The rate of industrial accident reduction is slowing down as the attention of the Ministry of Employment and Labor and related agencies on risk assessment systems decreased. this paper focuses on weakness of legal system for the risk assessment in recent years. A survey was conducted to identify the status and condition of the risk assessment system applying on small and medium-sized manufacturing companies. A set of questionnaires is designed to reflect various perspectives of the companies regarding the problems and solutions of the system. The results refer that differentiated instructions and support systems in response to the actual conditions of the companies are mandatory to reinforce the efficiency of risk assessment system.

PURPOSES : The objective of this study is to identify the characteristics affecting traffic accidents that have occurred in 564 industrial complexes nationwide from 2011 to 2015.METHODS : The traffic accidents were specified using various factors such as industrial complex type (national VS. general), industrial complex degradation (old VS. non-old), location of complex (capital VS. non-capital), and traffic law violation (speeding, signal violation, and median invasion). The average number of crashes and accident ratio (fatal, severe, and both) in terms of characteristics of industrial complexes were calculated. With a sample of crashes of the industrial complexes for 5 years, statistical significances were tested to analyze and compare the differences based on industrial complex and traffic law characteristics using parametric and non-parametric methods.RESULTS: From statistical results, it is observed that the crash frequency occurring in old industrial complexes is three times higher than that in non-old industrial complexes. Old industrial complexes located in a capital area, old national industrial complexes, and old general industrial complexes are considerably related to higher crash frequency, but the fatal accident ratio appeared to have no statistical difference across industrial complex characteristics. Severe crashes are more likely to occur in non-old industrial complexes on an average.CONCLUSIONS : It is necessary to eliminate potential threats to roads and traffic in the same manner as illegal parking in industrial complexes through the restoration of old industrial complexes. To improve the efficiency of road infrastructure, efforts should be made to improve traffic safety in accordance with industrial characteristics such as planning and operation of relevant local government programs.

This study compared data regarding industrial accidents and awareness level for beauty product safety for four main parts of beauty industry such as hair-care, nail-care, skin-care, and make-up. Major risk factors in beauty industry are dust, musculoskeletal disorders, and organic solvent of beauty product in order of percentage. The specific types of industrial accidents in beauty industry are mainly associated with musculoskeletal system such as cuts, sprain, and varicose vein. They are mainly compensated by personal budget. The awareness levels of chemical and heavy metal containment for beauty product by beauty industry employee were 77.2% and 59.1% respectively. Most employee confirmed only important items of labelling requirement of beauty product. Also, most employee did not understand MSDS(Materila Safety Data) for chemicals used in beauty industry. Only 38.1% of beauty industry employee has had safety education while most employee (73.6%) realized that they needed safety education. Also, safety education supervised by KOSHA(Korea Occupational Safety and Health Agency) was the most preferred. This study would be good basis for safe and healthy working environment of beauty industry employee.

본 연구는 미용업 분야를 헤어, 네일, 피부, 메이크업으로 나누어 사고재해 실태와 사용 제품에 대한 안전 인식도를 비교하기 위하여 수행되었다. 그 결과 근무하는 사업장의 작업 시 발생하는 유해요인으로는 분진, 근골격계, 화학 물질 함유 제품의 유기용제 순으로 높게 나타났으며, 지난 1년 동안의 사고 및 재해는 26.3%가 경험한 것으로 파악되었다. 사고나 재해의 종류로는 베임, 인대 늘어남, 하지 정맥류 등의 근골격계 이상이 가장 많았다. 미용업에서 사용되고 있는 제품의 화학물질 함유에 대한 인지도는 77.2%, 중금속 함 유에 대하여 인지도는 59.1%로 나타났으며, 미용업 관련 제품의 표시사항(성분, 주의 사항 등)에 대한 확인 여부는 ‘중요한 내용만 확인’한다는 응답이 가장 많았다. 화학물질에 대한 물질안전보건자료(MSDS)의 인지여부는 88.8%가 알지 못하는 것으 로 나타났으며, 물질안전보건자료(MSDS)에 대한 인식도 및 사업장의 비치 여부가 낮 은 것으로 파악되었다. 안전교육의 필요성은 73.6%로 높게 나타나 안전 교육에 대한 대책 마련이 요구되었다. 본 연구는 미용업 전반에 대한 사고재해 실태 및 안전의식에 관한 포괄적인 연구가 수행되어 미용업 근로자의 안전의식 고취와 정책적 안전관리 방안, 근로자 건강관리의 기초 자료로 활용되기를 기대한다.

