• 제목/요약/키워드: accidents detection

검색결과 494건 처리시간 0.03초

IEC60479 인체 임피던스 모델에 근거한 직류누설전류의 특성 및 검출 알고리즘 (Detection Algorithm and Characteristics on DC Residual Current based on Analysis of IEC60479 Impedance Model for Human Body)

  • 김용중;이진성;김효성
    • 전력전자학회논문지
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    • 제23권5호
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    • pp.305-312
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    • 2018
  • DC distribution systems has recently taken the spotlight. Concerns over human safety and stability facility are raised in DC distribution systems. Std. IEC 60479 provides basic guidance on "the effects of shock current on human beings and livestock" for use in the establishment of electrical safety requirements and suggests an electrical impedance of the human body. This study analyzes impedance spectrums based on the electrical equivalent impedance circuit for the human body; human body impedances measured by experiments are analyzed below the fundamental frequency (60 Hz). The analysis shows that the equivalent impedance circuit for the human body should be modified at least in low-frequency range below the fundamental frequency (60 Hz). The DC residual current detection method that can classify electric shock accidents of humans and electric leakages of facilities is proposed by applying the analysis result. The detection method is verified by experiments on livestock.

A Study on Traffic Light Detection (TLD) as an Advanced Driver Assistance System (ADAS) for Elderly Drivers

  • Roslan, Zhafri Hariz;Cho, Myeon-gyun
    • International Journal of Contents
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    • 제14권2호
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    • pp.24-29
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    • 2018
  • In this paper, we propose an efficient traffic light detection (TLD) method as an advanced driver assistance system (ADAS) for elderly drivers. Since an increase in traffic accidents is associated with the aging population and an increase in elderly drivers causes a serious social problem, the provision of ADAS for older drivers via TLD is becoming a necessary(Ed: verify word choice: necessary?) public service. Therefore, we propose an economical TLD method that can be implemented with a simple black box (built in camera) and a smartphone in the near future. The system utilizes a color pre-processing method to differentiate between the stop and go signals. A mathematical morphology algorithm is used to further enhance the traffic light detection and a circular Hough transform is utilized to detect the traffic light correctly. From the simulation results of the computer vision and image processing based on a proposed algorithm on Matlab, we found that the proposed TLD method can detect the stop and go signals from the traffic lights not only in daytime, but also at night. In the future, it will be possible to reduce the traffic accident rate by recognizing the traffic signal and informing the elderly of how to drive by voice.

Reproduction strategy of radiation data with compensation of data loss using a deep learning technique

  • Cho, Woosung;Kim, Hyeonmin;Kim, Duckhyun;Kim, SongHyun;Kwon, Inyong
    • Nuclear Engineering and Technology
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    • 제53권7호
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    • pp.2229-2236
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    • 2021
  • In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

선삭공정에서 딥러닝 영상처리 기법을 이용한 작업자 위험 감소 방안 연구 (A Study on Worker Risk Reduction Methods using the Deep Learning Image Processing Technique in the Turning Process)

  • 배용환;이영태;김호찬
    • 한국기계가공학회지
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    • 제20권12호
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    • pp.1-7
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    • 2021
  • The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

가상 환경에서의 딥러닝 기반 폐색영역 검출을 위한 데이터베이스 구축 (Construction of Database for Deep Learning-based Occlusion Area Detection in the Virtual Environment)

  • 김경수;이재인;곽석우;강원율;신대영;황성호
    • 드라이브 ㆍ 컨트롤
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    • 제19권3호
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    • pp.9-15
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    • 2022
  • This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

마이크로그리드 전력변환장치용 커패시터 고장 검출 기법 (Capacitor Failure Detection Technique for Microgrid Power Converter)

  • 이우현;송광철;안준재;박성미;박성준
    • 한국산업융합학회 논문집
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    • 제26권6_2호
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    • pp.1117-1125
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    • 2023
  • The DC part of the DC microgrid power conversion system uses capacitors for buffers of charge and discharge energy for smoothing voltage and plays important roles such as high frequency component absorption, power balancing, and voltage ripple reduction. The capacitor uses an aluminum electrolytic capacitor, which has advantages of capacity, low price, and relatively fast charging/discharging characteristics. Aluminum electrolytic capacitors(AEC) have previous advantages, but over time, the capacity of the capacitors decreases due to deterioration and an increase in internal temperature, resulting in a decrease in use efficiency or an accident such as steam extraction due to electrolyte evaporation. It is necessary to take measures to prevent accidents because the failure diagnosis and detection of such capacitors are a very important part of the long-term operation, safety of use, and reliability of the power conversion system because the failure of the capacitor leads to not only a single problem but also a short circuit accident of the power conversion system.

