• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.156-170
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• 2023

Objectives: This study conducted practical training to improve the proper usage of personal protective equipment(PPE), which greatly impacts workplace safety and health management. Personal protective equipment education was conducted through active participation, without theoretical modules, and aimed to identify the effects of repeated practical education and determine ways to increase participant satisfaction. Methods: Study data were analyzed using the IBM SPSS Statistics ver.29 software. First, participants' general characteristics were analyzed with frequency analysis. Second, the normality and equality of variances (Leven's test) were tested for the dependent variables prior to statistical analyses to determine the use of parametric tests. In general, normality is assumed when the sample size is 30 or more per the central limit theorem (Park et al., 2014). As our sample size of health management workers was 43, normality can be assumed. However, to ensure rigor of the study, we examined skewness and kurtosis. The results confirmed that the data were normally distributed. Third, the effects of repeated PPE training were analyzed using paired t-tests. Fourth, differences in satisfaction with PPE training according to the safety and health job position and safety and health certification were analyzed with t-test and Welch's t-test. For parameters that did not meet the assumption of equal variances, the Welch's t-test was performed. Results: Repeated PPE training improved the educational outcomes, and the improvements were significant in the 1st and 2nd respiratory PPE and safety and hygiene PPE training evaluations (p<.001). In terms of safety and health job position, repeated training led to improvements in educational outcomes, with significant improvements observed among supervisors and specialized health management institution workers in the 1st and 2nd training evaluations (p<.005). In terms of safety certification, repeated training led to improvements in educational outcomes, with significant improvements observed among both certified and non-certified individuals (p<.005). Regarding satisfaction with PPE training according to safety and health job positions, specialized health management institution workers showed greater satisfaction than supervisors, with significant differences in the satisfaction for expertise of lecture, work relevance, and lecturer's attitude (p<.001). Regarding satisfaction with PPE training according to safety and health certification, satisfaction was higher among certified individuals, with significant differences in satisfaction for work relevance and lecture attitude (p<.05) Conclusions: PPE education should be recommended to be provided as practical training. Repeated training can enhance educational outcomes for individuals with inadequate knowledge and understanding of PPE prior to education. For individuals with high levels of pre-existing knowledge and understanding of PPE, the results show that various training experiences should be provided to enhance their satisfaction. Therefore, it suggests that the workplace should actively seek educational media and methods to acquire expertise and skills in wearing personal protective equipment and improve the ability to use

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.247-264
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• 2023

Objectives: The study was to examine the features of three-dimensional(3D) face dimensions and to develop fit test panels for the labor population using respirators in South Korea. Methods: This study was part of the first-year work of a two-year-project conducted at Occupational Safety and Health Research Institute in 2021. After 3D head dimensions data were collected from Size Korea Center managing Sixth Size Korea databases, 3D face dimensions DB for the South Korean labor population was established for 21 items of face dimensions in line with the ISO/TS 16976-2 and KS A ISO 15535. With the South Korean labor population 3D face dimensions DB, in accordance with the ISO/TS 16976-2, the descriptive statistics of 3D face dimensions were calculated and two fit test panels were developed. Results: A total of 2,752 subjects were finally determined and they were 52.9% for male, 41.2% for the highest age-group of 15-29, and residents in the capital area. Mean and standard deviation were obtained for each of the 21 3D face dimensions items for the South Korean labor population. Among the items, male and female face widths were 137.6±5.7 mm, 133.2±5.0 mm, respectively. Male and female face lengths were 116.6±7.0 mm, 107.8±6.8 mm, respectively. Two new South Korean fit test panels, a bivariate test panel and a principal component analysis test panel, were developed using the 3D face dimensions DB as well. Conclusions: Using the 3D face dimensions DB, the mean and standard deviation were featured for each of the 21 items and also the two fit test panels were newly developed in the study. It is suggested that the study outputs should be utilized practically and effectively in selection, use, and management of respirators at work, expecting that respiratory protection can be furthermore improved for respirator users including labor population across the country.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.64-77
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• 2022