In this study, appropriate preventive measures are suggested as follows by analyzing industrial accident cases occurred for five years of company J and workers’ survey. First, personnel management including meticulous shift work and rotation task for olde

본 논문에서는 자기효능감(Self-efficacy belief)과 산업재해의 관계에 대한 분석을 수행하였다. 먼저 연구의 예비 단계에서는 크게 세 그룹 즉, 현장의 작업자, 현장 및 안전관련 자문업체에 근무하는 안전 관리자, 그리고 산업안전에 관계되는 정부기관 종사자 등을 대상으로 집중면접(Focus group interview)을 실시하였고, 이 집중면접의 결과를 근거로 하여 연구의 본 단계에서 적용될 설문지를 개발하였다. 본 설문지는 산업안전, 산업

Ultimate goal of this research is to develop a web-based forecasting system of industrial accidents. As an initial step for the purpose of this study, this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. In addition, this paper presents the logical process for development of a forecasting system. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years(2002~2004) in korea.

The state and its proper countermeasures of unreported industrial accidents in Gyungbuk area was investigated throughout a survey research. The goal of the study was to identify major factors that affect the number and causes of unreported accidents. Results from the survey indicated that significant number of unreported accidents exists especially in the small and medium sized industries. Types of the accidents, amount of increase in the insurance cost and level of the governmental enforcement due to the accidents were the major factors for deciding whether to report or not. These results suggested more compromising actions have to be taken by the government for revealing the present but unreported industrial accidents. A more efficient way for preventing the industrial accidents can be considered on the basis of true understanding about real industrial accident statistics.

의사결정나무 알고리즘은 데이터마이닝 기법중 하나인데 관심이 되는 데이터들에 대하여 분류 및 예측을 가능하게 해준다. 이 기법은 데이터 형태의 특성을 분석할 수 있고 산업재해 형태의 차이점을 찾아내는데 사용될 수 있다. 본 연구에서는 산업재해 데이터의 특성을 파악하고자 C4.5 알고리즘을 사용하였다. 본 연구에서 분석을 위하여 사용된 데이터는 강원도에서 발생한 2년 동안의 산업재해 관련 데이터로서 연구에 적용된 데이터의 수는 19,909개로 구성되어 있다

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years (2002 ~ 2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CART algorithm, a data mining technique. In this study, data on 10,536 accidents were analyzed to create risk groups, including the risk of disease and accident. Also, this paper used the gains chart produced by the decision tree. According to the result, gains chart can be used for a risk analysis for industrial accidents management. The sample for this work chosen from data related to manufacturing industries during three years (2002～2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

The data mining technique is an effective instrument for making large datasets accessible and different industrial accident data comparable. Many research studies have been focused on the analysis of industrial accidents in order to reduce them. However most researches used a typical technique for the analysis of data related to industrial accidents. The main objective of this study is to compare algorithms comparison for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network). This study uses selected nine independent variables to group injured people according to a dependent variable in a way that reduces variation. In this study, data on 10,536 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002 ~ 2004) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years (2002~2004) in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002~2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

Many researches and analyses have been focused on industrial accidents in order to predict and reduce them. As a similar endeavor, this paper is to develop an expert system for prevention of industrial accidents. Although various previous studies have been performed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. As an initial step for the purpose of this study, this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and Answer Tree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years(2002~2004) in korea. The initial sample includes a range of different businesses including the construction and manufacturing industries, which are typically vulnerable to industrial accidents.

The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years (2002~2004) in Korea.

산업재해 통계분석의 커다란 목적은 각 산업별로 주 위험요인을 도출하고 이에 따른 안전교육의 실시 또는 안전장치 등을 보완함으로써 산업재해를 줄이거나 예방하는데 있다고 볼 수 있다. 그러나 일반 제조업이나 건설업 등에서는 아직까지도 정량적 위험성 평가 기법이 개발되어 있지 않은 실정이다. 따라서 효율적인 위험성 평가 기법의 개발이 필요하다. 본 연구에서는 데이터마이닝 기법을 이용한 산업재해 예방을 위한 최적 알고리즘 선정 방법을 제시한다.