• Composites Research
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• v.19 no.5
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• pp.20-27
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• 2006

Self-sensing and interfacial evaluation of Ni nanowire/polymer composites were investigated using electro-macromechanical technique, which can be used fur a feasible sensing measurement on tensile and compressive loading/consequent unloading, temperature, and humidity. Mechanical properties of Ni nanowire with different aspect ratio and adding contents in either epoxy or silicone composites were measured indirectly using electro-pullout test under uniform and non-uniform cyclic loadings. Comparing apparent modulus with the conventional mechanical tensile modulus of Ni nanowire/epoxy composites, the trends were consistent with each other. Ni nanowire/epoxy composites showed the sensing response on humidity and temperature. Self-sensing on applied tensile and compressive loading/unloading was also responded for Ni nanowire/silicone composites via electrical contact resistivity showing the opposite trend between tension and compression. It can be due to the different electrically-interconnecting mechanisms of dispersed Ni nanowires embedded in silicone matrix.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.762-765
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• 2003

에틸렌글리콜(ethyleneglycol)을 이용한 폴리비닐에테르(polyvinyl ether)와 같은 고분자물질을 활용하여 Co$^{30}$ 방사선원으로부터 ${\gamma}$선을 조사시켜, 고분자 물질의 성질을 변화시킴으로써 화학, 전기전자, 환경 및 기타 여러 가지 응용분야에 적용하여 이용할 수 있는 기술을 개발하였다. 이를 바탕으로 관련재료의 단량체(monomer)로부터 중합체(polymer)를 합성하여 water swelling을 시키는 과정에서 각종 (금속) 이온을 흡입-제거하는 방법의 환경복원기술, 흡입 금속이온을 표면에 밀집시켜 금속막을 형성하는 응용기술, 생체조직의 대용물질로 활용하는 의용공학 및 열감지특성(thermal sensitive property) 또는 pH 감지특성(pH sensitive property)을 이용하여 의용기술에 적용하는 polymer 응용기술 등의 폭넓은 활용을 위하여 그 일환으로 전도성 고분자 제조기술로의 활용가능성을 연구하였다.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.172-174
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• 2020

기존의 절대위치 감지 방식은 초기위치를 알기 위하여 이동자의 위치를 초기화해야 하고 생산 단가가 높다는 단점을 보완하기 위해 MR 센서를 이용하여 비접촉 방식으로 선형모터 이동자 절대위치를 판별하는 시스템을 제안하였다. 센서값을 이용하여 절대위치를 판별해야 하기 때문에 ADC 기능과 실시간 연산 기능이 필요하다. 때문에 8bit MCU(Atmega324PA)를 이용했다. 본 논문에서 이용한 MR 센서의 출력 변동폭이 작기 때문에 Instrumentation Amplifier를 이용하여 증폭된 출력을 MCU로 읽어 사용했다. 제안 시스템의 회로 및 알고리즘을 구현하였고 이를 이론적, 실험적 분석을 통해 동작 및 타당성을 검증하였다.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.588-597
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• 2016

According to an annual industrial accident report from Ministry of Employment of Labor, among the various types of accidents, the number of accidents from construction industry increases every year with the percentage of 27.56% as of 2014. In fact, this number has risen almost 3% over the last four years. Currently, among the industrial accidents, heavy machinery causes most of the tragedy such as collision or narrowness. As reported by the government, most of the time, both heavy machinery drivers and workers were unaware of each other's positions. Nowadays, however when society requires highly complex structures in minimal time, it is inevitable to allow heavy construction equipments running simultaneously in a construction field. In this paper, we have developed Approach Detection System for excavator in order to reduce the increasing number. The imaged based Approach Detection System contains camera, approach detection sensor and Around View Monitor (AVM). This system is also applicable in a small scale construction fields along with other machineries besides excavators since this system does not require additional communication infra such as server.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• 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

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.7-15
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• 2010

Research of this paper focused on developing and demonstrating of algorithm with the figures of difference between historical traffic pattern data and real-time traffic data to decide on what the incident is. The aim of this dissertation is to develop incident detection algorithm which can be understood and modified easier to operate. To establish traffic pattern of this algorithm, weighted moving average method was applied. The basis of this method was traffic volume and speed of the same day and time at the same location based on 30-second raw data. The model was completed by a serious of steps of process-screening process of error data, decision of the traffic condition, comparison with pattern data, decision of incident circumstances, continuity test. A variety of parameter value was applied to select reasonable parameter. Results of application of the algorithm came out with figures of average detection rate 94.7 percent, 0.8 percent rate of misinformation and the average detection time 1.6 minutes. With these following results, the detection rate turned out to be superior compared with result of existing model. Applying the concept of traffic patterns was useful to gain excellent results of this study. Also, this study is significant in terms of making algorithm which theorized the decision process of actual operators.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.39-48
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• 2019

Purpose: This study aims at developing and implementing Smart Safety Management System based on IoT/ICT Convergence for safety of high-risk groups working at disaster or industrial field. Its functions are as follows. Method: We will develop three devices for keeping the safety of high-risk jobs: Sensor of inactivity, Lora based Refitting technology for communication between high-risk workers, and Lora Gateway for monitoring entire situations. Then we will test three devices in respect of their functions, and propose their applicabilities in the field. Results: The system can send and receive safety tags and danger signals by which sensor technology can detect dangerous state of workers. And its command terminal was developed by low-power wireless communication technology and LoRa Gateway, which can fulfill the lifting functions between safety tags. And, furthermore, the command terminal can monitor dangerous situations of disaster sites in real time and can perform the preemptive rescues. Conclusion: This study proves the functional efficacy of Smart Safety Management System for worker safety in various high-risk occupational groups, and also suggests ways to secure worker safety in disaster area and various high risk industrial sites.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.21-28
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• 2020

In this paper, a new type of electric wheelbarrow is proposed and developed. The developed electric wheelbarrow is equipped with an attitude reference system(ARS) sensor, which consists of 3-axis acceleration sensor and 2-axis Gyro sensor so that it can estimate pitch angle and roll angle. When an operator tilts the wheelbarrow up and down, the pitch angle is detected. The sign of the pitch angle is interpreted as the operator's intention for moving the wheelbarrow forward or backward and the controller drives the wheel of the wheelbarrow with the velocity according to the magnitude of the detected pitch angle. A cargo box of the wheelbarrow is designed to rotate and is controlled to maintain level always, so an operator can handle the electric wheelbarrow easily and safely. The wheelbarrow consists of an in-wheel motor, a DC motor, motor drives, an ARS sensor considering economical use in industrial field. Three experiments are performed to verify the feasibility and stability of the electric wheelbarrow.