Objectives: The purpose of this study is to objectify the level of case management performance and the factors influencing performance, to improve the case management performance at the Korea Worker's Compensation & Welfare Service (KWCWS) on the basis of the recognition of the objective realities of case management by job coordinators at the KWCWS, to develop a model of case management fit for the KWCWS, and to provide a basis for establishing guidelines for standardized case management. Methods: A total of 156 questionnaires were distributed to job coordinators at the KWCWS's headquarters, six regional headquarters, and 55 branches. One hundred forty-one questionnaires were collected and 126 were analyzed statistically using SPSS 21.0. Factor analysis and reliability analysis were conducted to verify the validity and reliability of the main measurement items in the research model. Frequency analysis was conducted for general characteristics of survey subjects. Frequency analysis or descriptive statistics were conducted to identify the level of independent variables (case manager's individual variables, job variables, institutional and organizational variables). Dependent variables (case management performance) and the degree of correlation were analyzed through correlation analysis between research variables. Multiple regression analysis and hierarchical regression analysis were conducted to examine the effect of independent variables on case management performance. Results: The results of the study showed that the level of overall performance in the five stages of case management was ordinary, with an average level of 3.45 on a 5-point scale. Levels of performance by step were institutional approach and intake (3.69), assessment (3.63), goal setting and intervention planning (3.46), implementation of intervention plan (3.32), and evaluation and termination (3.20), in that order. The explanatory power of case management performance (overall) by case managers with the KWCWS was case manager's institutional and organizational variables, job variables, and individual variables, in that order. At each stage of case management, the explanatory power of a case manager's institutional and organizational variables was found to be the greatest. The model changes at each stage of case management assume similar aspects statistically. In hierarchical regression analysis, it was institutional support that had a significant effect on case management performance (overall), and institutional support had the greatest effect. The results of multiple regression analysis in which all variables are input simultaneously showed that institutional support and expertise as well as self-efficacy had a positive effect. However, case management work experience, expertise (technology), and autonomy were found to have a negative effect during the stage of case management performance. Conclusions: As a result of the study, it was confirmed that raising the case manager's expertise and support from the institution and organization are important factors to improve the level of case management performance. The research also derived practical ways of reinforcement of case manager capacity, institutional and organizational support, operation of rehabilitation-case management teams, and occupational health-related aspects.

• Industry Promotion Research
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• v.8 no.1
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• pp.111-117
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• 2023

Sampling inspection methods for quality control have been proposed a lot in the industry. However, the sampling inspection method for reliability, which is a quality over time, has been relatively less presented, and there are not many literatures that are clearly summarized. Therefore, this paper focuses on the reliability conformity test to verify that the reliability evaluation scale value of the target is satisfied during the reliability test. To this end, first, we look at the conditions that both consumers and producers can satisfy in terms of the OC curve and find out what sampling methods satisfy the desired level of producer risk and consumer risk. Next, two methods of the reliability sampling methods such as attribute and variable reliability sampling methods are examined. Specifically, the attribute reliability sampling method is a form of sampling plan where n samples are tested for a certain period of T hours and the lot is accepted if the number of failures is less than or equal to a certain number c. On the other hand, the variable reliability sampling method is a form of sampling plan where the lot is accepted if the reliability evaluation scale such as MTBF satisfies a certain standard. Both sampling plans may also use inspection tables.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.717-736
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• 2023

The purpose of this study is to develop a statistical program using data and artificial intelligence prediction models and apply it to one class in the sixth grade of elementary school to see if it is effective in improving students' statistical literacy. Based on the analysis of problems in today's elementary school statistical education, a total of 15 sessions of the program was developed to encourage elementary students to experience the entire process of statistical problem solving and to make correct predictions by incorporating data, the core in the era of the Fourth Industrial Revolution into AI education. The biggest features of this program are the recognition of the importance of data, which are the key elements of artificial intelligence education, and the collection and analysis activities that take into account context using real-life data provided by public data platforms. In addition, since it consists of activities to predict the future based on data by using engineering tools such as entry and easy statistics, and creating an artificial intelligence prediction model, it is composed of a program focused on the ability to develop communication skills, information processing capabilities, and critical thinking skills. As a result of applying this program, not only did the program positively affect the statistical literacy of elementary school students, but we also observed students' interest, critical inquiry, and mathematical communication in the entire process of statistical problem solving.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.1-13
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• 2023

