• 조명전기설비학회논문지
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• 제29권10호
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• pp.25-33
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• 2015

LED lightings combined with IT technology can provide variable illumination environments that can be controlled by users according to their emotional need and preferences. This is one of the most beneficial functions compared with conventional lightings offering only fixed color and brightness. There is however lack of analysis data for creating practical lighting solutions satisfying user preferences in a wide range of applications from residential to commercial places. To materialize the technical advantages of user-controllable LED lightings, more observation data are required in various situations. Therefore, dissimilar emotional needs are determined in the present study for compartmental office spaces (staff lounge room, meeting room and desk job place) through subjective experiments by 45 observers. The optimum lighting conditions (CCT and illuminance) are finally obtained using Response Surface Method and relevant prediction functions are also deduced. The final outcome can be applied for making user-preferred office illumination products.

• 지역사회간호학회지
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• 제29권4호
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• pp.476-487
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• 2018

Purpose: The present study is focused on understanding weight perception related with individual- and school-level multifactorial origins, underestimated and overestimated respectively, in normal body weight adolescents. Methods: Using the 2017 Korea Youth Risk Behavior Web-based Survey data of 45,902 students from 799 secondary schools, a multilevel multinomial logistic regression analysis was performed where adolescents (level1) were nested within schools (level 2). Results: At the school level, the average school body mass index (BMI, $kg/m^2$) and physical education were associated with weight perception among both boys and girls. In boys, geographic areas were associated with weight underestimation. At the individual level, perceived economic status, weekly allowance, BMI ($kg/m^2$), smartphone usage time (hrs/day) and perceived stress were associated with weight perception among both boys and girls. Age, paternal education, academic achievement and alcohol use were associated with weight perception among girls, while part-time job and physical activity were associated with weight perception among boys. Conclusion: Our findings underscore the importance of individual- and school-level environments in developing correct weight perception and have implications for school health education to establish healthy lifestyle behaviors for all adolescents.

• 동아시아경상학회지
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• 제10권4호
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• pp.55-64
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• 2022

Purpose - Corporate reading management requires employees capable of doing their job since employee performance is crucial to the firm's profitability. The objective of this study was to evaluate the positive effects of corporate reading management on employee management. It focuses on factors increasing employee productivity and the impacts corporate management brings about on the overall productivity of any kind of company. Research design, Data, and methodology - The current authors have investigated and conducted the qualitative content analysis (QCA) to obtain the adequate textual dataset in the prior and current literature. One of the primary benefits of doing research using content analysis is that it enables the researcher to employ a combination of qualitative and quantitative research methodologies. Result - Based on the review of the prior literature, the present authors found that there are four corporate reading management solutions to improve employee performance, following those instructions - A. Increased Motivation and Productivity, B. Employee Development, C. Businesses' Objective Alignment, and D. Improved Work Environment. Conclusion - This study addressed and figured out different factors that reading corporate can apply to influence employee performance, thus increasing the company's productivity. Future research should evaluate the adverse effects businesses might face from unfavorable working environments. The research also needed to address how ethics influences employee performance.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.158-163
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• 2024

Virtual world technology is driving major advances in education, entertainment, and professional training. Metaverse and extended reality (XR) technologies maximize immersion to enhance learning, provide global learning environments, and expose students to situations that are difficult to experience in real life. Career exploration is an important developmental task in adolescence, and virtual training maximizes learning by providing life-like experiences with imagery training. Virtual training overcomes spatial, financial, performance, and situational constraints and is effective in a variety of fields, including military and disaster training. It provides customized learning for various users such as youth, job seekers, and people with disabilities, deepening their understanding of professional activities and improving their problem-solving skills. It also improves the quality of learning through repetitive learning and contributes to the improvement of teamwork and communication skills, and helps to solve financial problems by using unlimited internal resources and space in virtual space, and enables people with disabilities to perform in various professions. This paper investigated the value of virtual training as a comprehensive educational tool through an economical and efficient learning experience.

• Safety and Health at Work
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• 제15권3호
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• pp.263-270
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• 2024

Background: Long-haul truck drivers (LHTD) experience disproportionately greater chronic disease risk, which may be influenced by both occupational and lifestyle factors. This study aimed to explore the multilevel factors associated with LHTD's diet and physical activity (PA). Methods: Thirty in-depth interviews were conducted with LHTD in the Southern California border region. Interview questions captured occupational and lifestyle factors relating to PA and diet at multiple levels and were analyzed using thematic analysis. Results: Emergent themes relating to both diet and PA included time constraints, attitudes and perceived beliefs, and accessibility of environments to engage in PA and healthy eating. Themes specific to PA were weather conditions and flatbed job duties. Themes specific to diet included access to refrigerators/microwaves and social interactions. Conclusion: Findings from this study can inform the development of tailored, multi-level interventions to encourage PA and healthy dietary behaviors among LHTD.

