• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.893-898
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• 1998

원자력발전소의 확률론적안전성평가(PSA)의 일부로 수행되어 왔던 인간신뢰도분석(HRA)방법은 최근 여러가지 결함이 지적되어 왔고 이를 보완하는 노력들이 계속되어 왔다 본 연구에서는 기존 HRA 방법의 취약점을 해결할 수 있는 인간오류분석 방법 개발을 목표로, 현재까지 개발되어온 인간오류분석 방법들을 검토하고, 원전 운전원 직무의 분석에 적절하다고 판단되는 HRMS, CREAM, PHECA 등 세가지 방법을 선정하여 사고관리 운전원 직무중 '원자로공동중수' 직무에 적용하는 사례연구를 수행하였다 사례연구 결과, PHECA는 원자력발전소 운전원 직무의 오류분석으로는 부적합한 것으로 평가되었고, HRMS나 CREAM은 사고관리 인지오류분석에 기본적인 적합성은 있는 것으로 평가되었다. 각 방법에 대한 장, 단점과 개선점을 제시하였다.

• Product Safety
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• s.249
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• pp.44-47
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• 2014

두리화학(주)는 1974년 모체 공장인 대성화학을 창립한 이래 플라스틱 배관자재 생산 전문업체로 성장 발전해 왔다. 그동안 고객의 욕구를 충족시키고 고객 감동경영을 실현하기 위해 부단히 노력해온 두리화학(주)는, 최근 공동주택 배관 시스템의 최대 결점인 배수 소음 및 결로를 별도의 방음 및 보온 작업을 하지 않고도 배수소음 및 결로를 획기적으로 줄일 수 있는 공법을 개발하여, 국내 유수의 건설 업체에 납품하여 뛰어난 성능을 인정받게 되었다. 동종업계에서 국내 최대의 생산 설비와자동화 제조 공정을 갖추고 최고의 품질관리 시스템을 가동하여, 부적합품 발생 방지에 주력하고 있으며 사후 관리에도 만전을 기하고 있다. 또한 두리화학(주)는 기업의 최대 목적인 이윤을 추구함에 있어 사회적 책임을 다하고, "고객감동경영"의 경영철학을 바탕으로 무한경쟁시대의 21세기를 맞아, 항상 고객과 함께 연구하고 발전하는 기업으로 거듭날 것을 고객 앞에 다짐한다.

• Product Safety
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• s.246
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• pp.40-43
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• 2014

두리화학(주)는 1974년 모체 공장인 대성화학을 창립한 이래 플라스틱 배관자재 생산 전문업체로 성장 발전해 왔다. 그동안 고객의 욕구를 충족시키고 고객 감동경영을 실현하기 위해 부단히 노력해 온 두리화학(주)는, 최근 공동주택 배관시스템의 최대 결점인 배수 소음 및 결로를 별도의 방음 및 보온 작업을 하지 않고도 배수 소음 및 결로를 획기적으로 줄일 수 있는 공법을 개발하여, 국내 유수의 건설 업체에 납품하여 뛰어난 성능을 인정받게 되었다. 동종업계에서 국내 최대의 생산 설비와 자동화 제조 공정을 갖추고 최고의 품질관리 시스템을 가동하여, 부적합품 발생방지에 주력하고 있으며 사후 관리에도 만전을 기하고 있다. 또한 두리화학(주)는 기업의 최대 목적인 이윤을 추구함에 있어 사회적 책임을 다하고 "고객감동경영"의 경영철학을 바탕으로 무한 경쟁시대의 21세기를 맞아, 항상 고객과 함께 연구하고 발전하는 기업으로 거듭날 것을 다짐했다.

• Journal of the Korea Institute of Building Construction
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• v.16 no.6
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• pp.535-544
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• 2016

Korean government is promoting "K-Smart City" to overseas market which is an integrated solution of construction industry with ICT(Information and Communciations Technologies) industry. Due to nature of Smart City, construction quality and the development quality of the facilities need to be established to improve the overall quality. However, guidelines and regulations to initiate quality control for Smart City are behind the actual demand. This deficiency is bringing quality control for construction and ICT to be controlled separately causing lack of synergy and resulting in overall quality degradation. This research is designed to improve the construction quality of Smart City during its establishment stage by integrating ICT system with on-site construction (Integrated control center and on-site equipment). The adoption of this research to a real Smart City case had resulted in 22% reduction of construction inspection failure (Audit), and also allowed Construction Company to pre-align quality control of all purchased items of ICT Infra that resulted in 18% reduction of nonconformity, thus contributing to an overall quality improvement. This research is expected to be used widely among all construction industry of Smart City.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.140-149
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• 2021

