• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.1-9
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• 1987

역사적으로 통계학(statistics, statistik)의 본래의 뜻은 통치를 위한 사회 및 경제현상의 수량적 파악이다. 어쨌든간에 본 논문의 과제는 경제통계학(또는 경제통계론)의 과학으로서의 위치 즉 경체통계학의 내용, 과제 및 방법을 밝히는데 있다. 간단히 말하면 경제통계학은 경제활동의 집단적 현상을 수량적으로 표현하고 이를 분석하는 것이다. 즉 인간의 욕망을 충족시키기 위한 경제활동의 모든 현상, 예를 들면 생산과정, 분배과정, 또는 소비과정 등의 집단적 현상을 수적으로 기술하고 분석하는 것이 되겠다.

• The Korean Journal of Applied Statistics
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• v.2 no.2
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• pp.1-8
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• 1989

The survey results about curriculum of statistics department in Korea has been summarized. Because of the scientific speciality, curriculums of statistics have different forms depending on universities. So summarization of curriculums will be helpfull for the case of renewal of the curriculum or organizing the curriculum in the new statistics department.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.359-371
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• 2004

The goal of this article is to examine the problems and crisis of statistics departments, and to explore the means of settlement.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.207-212
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• 2003

21세기 정보화, 기술, 국제화 등 시대적 성격에서 볼 때 국가적 차원의 통계적 소양 교육은 일반 시민의 기본 소양 및 인적자원 개발의 문제임을 인식하고 국가 그리고 대학의 일반 교육 정책으로 다를 필요가 있다. 현재 대학 교육에서 소외되고 사각지대에 있는 기초통계학 교육을 사회 변화와 요구를 반영할 수 있는 교육이 시급한 과제이다. 본 논문은 대학 기초통계학의 현재의 대학교육 상황, 학생들의 교육적 배경, 통계적 소양과 시대적 의미, 기초통계학의 목표, 성격 그리고 방향들을 제시하였다.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.575-578
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• 2021

The author reviews the past 40-year activities on Korean translation of statistical terms within the Korean Statistical Society since 1980's and proposes a number of recent terms of statistical machine learning.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.657-668
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• 2023

It's important to note that air compressors in the industrial sector are major energy consumers, accounting for a significant portion of total energy costs in manufacturing plants, ranging from 12% to 40%. To address this issue, researchers have compared forecasting models that can predict the power consumption of air compressors. The forecasting models were designed to incorporate variables such as flow rate, pressure, temperature, humidity, and dew point, utilizing statistical methods, machine learning, and deep learning techniques. The model performance was compared using measures such as RMSE, MAE and SMAPE. Out of the 21 models tested, the Elastic Net, a statistical method, proved to be the most effective in power comsumption forecasting.

• The Journal of Bigdata
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• v.5 no.2
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• pp.145-153
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• 2020

This study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and is based on artificial intelligence-based HPAI spread analysis and patterning. Highly Pathogenic Avian Influenza (HPAI) is coming from abroad through migratory birds, but it is not clear exactly how it spreads to farms. In addition, it is assumed that the main cause of the spread is the vehicle, but the main cause of the spread is not exactly known. However, it is necessary to analyze the relationship between the vehicles and the facilities at the farms where they occur, as the type of vehicles that visit the farms most frequently is between farms and facilities, such as livestock transportation and feed transportation. In this paper, based on the Korea Animal Health Integrated System (KAHIS) data provided by Animal and Plant Quarantine Agency, the main cause of HPAI virus transfer is to be confirmed between vehicles and facilities.

• The Journal of Bigdata
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• v.5 no.2
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• pp.69-76
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• 2020

This study was conducted with the support of the Information and Communication Technology Promotion Center, funded by the government (Ministry of Science and ICT) in 2019. Highly pathogenic avian influenza (HPAI) is an acute infectious disease of birds caused by highly pathogenic avian influenza virus infection, causing serious damage to poultry such as chickens and ducks. High pathogenic avian influenza (HPAI) is caused by focusing on winter rather than year-round, and sometimes does not occur at all during a certain period of time. Due to these characteristics of HPAI, there is a problem that does not accumulate enough actual data. In this paper study, GAN network was utilized to generate actual similar data containing missing values and the process is introduced. The results of this study can be used to measure risk by generating realistic simulation data for certain times when HPAI did not occur.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.1009-1011
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• 2022

최근 미세먼지는 중대한 건강위험요소로 고려되고 있고, 미세먼지 취약계층은 이에 대한 적극적 대응이 필요하다. 그러나 현재의 대기환경지수는 세분화 되어있지 않아 본 논문에서는 위해성 평가와 GAM 모형을 기반으로 건강취약계층 대상을 위한 미세먼지 위험지수를 새롭게 개발하였다. 또한, 이에 따라 실내 및 실외활동을 추천하는 시스템을 구현함으로써 생활밀착형 스마트시티로 발돋움하도록 한다.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.777-789
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• 2020

COVID-19 has been rapidly spread world wide since late December 2019. In this paper, our interest is to estimate distribution of incubation time defined as period between infection of virus and the onset. Due to the limit of accessibility and asymptomatic feature of COVID-19 virus, the exact infection and onset time are not always observable. For estimation of incubation time, interval censoring technique is implemented. Furthermore, a competing risk model is applied to estimate the case fatality and cure fraction. Based on the result, the mean incubation time is about 5.4 days and the fatality rate is higher for older and male patient and the cure rate is higher at younger,female and asymptomatic patient.