• 대한안전경영과학회지
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• 제26권3호
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• pp.101-106
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• 2024

The health and working conditions of employees have become increasingly important issues in modern society. In recent years, there has been a continuous rise in problems related to the deterioration of workers' alth, which seriously affects their safety and overall quality of life. Although existing research has investigated various factors affecting workers' health and working conditions, there is still a lack of studies that scientifically analyze and identify key variables from the vast number of factors. This study employs the Lasso (Least Absolute Shrinkage and Selection Operator) technique to mathematically analyze the key variables influencing workers' health status and satisfaction with their working environment. Lasso is a technique used in machine learning to identify a small number of variables that impact the dependent variable among a large set of variables, thereby reducing model complexity and improving predictive accuracy. The results of the study can be utilized in efficiently improving workers' health and working environments by focusing on a smaller set of impactful variables.

• 품질경영학회지
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• 제52권3호
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• pp.509-527
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• 2024

Purpose: This study aims to investigate the factors influencing the adoption of Generative AI in the workplace, focusing on both enablers and inhibitors. By employing the dual factor theory, this research examines how knowledge support, customization, entertainment, perceived risk, realistic threat, and identity threat impact the intention to adopt Generative AI technologies such as ChatGPT. Methods: Data were collected from 192 participants via MTurk, all of whom had experience using Generative AI. The survey was conducted in June 2024, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure the validity and reliability of the measurement model. Attention-check questions were used to ensure data quality, and participants provided demographic information at the end of the survey. Results: : The findings reveal that knowledge support and entertainment significantly enhance the intention to adopt Generative AI, whereas realistic threat poses a substantial barrier. Customization, perceived risk, and identity threat did not significantly affect adoption intentions. Conclusion: This study contributes to the literature by addressing the gap in understanding the adoption mechanisms of Generative AI in professional settings. It highlights the importance of promoting AI's knowledge support and entertainment capabilities while addressing employees' concerns about job security. Organizations should emphasize these benefits and proactively mitigate perceived threats to foster a positive reception of Generative AI technologies. The findings offer practical implications for enhancing user acceptance and provide a foundation for future research in this area.

• 한국산업정보학회논문지
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• 제28권6호
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• pp.53-62
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• 2023

본 논문은 국내 조선산업의 요양 재해 현황에 나타난 미숙련 기술인력들의 높은 재해 발생률을 낮추기 위한 방안으로 XR(Extended reality) 기술을 활용한 훈련 콘텐츠 도입의 필요성을 윈치를 활용한 케이블 포설을 중심으로 제시하였다. 국내 조선산업의 요양재해율은 타 제조업에 비해 평균 97.4%(2017~2020)나 높았으며, 특히 조선산업요양 재해의 31.8% 이상이 근속 기간 6개월 미만의 근로자들에게 발생하여 이는 신규입사자들이 업무에 대한 정보 부족과 미숙련도 및 작업환경에 익숙하지 못한 것이 그 원인으로 보인다. 최근 조선산업은 경기 회복으로 많은 신규인력이 유입되고 있으며, 이로 인해 재해 발생률이 높아질 가능성이 있다. 이런 점은 단기간에 신규인력들을 작업환경에 익숙해지는 데 도움이 되는 훈련방법을 도입할 필요가 있음을 말해준다. 국내 조선소의 교육훈련은 용접, 도장, 기계설치, 전기설치 등 특정 기술의 숙련에 치우쳐 있으며 작업환경에 익숙해질 수 있는 훈련은 작업용 2D 도면을 이용하여 선박 건조방법 등을 설명하는 단편적인 교육에 머무르고 있다. 이러한 이유로 현장에서는 다시 OJT(On-the-Job Traning)를 반복 실시하고 있는 실정이다. 본 논문에서는 XR 기술을 활용한 훈련의 도입을 제안하고 구체적인 예로 Unity로 선박에서 윈치를 활용한 케이블 포설(Pulling) 작업 과정을 XR 기반 훈련 콘텐츠로 구현하였다. 개발된 콘텐츠를 활용하면 신규작업자들이 가상공간에서 시뮬레이션을 통해 실제 작업 과정을 미리 경험하고, 이를 통해 신규인력이 작업환경에 익숙해지는 데 도움을 줄 수 있는 보다 효과적인 훈련 콘텐츠가 될 수 있음을 예증하였다.