• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.1
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• pp.91-102
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• 2014

Objectives: This study was performed in order to devise a procedure for supplementing the Special Management Materials in the Occupational Safety and Health Act and recommend candidate materials. The results are expected to be used as fundamental data for classification and criteria necessary to manage Special Management Materials in workplaces. Also, they are expected to be used as a basis for selecting target materials to nominate as additional Special Management Material. Methods: We investigated the selection standards for candidates and review ranges of data sources to nominate Special Management Materials. The substances classified as GHS(Globally Harmonized System of classification and labeling of chemicals) category 1A(known to have carcinogenic potential and reproductive toxicity for humans) or 1B(presumed to have carcinogenic potential and reproductive toxicity for humans) carcinogens and reproductive toxicants among the Controlled Hazardous Substances of the Regulation on Occupational Safety and Health Standards and substances with OELs(Occupational Exposure Limits) were inspected as the candidates for Special Management Materials. Conclusions: A seven-step procedure for selecting candidates to designate as Special Management Materials was suggested, including the setting of target chemicals for evaluation, classification of CMR(Carcinogens, Mutagens or Reproductive toxicants) by GHS classification and criteria, suggestion and selection of the candidates, and more. This study recommends 58 chemicals as qualified candidates to supplement the Special Management Materials.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• The Journal of Information Systems
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• v.27 no.2
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• pp.175-196
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• 2018

Purpose The purpose of this study is to identify problems of current educational facility data management and recommend a standardized terminology classification system as a solution. In addition, the research aims to present a preemptive and integrated disaster and safety management framework for educational facilities by seeking efficient business processes through secured data quality, systematic data management, and external data linkage and analysis. Design/methodology/approach A terminology classification system has been established through various processes including filtering and analysis of related data including laws, manuals, educational facilities accidents, and historical records. Furthermore, the terminology classification system has been further reviewed through several consultations with experts and practitioners. In addition, the accumulated data was refined according to the established standard terminology and an Excel database was developed. Based on the data, accident patterns occurred in educational facilities over the past 10 years were analyzed. Findings In the study, a template was developed to collect consistent data for the standardized disaster and safety management terminology classification system in educational facilities. In addition, the standardized data utilization methods are presented from the viewpoint of 'education facility disaster safety data management', 'data analysis and insight', 'business management through data', and 'leaping into big data management'.

• Journal of Sensor Science and Technology
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• v.18 no.5
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• pp.385-392
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• 2009

This paper describes the algorithm development of a new body pressure detection sensor for occupant classification system. U.S. Government has required that advanced airbag system should be installed to every automobiles after 2006 according to FMVSS 208 regulation. Therefore, Occupant Classification System should be provided the passenger with safety in order to protect the infants or children that sit in the front passenger seat. When an occupant sits on the chair of the vehicle, deployment of the airbag depends on passenger's weigh distribution and postures. Authors have been developed a new pattern recognition of passenger and weight distribution at the same time by Force Sensing Resistor for the safety.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.107-119
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• 2010

The aim of this study was to compare the carcinogen classification systems of developed countries or global organizations with domestic system under Industrial Safety and Health Act (ISHA). We selected the representative institutions which had carcinogen classification system such as International Agency for Research on Cancer (IARC), National Toxicological Program (NTP), Environmental Protection Agency (US-EPA), American Conference of Governmental Industrial Hygienists (ACGIH), and European Union (EU). We collected the carcinogen lists issued by 5 institutions, and merged by CAS number of each chemical with Microsoft Access 7.0. We found that confirmed human carcinogens, probable human carcinogens and possible human carcinogens were 34, 179, and 252, respectively. All of the institutions classified chemicals as 2 (NTP), 3 (EU) or 5 (IARC, ACGIH, US-EPA) categories based on the weight of scientific evidences for carcinogenicity and periodically updated the carcinogen list by regular procedure. However, a total of 90 chemicals could be classified as carcinogen under ISHA in Korea. There was no procedure or system which periodically update the carcinogen lists. In addition, the status of carcinogen classification according to regulation was confused. In conclusion, these findings suggest that the carcinogen classification and management system should be amended by consideration of systems of advanced institutions and the domestic regulation system.

• Journal of KIBIM
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• v.6 no.2
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.120-130
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• 2021

Recently, the government is reinforcing safety management at construction sites to prevent safety accidents in construction works, and the safety management plan of workers is an important situation. Meanwhile, modular construction is expected to be gradually expanded to middle and high-rise buildings, but active measures to ensure worker safety are insufficient. This study is a preliminary study of the development of a checklist for preventive worker safety management. The purpose of this study is to derive a checklist classification system for the safety management of workers in the field of modular construction by preceding studies, case analysis, and expert advisory opinions. The classification system consists of large categories of factory manufacturing, transportation, and on-site construction, and the sub-system consists of six sub-classes: foundation work, frame work, modular frame installation work, finishing work, and facility work. Among them, the sub-classification of modular frame installation work consists of 12 unit works, centering on module lifting and assembly module work, which are the main construction processes. And the classification system reflects the three main management factors and contents for defined safety management. It is expected that the research results of this study can contribute to efficient safety management at the modular construction site.

• Journal of the Korea Safety Management & Science
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• v.19 no.3
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• pp.1-9
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• 2017

In the industrial field, various type of fuel have been used for product processing facilities. Recent for 10 years, the usage of natural gas (NG) was gradually increased. Because it has many merits; clean fuel, no transportation, storage facility and so on. There are common safety concept that strict explosion protection approaches are needed for facilities where explosive materials such as flammable liquid, vapor and gases exist. But some has an optimistic point of view that the lighter than air gases such as NG disperse rapidly, hence do not form explosion environment upon release into the atmosphere, many parts has a conventional safety point of view that those gases are also inflammable gases, hence can form explosion environment although the extent is limited and present. In this paper, the heating equipments (Hot Oil Heater) was reviewed and some risk management measures were proposed. These measures include hazardous area classification and explosion-proof provisions of electric apparatus, an early gas leak detection and isolation, ventilation system reliability, emergency response plan and training and so on. This study calculates Hazardous Area Classification using the hypothetical volume in the KS C IEC code.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1774-1779
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• 2016

This paper proposes a portable cable fault detection system with automatic fault distinction and distance measurement using time-frequency correlation and reference signal elimination method and automatic fault classification algorithm in order to have more accurate fault determination and location detection than conventional time domain refelectometry (TDR) system despite increased signal attenuation due to the long distance to cable fault location. The performance of the developed system method was validated via an experiment in the test field constructed for the standardized performance test of power cable fault location equipments. The performance evaluation showed that accuracy of the developed system is less than 1.34%. Also, an error of automatic fault type and location by detection of phase and peak value through elimination of the reference signal and normalization of correlation coefficient and automatic fault classification algorithm not occurred.

• Journal of the Korea Safety Management & Science
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• v.2 no.3
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• pp.171-184
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• 2000

We may be given a set of observations with the classes or clusters. The aim of this article is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets. In this paper, machine learning algorithm classifiers based on CART, C4.5, CAL5, FACT, QUEST and statistical discriminant analysis are compared on various datasets in classification error rate and algorithms.