• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.25-38
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• 2021

The aim of this study is to protect workers and pedestrians from accidents at night or bad weather by attaching optical fiber to existing safety clothing that is made only with fluorescent fabrics and retroreflective materials. A safety vest was designed and manufactured by applying optical fiber, and energy-harvesting technology was developed. The safety vest was designed to emit light using the automatic flashing of optical fibers attached to the film, and an energy harvester was manufactured and attached to drive the light emission of the optical fiber more continuously. As a result, first, the vest wearer' body was recognized from a distance through the optical fiber and retroreflection, which helped prevent accidents. Thus, this concept helps in saving lives by preventing accidents during night-time work on the roadside or activities of rescue crew and sports activities, or by quickly finding the point of an accident with a signal that changes the optical fiber light emission. Second, to use the wasted energy, a piezoelectric-element power generation system was developed and the piezoelectric-harvesting device was mounted. Potentially, energy was efficiently produced by activating the effective charging amount of the battery part and charging it auxiliary. In the existing safety vest, detecting the person wearing the vest is almost impossible in the absence of ambient light. However, in this study, the wearer could be found within 100 m by the light emission from the safety vest even with no ambient light. Therefore, in this study, we will help in preventing and reducing accidents by developing smart safety clothing using optical fiber and energy harvester attached to save lives.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.105-116
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• 2020

The purpose of this study is to progress the limitations and disadvantages of existing safety clothing by applying high technology to current safety clothing that is produced and distributed only with fluorescent fabrics and retroreflective materials. Therefore, the industrial suspender-type safety belt and engineering technology are introduced, designed, and fabricated to help save a life in an emergency. First, the suspender-type safety belt to be developed is designed to emit light by LED attached to the film, and the body of the belt-wearer is recognized from a distance through retroreflection from the flashing LED. It aims to support people's safety by preventing accidents during roadside work, rescue activities, and sports activities at night. Second, with the development of advanced devices when the user is in an unconscious state due to distress or falls into an unconscious state due to distress or accident, the tilt sensor of the control unit attached to the belt automatically detects the angle of the human body and generates light and sound. It is intended to further enhance the utilization by mounting a sensing and signaling device that generates a distress signal and shaping it in the form of a belt attached to a vest that can be easily detached from the outside of the garment. When the wearer falls due to an accident, the tilt sensor of this belt detects the angle change and then the controller generates a high-frequency sound and repeated LED blinking signals at the same time. In the case of conventional safety vests, it is almost impossible to detect that the person is wearing a vest when there is no ambient light, but in case of the safety belts in this study, the sound and light signals of the safety belt enable us to find the wearer within 100 meters even when there is no ambient light.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.129-140
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• 2023

Following ISO 20471, in this study, first, two sets of safety clothes and safety vests were made by designing and attaching animal and bird patterns preferred by children to retroreflective films and black fabrics on those fluorescent fabrics and retroreflective materials prescribed by international standards. Second, by mounting a smart photonic device on the safety clothing so that the body can be recognized from a distance even without an ambient light source at night, children can emit three types of light depending on the situation with just one-touch of the button. From a result of comparison with visibility a day and night by dressing a mannequin in the made smart safety clothing, the difference in visibility was evident at night, it was confirmed that we can see the figure of a person even at a distance of approximately 70 m. Therefore, it is expected to contribute to the prevention of traffic and other accidents on the road, as the drivers driving at night or in bad weather can recognize a person from a distance. Third, in case of the energy is exhausted and cannot maintain the stability of the light-emitting function of the optical faber, we can use energy harvesting device, and the light-emitting time will be extended. As a result it comes up to emit light stably for a long time. And this prove that smart photonic safety clothing can also be used for night workers. Therefore, optical fiber safety clothing is expected to be highly wearable not only in real life but also in dark industrial sites due to stable charging by applying the energy harvesting provided by solar cells.

• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.