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.

Forklift achieves transportation of freight and continues loading and unloading work repeatedly long for hours in industry spot. Therefore, drivers feel tired make a mistake for wrong operation of vehicle caused by continuous work. These components are resulted in CTDs, some industrial accident. That is the forklift need to ergonomics access. So, in this paper, requirements of forklift user were abstracted using questionnaire, produced important design factor for pedal and lever using QFD(Quality Function Deployment), and then suggested ergonomic considerations for industrial accident prevention.

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.

The life change unit(LCU) models by behavior scientific approach method are very important in order to minimize industrial accident by the job stress. Especially, these models are analyzed the accident proneness concept through the LCU factors. Therefore this paper describes the analytical results between the Eastern LCU model and Western LCU model and its application. Death of parents, death of close friend, jail term, personal injury or illness between the Eastern LCU models and Western LCU models are different in the human behavior and psychological sides, whereas changes in family member s health are turned out same life ideas. In the case of Korean LCU models between the unmarried and married workers living in the middle area are approved in the order of death of parents, death of spouse, death of close family member, and death of close friend.

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( 2003.2~2004.1 ) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

고도화된 산업의 발달로 인하여 많은 업종들이 생겨나게 되었고, 그로 인해 크고 작은 사고들이 발생하게 되었다. 사고의 발생은 부상, 사망과 질병 등의 재해자들을 발생시키고, 사고로 인한 재해자의 수는 한 사업장에서도 여러 건씩 발생한다. 이러한 재해자의 공통된 특성 요소와 성향을 분석할 수 있다면, 1%의 재해자라도 더 줄일 수 있으리라 사료된다. 본 논문에서는 업종별 그룹을 통해 재해자들의 특성화 분석을 위하여 총 25,157개의 데이터를 바탕으로 데이터 Mining 기법중의 하나인 Classification and Regression Tree(CART)를 이용하여 분석을 하였다. 종속변수인 10개의 주요 업종을 중심으로 재해자들이 어떠한 성향을 나타내고 있는지 알아보고자 독립변수인 재해자 구분, 재해시간, 근속기간, 성별, 회사 규모와 연령 등 8개 항목을 채택하여 재해자 위험 그룹을 분석하고 그 결과를 제시한다.

의사 결정 나무 알고리즘은 관심이 되는 집단을 몇 개의 소집단으로 분류하거나 특성을 예측하기 위한 데이터 마이닝 분석 기법중 하나이다. 이 기법은 각 업종별 특성을 분석하여 업종별 차이점을 찾기 위해 사용되었다. 여기에서 사용되어진 의사결정 알고리즘으로는 C4.5알고리즘을 사용하였다. 트리는 이득율(Gain Ratio)에 의해서 Top-Down방식으로 구성하게 되었다. 본 연구에서 사용된 데이터는 2003년, 2004년에 발생 되어진 데이터로 총 25,159개의 데이터를 대상으로 정제과정을 거쳐 24,887개의 데이터를 사용하였고, 한 개의 종속 변수와 8개의 독립 변수로 이루어져 있다. 총 222개의 트리 노드가 만들어 졌고 최종 노드(Leaf Node)는 총 151개가 생성되었다. 생성된 트리 결과에 대한 정확성 측정을 위해 정확도(Accuracy), 오분류 확률(Misclassification Rate)을 계산하였다.

With Zohar(1980)'s research on safety climate and industrial safety, occupational safety and health literatures begin to emphasize the influence of organizational context. Based on this research trend, this paper tried to review the relationship between i

산업재해의 직접적인 원인은 작업자의 불안전한 행동과 작업과정에서의 안전시설 설치미흡으로 인한 불안전한 상태로와 기술적, 교육적, 관리적 원인의 복합적인 형태로 발생되는데 그 가운데 산업안전보건법상의 건설공사 각 공종 별 기술적인 안전시설설치 기준이 건설업 종사자에게 쉽게 적용하기가 현행 법기준에는 문제점이 있으며 이러한 복잡한 법기준을 분석하여 안전사고 예방 기법에 적용하고자 제시하고자하며, 향후에 이를 바탕으로 산업안전보건법상의 안전기준을 체계적으로 분석하여 제시하고자 한다.

It has been Management for stabilize Enterprise-Management for Economic demand for to Productivity, Automation, customer satisfaction, Especially Semiconductor-Industry has been, potential-risk in working to factory to machine equipment, all kinds of utility, gas, chemical, electronic, Fire. This study of basic-purpose has Research Different From as Follow to analysis and Solution For semiconductor product Factory of a actual point Data and specific-gravity to Relation for company-Injury. 1. It has been try to Injury-Tendency and cause-Analysis for our County-Manufacture-Occupation. 2, Semiconductor Injury of Actual-condition in Enforcement for problem and Analysis that Injury Problem has occupated it Submitted to Solution for ordinary Injury theory View to point Solve at for New Model has applicated to that nilem for processed to Solution.

The purpose of this paper is to supply the principal methods for the safety in the press industrial accident preventions and the basic analyzing theory in the press industrial accidents. It was reached the press machine installed companys and was analyzed the accident characteristics, types, the worker's working period and the distributed worker's age of the happened the press machine industrial accidents. The direct reasons are almost reasons of the press industrial accidents. But it should be not ignore the indirect reasons of the accidents In this study, it was presented that the safety devices is selected by the amalyzed reasons in the press industrial accidents for the principal accident prevention.