• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.187-195
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• 2015

In this paper, we examined the effect of positive affect and negative affect on the life satisfaction in the workplace. Also, this study focused on an empirical test of the role of work self-efficacy and work meaningfulness in the subjective well-being of office worker. To achieve this purpose, we suggested a research model consisting of factors such as work self-efficacy, work meaningfulness, positive affect, negative affect, life satisfaction. Data was collected using the survey method, and analyzed using structural equation model. According to PLS analysis, first, lower negative affect was associated with higher life satisfaction. Secondly, work meaningfulness was a very important predictor for the subjective well-being of office worker.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.185-191
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• 2016

This study verified the structural relations among empowering leadership, self-efficacy and organizational citizenship behavior (OCB) in social welfare organizations. To this end, the study chose self-efficacy as an intervening variable and OCB as a dependent variable by considering the characteristics of social welfare organizations and their works. The study found that empowering leadership has a significantly positive impact on self-efficacy while both self-efficacy and empowering leadership have a significantly positive impact on OCB. Self-efficacy has meditating effects on the relationship between empowering leadership and OCB. Based on these results, the study suggests policy implications for the improvement of social workers' self- efficacy and OCB through empowering leadership in social welfare centers.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.149-155
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• 2003

The purpose of this study is to analyze the degree of product's qualify improvement by the effectiveness which is identified as worker's mindset improvement, a management ability improvement and quality system's establishment in the existing papers as well as the construction of a quality assurance system by acquiring the ISO 9000 series certification. In order to identify the product's quality improvement, the analysis of Garvin's eight dimensions of quality characteristics as well as the analysis a design quality improvement, a manufacturing quality improvement, a service quality improvement are conducted through this study.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.157-162
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• 2018

The purpose of this study is to investigate the relationship between resilience, burnout, and self-efficacy of Public Social Workers and to draw implications for effective management of Public Social Workers. In order to accomplish this study purpose, the resilience of Public Social Workers as an independent variable, burnout as a dependent variable of occupational identity, and self-efficacy as a moderating variable were selected. The causal relationship between resilience and burnout and the moderating effect of self-efficacy were analyzed. As a result of the analysis, the resilience of Public Social Workers showed a significant effect on burnout, and the effect of resilience on burnout varied according to self-efficacy, so there was a moderating effect of self-efficacy. Based on the results of this analysis, the theoretical implications and policy implications of this study are suggested.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.145-151
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• 2018

While commercialization of IoT technologies in the safety management sector is being promoted in terms of industrial safety of large indoor businesses, implementing a system for risk management of small outdoor work sites with frequent site movements is not actively implemented. In this paper, we propose an efficient dynamic workload balancing strategy which combined low-power, wide-bandwidth (LPWA) communication and low-power Bluetooth (BLE) communication technologies to support customized risk management alarm systems for each individual (driver/operator/manager). This study was designed to enable long-term low-power collection and transmission of traffic information in outdoor environment, as well as to implement an integrated real-time safety management system that notifies a whole field worker who does not carry a separate smart device in advance. Performance assessments of the system, including risk alerts to drivers and workers via Bluetooth communication, the speed at which critical text messages are received, and the operation of warning/lighting lamps are all well suited to field application.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.109-118
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• 2022

Workers' anomalous trajectories allow us to detect emergency situations in the workplace, such as accidents of workers, security threats, and fire. In this work, we develop a scheme to detect abnormal trajectories of workers using the edit distance on real sequence (EDR) and density method. Our anomaly detection scheme consists of two phases: offline phase and online phase. In the offline phase, we design a method to determine the algorithm parameters: distance threshold and density threshold using accumulated trajectories. In the online phase, an input trajectory is detected as normal or abnormal. To achieve this objective, neighbor density of the input trajectory is calculated using the distance threshold. Then, the input trajectory is marked as an anomaly if its density is less than the density threshold. We also evaluate performance of the proposed scheme based on the MIT Badge dataset in this work. The experimental results show that over 80 % of anomalous trajectories are detected with a precision of about 70 %, and F1-score achieves 74.68 %.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.64-71
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• 2022

Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.884-886
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• 2021

각종 건설 현장에서 안전모 미착용은 주된 위험 요인 중 하나이다. 현장에서 관리자가 직접 작업자들의 안전모 착용 여부를 감독할 수 있지만 관리자가 항상 관리가 가능한 장소에 있어야 하는 한계가 있다. 본 연구에서는 안전모 착용 여부를 딥러닝 기반으로 인식하여 건설 현장에서의 안전 관리를 할 수 있도록 하는 시스템을 제안한다. 이를 위해 대표적인 객체 인식 알고리즘인 YOLO를 사용하여 현장에서의 안전모 착용 여부를 인식한다. 다음으로는 인식된 결과를 바탕으로 위험 상황을 판단하는 알고리즘을 제안한다. 제안된 시스템을 활용하면 효율적으로 건설 현장의 위험 상황을 관리할 수 있을 것으로 기대된다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.221-222
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• 2021

Conventionally, in material management at a construction site, the type, size, and quantity of materials are identified by the eyes of the worker. Labor-intensive material management by manpower is slow, requires a lot of manpower, is prone to errors, and has limitations in that computerization of information on the identified types and quantities is additionally required. Therefore, a method that can quickly and accurately determine the type, size, and quantity of materials with a minimum number of workers is required to reduce labor costs at the construction site and improve work efficiency. In this study, we developed an automated convolution neural network(CNN) and computer vision technology-based rebar size and quantity estimation system that can quickly and accurately determine the type, size, and quantity of materials through images.