• Journal of the Korean Society of Safety
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• v.29 no.3
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• pp.79-84
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• 2014

Human error is one of the major contributors to the accidents. A lot of risk assessment techniques have been developed for prevention of accidents. Nevertheless, most of them were interested in physical factors, because quantitative evaluation of human errors was difficult quantitatively. According to lack of risk assessment techniques about human errors, most of industrial risk assessment for human errors were based on data of accident analysis. In order to develop an effective countermeasure to reduce the risk caused by human errors, a systematic analysis is needed. Generally, risk assessment system is composed of 5 step(classification of work activity, identification of hazards, risk estimation, evaluation and improvement). This study aimed to develop a risk identification technique for human errors that could mainly be applied to industrial fields. In this study, Ergo-HAZOP and Comprehensive Human Error Analysis Technique were used for developing the risk identification technique. In the proposed risk identification technique, Ergo-HAZOP was used for broad-brush risk identification. More critical risks were analysed by Comprehensive Human Error Analysis Technique. In order to verify applicability, the proposed risk identification technique was applied to the work of pile head cutting. As a consequence, extensive hazards were identified and fundamental countermeasures were established. It is expected that much attention would be paid to prevent accidents by human error in industrial fields since safety personnel can easily fint out hazards of human factors if utilizing the proposed risk identification technique.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.90-95
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• 2012

To prevent similar accidents with the basis of industrial accidents already occurred in industrial plants, it would be possible only after true causes are grasped. Unfortunately, however, most accident investigation carried out with the basis of legal regulation failed to grasp them so that similar accidents have been repeated without cease. This research aimed to find out differences between results from conventional accident investigation and those from human error analysis, and to draw out effective and practical counter-plans against industrial accidents occurred repeatedly in an autoglass manufacturing company. As for analysis, about 110 accident cases that occurred for last 7 years were collected, and by adopting the Comprehensive Human Error Analysis Technique developed by the previous researchers, not direct causes but basic fundamental causes that might induce workers to human errors were sought. In consequence, the result showed that facility factors or environmental factors such as improper layout, mistakes in engineering design, and malfunction of interlock system were authentic major accident causes as opposed to managerial factors such as personal carelessness or failure to wearing personal protective equipments, and/or improper work methods.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.53-72
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• 2004

Today we are observing a lot of injuries, casualties, and property losses that are mainly caused by the defects of products. In order to derive safety designs, which minimize the possibility of such product liability-related accidents, we need to take into account the user-product interaction as an important part of the danger factor analysis. Existing risk analysis techniques, however, have some limitations in detecting comprehensive danger factors that are peculiarly involved in human errors and the functional defects of products. Researches on danger factor analysis regarding the user-product interaction have been carried out actively in ergonomics. In this paper, we suggest a novel product risk analysis technique, which is more objective and systematic compared to the previous ones, by combining a modified TAFEI (Task Analysis For Error Identification) technique with SASA (Systematic Approach to Accident Scenario Analysis) technique. By applying this technique to the product design practice in industry, corporations will be able to improve the product safety, consequently strengthening the competitiveness.

• Journal of the Korea Institute of Building Construction
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• v.23 no.4
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• pp.417-428
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• 2023

The demolition of domestic infrastructures mirrors other significant construction initiatives in presenting a markedly high accident rate. A comprehensive investigation into the origins of such accidents is crucial for the prevention of future incidents. Upon detailed inspection, the causes of demolition construction accidents are multifarious, encompassing unsafe worker behavior, hazardous conditions, psychological and physical states, and site management deficiencies. While statistics relating to demolition construction accidents are consistently collated and reported, there exists an exigent need for a more foundational cause categorization system based on accident type. Drawing from Heinrich's Domino Theory, this study classifies the origins of accidents(unsafe behavior, unsafe conditions) and human errors(human factors) as per the type of accidents experienced during demolition construction. In this study, a three-step model of QFD-FMEA(Quality Function Deployment - Failure Mode Effect Analysis) is employed to systematically categorize accident causes according to the types of accidents that occur during demolition construction. The QFD-FMEA method offers a technique for cause classification at each stage of the demolition process, including direct causes(unsafe behavior, unsafe environment), and human errors(human factors) through a tri-stage process. The results of this accident cause classification can serve as safety knowledge and reference checklists for accident prevention efforts.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.