Most industries have introduced and operate an information security management system (ISMS) or a personal information security management system (PIMS) to suitably protect and maintain customer's information and company trade secrets. This study starts with the premise that it is desirable for every industry considering information security to maintain an ISMS. ISMS can be of different types among various organizations, taking into consideration culture, practical work procedures, and guidelines for information security. This study intends to derive primary control areas of an ISMS for each industry based on organizational size and audit type by analyzing non-conformity trends and control factors according to certification audits for organizations introduced for international ISMS under ISO27001. This study analyzed improvement effects of ISMS through case analyses. It is meaningful as exploratory research, although it was difficult to acquire data for empirical study because few organizations maintain certification in major industrial sectors. The requirements presented the highest frequency of non-conformity for each type from the 2013-initiated ISO27001; the years 2013 to 2020 were extracted as the primary control area. The study found that for primary control areas of ISMS for each of three industries, organizational size and audit type had differences.

• Journal of Food Hygiene and Safety
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• v.33 no.1
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• pp.23-30
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• 2018

The purpose of this research was to introduce network analysis method for analyzing pesticide residues in incongruity commercial agricultural commodities. Based on the "results in pesticide residues on incongruity commercial agricultural commodities" on "Guidelines for food safety management 2017", we used centrality analysis for pesticide residues via degree, closeness and betweenness centrality measurement. In case of degree centrality result, chlorpyrifos and diazinon were the most highly "connected node" in pesticide network. For the closeness centrality result, the most pesticides showed the similar closeness trend except for 19 species of pesticides. Fludioxonil and chlorpyrifos are recognized as the "bridge" of pesticides network with their high betweenness centrality. The results of network analysis show the "relation" data, which could not represent through out the conventional statistical analysis, among the pesticide residues. We hope that the network analysis method will be appropriate and precise tool for analyzing pesticide residues via elaboration and optimization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.488-495
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• 2007

In spite of regular electrical safety inspection and improvement management of incongruent facilities, over 80 percent of electrical fires were generated in electrical facilities for general use, and the prevention of electrical safety accidents gets more difficult because of incomprehension on electrical safety inspection system, the increasement of non-inspection electrical facilities user, un-installation of residual current protective devices and leaving unsuitable equipments alone. This paper analyzed electrical safety inspection system of electrical facilities for general use and results of safety inspection and electrical fire statistics, and deducted the related problems. Also, improvement method of safety management of electrical facilities for general use was proposed in view of electrical safety inspection system, inspection implementation method, electrical fire statistics establishment, electrical safety consciousness and remote electrical safety inspection system.

• The KIPS Transactions:PartD
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• v.18D no.1
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• pp.57-66
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• 2011

Recently, the necessity of new methods of spatial data integration and analysis in CRM has been increased since it is acknowledged that about eighty percent of all data stored in corporate databases has a spatial component. But conventional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. This paper has designed and implemented spatially-enabled integration management system that can manage consistently both enterprise and spatial data through a legacy CRM system and object-oriented database and additionally support spatial analysis and map visualization for a gCRM. Through implementation, it is demonstrated that the proposed system can facilitate effectively spatial data management and analysis in a legacy CRM system.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• The Journal of Bigdata
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• v.8 no.1
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• pp.157-165
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• 2023

Seafood is a major source of protein in many countries and its consumption is increasing. In Korea, consumption of seafood is increasing, but self-sufficiency rate is decreasing, and the importance of safety management is increasing as the amount of imported seafood increases. There are hundreds of species of aquatic products imported into Korea from over 110 countries, and there is a limit to relying only on the experience of inspectors for safety management of imported aquatic products. Based on the data, a model that can predict the customs inspection results of imported aquatic products is developed, and a machine learning classification model that determines the non-conformity of aquatic products when an import declaration is submitted is created. As a result of customs inspection of imported marine products, the nonconformity rate is less than 1%, which is very low imbalanced data. Therefore, a sampling method that can complement these characteristics was comparatively studied, and a preprocessing method that can interpret the classification result was applied. Among various machine learning-based classification models, Random Forest and XGBoost showed good performance. The model that predicts both compliance and non-conformance well as a result of the clearance inspection is the basic random forest model to which ADASYN and one-hot encoding are applied, and has an accuracy of 99.88%, precision of 99.87%, recall of 99.89%, and AUC of 99.88%. XGBoost is the most stable model with all indicators exceeding 90% regardless of oversampling and encoding type.