• Journal of Korean Public Health Nursing
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• v.34 no.3
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• pp.473-489
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• 2020

Purpose: This study investigated the patient safety culture (PSC), the perception of importance on patient safety management (PIPSM) and the patient safety management activities (PSMA) of care workers in nursing homes. This was a descriptive study that attempted to provide basic data for the patient safety education program of care workers. Methods: Data were collected using questionnaires and interviews from July 1 to 31 in 2020. One hundred and seventy-four care workers participated in quantitative research. The collected data were analyzed by the SPSS/WIN 25.0 program using descriptive statistics, t-test, ANOVA, Bonferroni, and Pearson's correlation. The qualitative data were collected through semi-structured, audio-recorded interviews with six representatives and six care workers from six nursing homes. Content analysis was performed to analyze the data. Results: Positive correlations were observed between PSC and PIPSM, and between PIPSM and PIPSM. Care workers' experience in patient safety management was in the following six categories: "Safety accident risk factors", "Type of safety accidents", "How to prevent safety accidents", "Effective safety management education", "Emphasis on occupational ethics of care workers", and "Needs for standard protocol" Conclusion: These findings indicate that considering the care workers' age and facility size, nurses should enhance patient safety education for care workers and establish a management activity system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.139-147
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• 2021

In this study, we proposed the possibility of reliability analysis of risk index by using Monte Carlo simulation as basic data of safety accident occurrence data and expert opinion by construction industry type. Through this, it is expected that risk index for safety accidents according to detailed types of works will be presented stochastically and it will be possible to predict the risk factors and the expected range of damage based on the reliability analysis in the construction safety management plan. It will also reduce many of the planning risks that are common to decision makers in the field of construction management. In identifying risks, road bridge construction was classified into earthworks, drainage works, and bridge construction, and possible safety accidents were classified based on expert data. The risk index was calculated for each detailed construction of road and bridge construction, drainage construction, and bridge construction.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.56-61
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• 2022

In this study, a systematic survey was employed to investigate managers' awareness on the safety culture project in small- to medium-sized industries. The existing regulatory and technology-oriented disaster prevention strategy has been implemented to reduce industrial accidents within a short timeframe, but it is essential to reduce fundamental accidents as well by improving the organizational safety culture or software elements, such as safety awareness. Questionnaires and interviews were conducted to examine the managers' awareness about safety culture, consisting of 17 questions related to safety consulting and with the participation of 191 executives and safety managers. The results showed a significant difference in the self-pay rate of safety consulting depending on the size of the workplace under evaluation. Based on the results, the safety-consulting business focused on providing differentiated consulting support and realistic improvement plans according to the workplace size. The study concluded that it is possible to reduce fundamental human errors and improve organizations' safety awareness or safety culture by employing safety consulting differentiated by workplace size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1272-1278
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021 and is implementing this law. However, the number of occupational accidents in 2021 increased by 10.7% compared to the same period of the previous year. Therefore, safety measures are urgently needed in the industrial field. In this study, BLE Mesh networking technology is applied for safety management of high-risk industrial sites with poor communication environment. The complex sensor AIoT device collects gas sensing values, voice and motion values in real time, analyzes the information values through artificial intelligence LSTM algorithm and CNN algorithm, and recognizes dangerous situations and transmits them to the server. The server monitors the transmitted risk information in real time so that immediate relief measures are taken. By applying the AIoT device and safety management system proposed in this study to high-risk industrial sites, it will minimize industrial accidents and contribute to the expansion of the social safety net.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.3
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• pp.251-261
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• 2022

Tuna purse seine fishery (TPF) constitute more than 60% of distant water fishery production in Korea based on a statistic of 2018, and 28 ships from four different companies were under operation at the western and central Pacific Ocean. On this research, common risk factors during TPF were investigated via enumeration of five years Korean fisherman's insurance payment statement, followed by some counterplans to diminish the accident rate. The accident rate of TPF on the Pacific Ocean peaked by 43.0% in 2014 and constantly decreased to 23.0% until 2018, presenting an average of 33.6%. Meanwhile, the accident rate on the Indian Ocean reached the highest point 55.1% in 2014 and declined to 11.6% in 2016, having an average of 24.7%. The average accident rate of the Indian Ocean scored 8.9% lower than the rate of the Pacific Ocean, but no statistic significance was observed. Depending on the process of operation, 'casting or hauling of net' was the most frequent part that people received an injury (40.4%). When the accidents were classified by their types, 'falling down' was the most recurrent cause of the injuries (28.5%). At the point of severity, the worst injuries were induced by crush hazard. Considering aforementioned accident frequency and severity, all the factors on the accident type list were divided into three different groups including high risk, moderate risk, and common risk. This study is expected to contribute to the reduction of occupational accidents during the work of fishermen and establishment of a safety management system for distance water fishing vessels.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.15-26
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• 2023

Purpose: It is necessary to prepare more groundbreaking measures to prevent recurrence in order to reduce the number of industrial accidents in Korea that occur steadily. In particular, since workers with disabilities are much more vulnerable to disaster safety than non-disabled workers, there is a great need to build a customized safety environment system suitable for the characteristics of the work in which workers with disabilities work and to promote management efficiency. Research design, data and methodology: Based on the analysis of the actual status of safety and health management of workplaces for the disabled, such as small, medium-sized and major businesses in Korea, an app was designed and developed to improve safety and health management efficiency of workplaces for the disabled. First, it was designed so that managers of workplaces with disabilities can understand at a glance key legal information that managers need to know and it was improved to suit the eye level of disabled workers so that they could self-evaluate the risk of their work by applying the risk assessment model for workplaces with disabilities. In addition, a mobile education environment was created in which safety and health education contents suitable for the characteristics of disabled workers can be learned by themselves. Results: When this app is applied to domestic workplaces, it is possible to check the exact contents of occupational safety and health education and easily search and check various legal information anytime, anywhere, allowing managers and disabled workers to quickly and efficiently manage various safety information. Conclusions: In addition, the establishment of a mobile safety and health management system that can quickly identify and clearly respond to various legal standards and risks of workplaces with disabilities can be expected to help prevent industrial accidents at workplaces with disabilities in Korea.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.609-615
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• 2024

The construction industry's fatality count stands at 402, comprising approximately 46% of total industrial accidents. Notably, construction costs less than 5 billion won account for about 69%, so strengthening safety management at small and medium-sized construction sites is required. In this study, 19,511 accident investigation data were collected using web scraping. Through statistical analysis of the collected structured data and text mining analysis of the unstructured data, accident types and causes of accidents were analyzed by construction costs at sites less than 5 billion won. As a result, it was confirmed that there were differences in accident types and causes depending on the construction costs. It is hoped that the results of this study will be used for customized safety management at small and medium-sized construction sites.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.