• Journal of the Korean Society of Industry Convergence
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• v.23 no.3
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• pp.419-425
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• 2020

There are many accident risks in logging operation of forestry such as struck by felled trees and caught in, under, or between felled trees. These accidents are primarily occurred by not keeping a safe distance between workers. According to the forestry safety instruction, workers are not supposed to go into the safety zone which is a circle with a radius of more 2 times the height of felling tree. However, this rule does not keep because of poor safety consciousness, poor sight and extreme noise of logging operation machines. This problem causes many major accidents every year. To solve this problem, we made forestry safety helmets based on IoT technology. These helmets have functions to make a visual and an acoustic alarm signal when reach the distance between workers within 20 meters. We developed the algorithm to operate the helmet's functions and conducted tests to check the functions. As a result of tests, we assured the normal system operating conditions.

• Journal of the Korea Institute of Building Construction
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• v.8 no.2
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• pp.131-136
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• 2008

Safety management is the most important factor in the construction industry. If the construction company don't control the risk, it causes the accident which give the company fatal loss. According to the Korea industrial safety analysis reports, the 25.72% of the disasters are from the construction industry, and the 13.6% construction disasters are caused by not properly using the safety gears. Therefore, this study is to investigate the Wearing Safety Gear by Occupational Classification and the Satisfaction in the Construction Field. The results are ; Carpenters are dissatisfied with the safety shoes and belt, re-bar workers are dissatisfied with the safety helmet and shoes, Concrete workers are dissatisfied with the safety helmet and goggles.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.33-41
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• 2022

Recently the use of personal mobility devices is rapidly increasing, and the businesses related to personal mobility devices are quickly growing as well. Although the use of personal mobility devices provides convenience for short distance movements, many problems occur due to the lack of safety devices and the absence of associated road traffic laws. The number of accidents caused by using personal mobility devices continues increasing every year, and the injuries or deaths are seriously happening with those accidents. When using personal mobility devices, there are basic safety precautions such as wearing a helmet, prohibiting boarding with more than two people, prohibiting boarding with more than 100kg, prohibiting using after drinking alcohol, and so on. However, it is exposed to traffic accidents because there is no way to check before using the system. Therefore, to ensure the user's safety in using the electric kickboard among personal mobility devices, this paper proposes a system that can check the user's safety state before using the electric kickboard. It is possible to safely use personal mobility devices and prevent accidents by proposing a system structure of the electric kickboard that can be used after checking for the use of more than two people, overweight, wearing a helmet, and drinking alcohol.

• Journal of the Korea Safety Management & Science
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• v.23 no.1
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• pp.9-15
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• 2021

The leading source of occupational fatalities is a portable ladder in Korea because it is widely used in industry as work platform. In order to reduce victims, it is necessary to establish preventive measures for the accidents caused by portable ladder. Therefore, this study statistically analyzed injury death by portable ladder for recent 10 years to investigate the accident characteristics. Next, to monitor wearing of safety helmet in real-time while working on a portable ladder, this study developed an object detection model based on the You Only Look Once(YOLO) architecture, which can accurately detect objects within a reasonable time. The model was trained on 6,023 images with/without ladders and safety helmets. The performance of the proposed detection model was 0.795 for F1 score and 0.843 for mean average precision. In addition, the proposed model processed at least 25 frames per second which make the model suitable for real-time application.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.399-407
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• 2019

In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.23-24
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• 2009

In dangerous Industry fields (eg. construction, shipbuilding, the mining industry, and so on) many employees have lost their life due to risky environment, so that costs of social and Industry have been increased. To solve this problem, we Implement u-helmet using temperature, humidity, illumination sensors and monitoring GUI system.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.57-63
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• 2021

The purpose of this study is to develop a Smart Helmet to safeguard forest firefighting crews and provide on-site information in real time. The Smart Helmet for forest firefingting crews is equipped with a camera, video/voice communication module, GPS, Bluetooth, and LTE module to promote the safety of them, and through the Smart Helmet, the site situation is is transmitted in real time, and full duplex communication is possible. As a result of testing using the Smart Helmet, the control center was able to receive on-site information and communication with on-site forest firefighting crews. Through site evaluation and user evaluation, it was confirmed that the Smart Helmet needs to be improved. The developed Smart Helmet can be used in various ways in forest disasters and forest industry.

• Journal of the Korean Institute of Gas
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• v.17 no.6
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• pp.52-57
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• 2013

In this study, the strain energy density, stress and deformation behaviors have been analyzed as functions of a thickness and a force area of protective helmets with and without an extruder on the top of the shell structure using the finite element method. The strain energy density in which is related to the absorption capacity of an impact energy transfer is one of a key element of the helmet safety. The FEM analyzed results show that when the impulsive force of 4,540N is applied on the top surface of the helmets, the maximum stress is linearly reduced for an increased area of impact forces. But, the maximum strain energy density has been reduced for the increased force area. The reduced strain energy density may increase the impulsive forces transferred to the head and neck of helmet wearers, which may decrease the impact energy absorption safety of the helmets. In thus, it is safer design of the helmet in which has an extruded structure on the summit surface, but the modified helmet may decrease the impact energy absorption capacity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.77-83
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• 2019

Major causes of industrial accidents include falls and gas leak. The existing safety helmet and smart device combination products are focused on convenience, so the functions to prevent such accidents are insufficient. We developed a smart helmet focusing on fall accident detection and gas leak detection. We also developed management system to manage workers efficiently. Its core function is to detect dangerous conditions of employees, to communicate with managers and to confirm the situations of workers. The effectiveness of the combustible gas measurement capability was verified through experiments. However, since a significant amount of power consumption is founded due to continuous operation of the board and the sensor, countermeasures such as replacing with a large capacity battery are required.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1226-1274
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• 2022

In this paper, we dealt with a safety helmet for detecting dangerous situations that focuses on falling accidents and gas leaks, which are the main causes of industrial accidents. the fall situation range was set through gravity acceleration measurement using an acceleration sensor, and as a result, a fall detection rate of 80% could be confirmed. .In addition, the dangerous gas concentration was measured through a gas sensor, and when a digital value of 188 or more was output through a serial monitor, it was determined as a gas dangerous situation, and a fall warning message and a gas warning message could be checked through a smart-phone application produced based on the app inventor program.