• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.11-17
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• 2012

In recent years, there has been a popularity rose up on Smart TV (Smart Television) usage at home. Therefore, it is also have increase the demand on grid computing system. Smart TV has a variety of platform and usage compare to PC (Personal computer). Base on this, it is difficult to apply a traditional grid system on Smart TV. One major reason are concerning the small idle time compare to PC. To overcome this problem, this paper will propose a Javascript grid system and introducing a new scheduling policy that best suit for a smart TV. We have conduct an experiment on the proposed method. The result provides an average of 1.78 percent, which is improved compare to the traditional method which is only provides an average of 0.09 percent.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.246-252
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• 2020

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.23-49
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• 2014

Development of smart devices and the network of the company change the way of working. 'Smart work' is a type of work method expected to handle business regardless of the time and space. Therefore, 'Smart Work' is being introduced and gradually expanded in variety of companies. However, despite this popularity its effect is under question. This article reports on the effect of 'Smart Work' through literature review and analyzed the relationship among the worker's job satisfaction, smart work continued usage. The results of this study are expected to help companies to establish strategies connected with 'Smart Work.'

• Smart Media Journal
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• v.12 no.3
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• pp.120-128
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• 2023

There are various ICT technologies continuously being developed to reduce damage by industrial accidents. And research is being conducted to minimize damage in case of industrial accidents by utilizing sensors, IoT, big data, machine learning and artificial intelligence. In this paper, we propose a design method for a smart device capable of multilateral communication between devices and smart repeater in the communication shaded Areas such as closed areas of industrial sites, mountains, oceans, and coal mines. The proposed device collects worker's information such as worker location and movement speed, and environmental information such as terrain, wind direction, temperature, and humidity, and secures a safe distance between workers to warn in case of a dangerous situation and is designed to be attached to a helmet. For this, we proposed functional requirements for smart devices and design methods for implementing each requirement using sensors and modules in smart device. And we derived evaluation items for performance evaluation of the smart device and proposed an evaluation environment for performance evaluation in mountainous area.

• Journal of Korean Library and Information Science Society
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• v.45 no.1
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• pp.297-317
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• 2014

This study is to produce baseline data, in its purpose of improving the quality of life of industrial workers as well as the business competitiveness, though improvements in reading culture. The data produced in this study will be based on questionnaire, regarding sub-topics of the reading environment, the status of reading culture, and remaining demands. The results argue that the responding workers in Siheung Smart Hub Complex had inadequate environment of reading, while had weak status of reading culture. We propose the ways of improvements in the reading culture of the worker in Siheung Smart Hub Complex, as well as their reading infrastructure.

• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.7-8
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• 2017

각종 산업현장에서 작업자들의 안전 불감증으로 인해 발생하는 안전사고는 매년 꾸준히 증가하고 있는 추세이다. 본 논문에서 제안하는 스마트 작업자 안전벨트 및 행동인식 기반 위험경보 시스템은 이러한 상황을 방지하고자 작업자가 안전벨트의 훅을 제대로 걸지 않고 일을 진행하는 경우, 작업장 내에서 뛰어다니는 경우, 잘못된 자세로 일하는 경우를 시스템에서 인지하고 작업자, 관리자에게 알림을 줌으로서 작업자의 안전사고를 예방할 수 있도록 하였다.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.519-531
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• 2023

With the aim of preventing safety accidents at construction sites, the company aims to create safe behaviors intended through variables called smart safety and health activities to help reduce industrial accidents. Purpose: It analyzes how smart safety and health activities affect accidents caused by unsafe behavior and changes in worker behavior, which is the root cause, and verifies the hypothesis that it helps prevent safety accidents and protect workers' lives. Method: Smart safety and health activities were selected as independent variables (X), and intended safety and anxiety, which are workers' behavioral intentions, were set as dependent variables (Y), attitude and subjective norms, and planned behavioral control as parameters (M). Exploratory factor analysis, discriminant validity analysis, and intensive validity analysis of safety and health activities were used to analyze the scale's reliability and validity. To verify the hypothesis of behavior change, the study was verified through Bayesian model analysis and MC simulation's probability density distribution. Result: It was found that workers who experienced smart safety and health activities at construction sites had the highest analysis of reducing unstable behavior and performing intended safety behavior. The research hypothesis that this will affect changes in worker behavior has been proven, the correlation between variables has been verified in the structural equation and path analysis of the research analysis, and it has been confirmed that smart safety and health activities can control and reduce worker instability. Conclusion: Smart safety and health activities are a very important item to prevent accidents and change workers' behavior at construction sites.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.