripts submitted in the JKOSTRO.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2003.11b
• /
• pp.553-556
• /
• 2003

사회과학 학문을 공부하는 학생들은 현상을 분석하고 미래를 예측하는 기법으로 통계를 많이 사용하고 익혀야 한다. 본 연구에서는 맹목적으로 수치를 다루는 회계 및 통계 학습에서 벗어나 멀티미디어로 흥미를 유발시키고 인터넷상에서 학생들이 직접 실습을 할 수 있는 환경을 구축하여 회계 통계 학습에 도움을 주어 실제 산업체에 적용 가능한 지식을 주고자 한다. 본 연구는 사회과학의 분야에서 어떠한 형태의 자료를 입력하여도 사용자가 바라는 통계를 얻도록 하는데 주안점을 두고 있다. 개발된 멀티미디어 콘텐츠를 통하여 이루어진 학습에 대하여 효과를 측정하여 멀티미디어 기술을 활용한 사회과학 통계교육에 어떤 효과가 있는지를 분석하고자 한다.

• Journal of the Korea Organic Resources Recycling Association
• /
• v.23 no.3
• /
• pp.78-84
• /
• 2015

The aim of this study was to evaluate methane emission flux based on spatial methane concentration using laser methane detector, and geospatial methodology (Inverse distance weighting) at a landfill. The obtained results showed that the spatial methane concentrations were in good agreement with the methane emission fluxes. Thus, it was concluded that the methane emission flux could be derived from spatial methane concentrations. In addition, the results of the geospatial calculations showed that 12.85% of the total area contributed more than 42.21% of total flux. This suggested that the geospatial methodology might be essential in chamber method to determine accurate methane emission fluxes from landfills.