• Journal of the Korean Society of Safety
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• v.36 no.4
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• pp.80-87
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• 2021

Risk assessment techniques are processes used to evaluate hazardous risk factors in construction sites, facilities, raw materials, machinery, and equipment, and to estimate the size of risk that could lead to injury or disease, and establish countermeasures. The most important thing in assessing risk is calculating the size of the risk. If the size of the risk cannot be calculated objectively and quantitatively, all members who participated in the evaluation would passively engage in establishing and implementing appropriate measures. Therefore, this study focused on predicting accidents that are expected to occur in the future based on past occupational accident statistics, and quantifying the size of the risk in an overview. The technique employed in this study differs from other risk assessment techniques in that the subjective elements of evaluators were excluded as much as possible by utilizing past occupational accident statistics. This study aims to calculate the size of the risk, regardless of evaluators, such as a manager, supervisor, safety manager, or employee. The size of the risk is the combination of the likelihood and severity of an accident. In this study, the likelihood of an accident was evaluated using the theory of Bud Accident Chainability, and the severity of an accident was calculated using the occupational accident statistics over the past five years according to the accident classification by the International Labor Organization.

• Safety and Health at Work
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• v.13 no.2
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• pp.170-179
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• 2022

Background: The vulnerability of international migrant workers is on the rise, affecting the frequency of occupational accidents at workplaces worldwide. If migrant workers are managed in the same way as native workers, the consequences on safety assurance and risk management will be significant. This study aimed to develop the vulnerability factor model for migrant workers in seafood processing industries because of significant risk-laden labor of Thailand, which could be a solution to control the risk effectively. Methods: A total of 569 migrant workers were surveyed (432 Burmese and 137 Cambodian), beginning with 40 initial vulnerability factors identified in the questionnaire established from experts. The data were analyzed through descriptive analysis; exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to ascertain the model. Results: The result of content validity >0.67 and the Cronbach's alpha of 0.957 specified the high reliability of 40 factors. The EFA indicated a total variance of 65.49%. The final CFA validated the model and had an empirical fitting; chi-square = 85.34, Adjust Goodness-of-Fit Index = 0.96, and root mean square error of approximation = 0.016. The structure concluded with three dimensions and 18 factors. Dimension 1 of the structure, "multicultural safety operation," contained 12 factors; Dimension 2, "wellbeing," contained four factors; and Dimension 3, "communication technology," contained two factors. Conclusion: The vulnerability factor structure developed in this study included three dimensions and 18 factors that were significantly empirical. The knowledge enhanced safety management in the context of vulnerability factor structure for migrant workers at the workplace.

• Journal of IKEEE
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• v.26 no.2
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• pp.219-225
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• 2022

The textile industry in Korea, the dyeing sector is an energy-intensive sector and has low per-unit productivity due to its labor-intensive nature. If the defective rate of dyed fabrics is high, additional costs are incurred due to an increase in production cost due to re-dyeing. Therefore, the goal of the dyeing factory was to minimize the defect rate rather than to save energy. It was difficult to check the dyeing state of the fabric in real time due to the risk of accidents due to burns or pressure when dyeing in a high-temperature and high-pressure environment. In this paper, a complex sensor that can measure the exhaustion rate of dye solution in the dyeing machine using turbidity, pH, and conductivity sensors was proposed, and the experimental method and experimental results were analyzed.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.85-94
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• 2022

Recently, consumers who prefer contactless consumption are increasing due to pandemic trends such as Corona 19. This is the driving force for developing the last mile-based logistics ecosystem centered on the online e-commerce market. Lastmile led to the continued development of the logistics industry, but increased the amount of cargo in urban area, and caused social problems such as overcrowding of logistics. The courier service in the logistics base area utilizes the process of visiting the delivery site directly because the courier must precede the loading work of the cargo in the truck for the delivery of the ordered product. Currently, it's carried out as automated logistics equipment such as conveyor belt in unloading or classification stage, but the automation system isn't applied, so the work efficiency is decreasing and the intensity of the courier worker's labor is increased. In particular, small-scale courier workers belonging to the sub-terminal unload at night at underdeveloped facilities outside the city center. Therefore, the productivity of the work is lowered and the risk of safety accidents is exposed, so robot-based loading technology is needed. In this paper, we have derived the top-level concept and requirements of robot-based loading system to increase the flexibility of logistics processing and to ensure the safety of courier drivers. We defined algorithms and motion concepts to increase the cargo loading efficiency of logistics sub-terminals through the requirements of end effector technology, which is important among concepts. Finally, the control technique was proposed to determine and position the load for design input development of the automatic conveyor system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.692-712
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• 2022