This study forecasted the manpower demand of eco-friendly smart shipbuilding, whose importance and weight are increasing according to the environmental regulations of the IMO and the spread of the 4th industrial revolution technology. It predicted the shipbuilding industry manpower by applying various models of trend analysis and time series analysis based on data from 2000 to 2020 of Statistics Korea. It was found that the prediction applying geometric mean had the smallest gap among the trend and time series analysis methods in comparing between forecast results and actual data for the past 5 years. Therefore, the demand for manpower in the shipbuilding industry was predicted by using the geometric mean method. In addition, the manpower demand of smart eco-friendly ships wast forecasted by using the 2018 and 2020 manpower survey results of the Ministry of Trade, Industry and Energy and reflecting the trend of manpower increase in the shipbuilding industry. The result of forecasting showed that 62,001 person in 2025 and 85,035 people in 2030. This study is expected to contribute to the adjustment of manpower supply and demand and the training professional manpower in the future by increasing the accuracy of forecasting for high value-added eco-friendly smart ships.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.916-923
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• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.128-148
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• 2023

Dementia is an umbrella term that describes the loss of thinking, memory, attention, logical reasoning, and other mental abilities to the extent that it interferes with the activities of daily living. More than 50 million individuals worldwide live with dementia, which is expected to increase to 131 million by 2050. Recent research has shown that poor oral health increases the risk of dementia, while oral health declines with cognitive decline. In this narrative review, the literature was based on the "hypothesis" that dementia and oral health have a close relationship, and appropriate oral health and occlusal rehabilitation treatment can improve the quality of life of patients with dementia and prevent progression. We conducted a literature search in PubMed and Google Scholar databases, using the search terms "dementia," "major neurocognitive disorder," "dentition," "occlusion," "tooth loss," "dental prosthesis," "dental implant," and "occlusal rehabilitation" in the title field over the past 30 years. A total of 131 studies that scientifically addressed dementia, oral health, and/or oral rehabilitation were included. In a meta-analysis, the random effect model demonstrated significant tooth loss increasing the dementia risk 3.64-fold (pooled odds ratio=3.64, 95% confidence interval [2.50~5.32], P-value=0.0348). Tooth loss can be an important indicator of cognitive function decline. As the number of missing teeth increases, the risk of dementia increases. Loss of teeth can lead to a decrease in the ascending information to the brain and reduced masticatory ability, cerebral blood flow, and psychological atrophy. Oral microbiome dysbiosis and migration of key bacterial species to the brain can also cause dementia. Additionally, inflammation in the oral cavity affects the inflammatory response of the brain and the complete body. Conversely, proper oral hygiene management, the placement of dental implants or prostheses to replace lost teeth, and the restoration of masticatory function can inhibit symptom progression in patients with dementia. Therefore, improving oral health can prevent dementia progression and improve the quality of life of patients.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.77-84
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• 2024

In the fourth industrial-revolution era, the construction industry is transitioning from traditional methods to digital processes. This shift has been challenging owing to the industry's employment of diverse processes and extensive human resources, leading to a gradual adoption of digital technologies through trial and error. One critical area of focus is the safety management at construction sites, which is undergoing significant research and efforts towards digitization and automation. Despite these initiatives, recent statistics indicate a persistent occurrence of accidents and fatalities in construction sites. To address this issue, this study utilizes large-scale language-model artificial intelligence to analyze big data from a construction safety-management information network. The findings are integrated into on-site models, which incorporate real-time updates from detailed design models and are enriched with location information and spatial characteristics, for enhanced safety management. This research aims to develop a big-data-driven safety-management platform to bolster facility and worker safety by digitizing construction-site safety data. This platform can help prevent construction accidents and provide effective education for safety practices.