Buoys used for Aid to Navigation systems are widely used to guide the sea paths and are powered by batteries, requiring continuous battery replacement. However, since human labor is required to replace the batteries, humans can be exposed to dangerous situation, including even collision with shipping vessels. In addition, Maritime sensors are installed on the route signs, so that these are often damaged by collisions with small and medium-sized ships, resulting in significant financial loss. In order to prevent these accidents, maritime object detection technology is essential to alert ships approaching buoys. Existing studies apply a number of filters to eliminate noise and to detect objects within the sea image. For this process, most studies directly access the pixels and process the images. However, this approach typically takes a long time to process because of its complexity and the requirements of significant amounts of computational power. In an emergent situation, it is important to alarm the vessel's rapid approach to buoys in real time to avoid collisions between vessels and route signs, therefore minimizing computation and speeding up processes are critical operations. Therefore, we propose Fast Connected Component Labeling (FCCL) which can reduce computation to minimize the processing time of filter applications, while maintaining the detection performance of existing methods. The results show that the detection performance of the FCCL is close to 30 FPS - approximately 2-5 times faster, when compared to the existing methods - while the average throughput is the same as existing methods.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1226-1236
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• 2022

Because students majoring in construction-related fields must develop a broad repository of knowledge and skills, effective transferal of these is the primary focus of most academic programs. While inculcation of this body of knowledge is certainly critical, actual construction projects are complicated ventures that involve levels of risk and uncertainty, such as resistant neighboring communities, unforeseen weather conditions, escalating material costs, labor shortages and strikes, accidents on jobsites, challenges with emerging forms of technology, etc. Learning how to develop a level of discernment about potential ways to handle such uncertainty often takes years of costly trial-and-error in the proverbial "school of hard knocks." There is therefore a need to proactively expedite the development of a sharpened intuition when making decisions. The AGC Education and Research Foundation case study committee was formed to address this need. Since its inception in 2011, 14 freely downloadable case studies have thus far been jointly developed by an academics and industry practitioners to help educators elicit varied responses from students about potential ways to respond when facing an actual project dilemma. AGC case studies are typically designed to focus on a particular concern and topics have thus far included: ethics, site logistics planning, financial management, prefabrication and modularization, safety, lean practices, preconstruction planning, subcontractor management, collaborative teamwork, sustainable construction, mobile technology, and building information modeling (BIM). This session will include an overview of the history and intent of the AGC case study program, as well as lively interactive demonstrations and discussions on how case studies can be used both by educators within a typical academic setting, as well as by industry practitioners seeking a novel tool for their in-house training programs.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.136-144
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• 2022

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.17-28
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• 2023

Potholes that occur on paved roads can have fatal consequences for vehicles traveling at high speeds and may even lead to fatalities. While manual detection of potholes using human labor is commonly used to prevent pothole-related accidents, it is economically and temporally inefficient due to the exposure of workers on the road and the difficulty in predicting potholes in certain categories. Therefore, completely preventing potholes is nearly impossible, and even preventing their formation is limited due to the influence of ground conditions closely related to road environments. Additionally, labeling work guided by experts is required for dataset construction. Thus, in this paper, we utilized the Mean Teacher technique, one of the semi-supervised learning-based knowledge distillation methods, to achieve robust performance in pothole image classification even with limited labeled data. We demonstrated this using performance metrics and GradCAM, showing that when using semi-supervised learning, 15 pre-trained CNN models achieved an average accuracy of 90.41%, with a minimum of 2% and a maximum of 9% performance difference compared to supervised learning.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.122-123
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• 2023

About 1 year and 7 months have passed since the Serious Accident Punishment Act was implemented, and although many studies have been conducted on the definition and policy of the Act, almost no research has been conducted on its impact on the logistics industry. In particular, research on the PDI (PRS) process within finished vehicle logistics is severely lacking ‥‥‥. The government is expecting to expand the scope of punishment and upgrade the safety management system through the Serious Accident Punishment Act, but if you check the industrial accident data issued by the Ministry of Employment and Labor in 2022, the number and rate of industrial accidents will increase compared to 2021

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.1
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• pp.26-34
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• 2024

Objectives: The successful implementation of the Port Safety Special Act is a very important matter. Now that one year has passed since its introduction, this study aims to review the achievements so far and identify future tasks. Methods: The provisions of the Special Act on Port Safety were analyzed and the latest literature related to port safety management was reviewed. In addition, an in-depth interview was conducted with a business owner. Results: The achievements over the past year are as follows. As business operators took greater responsibility for safety management, blind spots in safety were resolved to an extent. Specialized training for the port unloading industry was provided, and a safety management system was established for unloading docks. In addition, the Ministry of Oceans and Fisheries was able to intervene in the prevention of safety accidents at ports through the deployment of port safety inspectors. In 2022, the port industry accident frequency and death rate declined compared to the previous year. Conclusions: The "Port Safety Special Act" has become relatively well established in the port industry over the past year. However, since the Serious Disaster Punishment Act was implemented in January of the same year, there is a limit on determining what is necessarily the effect of the Special Act. Future tasks include unifying contracts centered on cargo handling companies, supporting safety management costs, increasing the number of port safety inspectors, producing reliable port disaster statistics, and cooperating between the Ministry of Oceans and Fisheries and the Ministry of Employment and Labor.