분광정합 및 혼합 분석 방법을 활용한 위험·유해물질 스티렌 탐지 (Hazardous and Noxious Substances (HNSs) Styrene Detection Using Spectral Matching and Mixture Analysis Methods)

  • 박재진;박경애;김태성;이문진
    • 해양환경안전학회지
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    • 제28권spc호
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    • pp.1-10
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    • 2022
  • 국내외 해상 위험·유해물질(Hazardous and Noxious Substances, HNS) 물동량이 증가함에 따라 HNS 유출 사고의 위험성이 점차 높아지고 있다. 해상에 유출된 HNS는 해양생태계 파괴를 비롯한 해양환경 오염 및 인명피해를 유발하며, 화재 및 폭발 등을 동반한 2차 사고 발생 가능성도 존재한다. 따라서 해상 HNS의 신속한 탐지와 각 물질 특성에 적합한 방제전략을 수립해야 한다. 본 연구에서는 초분광 원격탐사에 기반한 지상 HNS 유출 실험 과정 및 탐지 알고리즘 적용 결과를 제시하고자 한다. 이를 위해 프랑스 브레스트 지역의 야외 풀장에서 스티렌을 유출한 후 초분광 센서를 활용한 동시 관측을 수행하였다. 순수 스티렌 및 해수 스펙트럼은 주성분 분석(principal component analysis, PCA) 및 N-Findr 기법을 적용하여 추출하였으며, 또한 spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM)을 포함한 분광정합 기법을 적용하여 초분광 영상 내 화소들을 스티렌 및 해수로 분류하였다. 그 결과 SDS 및 SSV 기법이 우수한 스티렌 탐지 결과를 보여주었으며, 스티렌 총 면적은 약 1.03 m2로 추정되었다. 본 연구는 해상 HNS 모니터링에 주요 역할을 할 것으로 기대된다.

항공 초분광 원격탐사 실험 기반 선박 스펙트럼 분석 및 탐지 (Spectrum Analysis and Detection of Ships Based on Aerial Hyperspectral Remote Sensing Experiments)

  • 박재진;박경애;김태성;이문진
    • 한국지구과학회지
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    • 제45권3호
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    • pp.214-223
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    • 2024
  • 최근 해상 교통량 증가 및 연안 중심의 레저활동으로 인해 다양한 해양사고가 발생하고 있다. 그 중 선박사고는 인명 및 재산 피해를 유발할 뿐만 아니라 기름 및 위험·유해물질 유출을 동반한 해양 오염사고로 이어질 가능성이 크다. 따라서 해양사고 대비 및 대응을 위한 지속적인 선박 모니터링이 필요하다. 본 연구에서는 해상 선박 모니터링 체계 구축을 위한 초분광 원격탐사 기반의 항공 실험 수행 및 선박탐지 결과를 제시하였다. 한반도 서해 궁평항 인근 해역을 대상으로 초분광 항공관측을 수행하였으며, 사전에 다양한 선박 갑판에 대한 분광 라이브러리를 구축하였다. 탐지 방법으로는 spectral correlation similarity (SCS) 기법을 사용하였으며 초분광 영상과 선박 스펙트럼 사이의 공간 유사도 분포를 분석하였다. 그 결과 초분광 영상에 존재하는 총 15개의 선박을 탐지하였으며 최대 유사도에 기반한 선박 갑판의 색상도 분류하였다. 탐지 선박들은 고해상도 digital mapping camera (DMC) 영상과의 매칭을 통해 검증하였다. 본 연구는 해상 선박탐지를 위한 항공 초분광 센서 활용의 기초로서 향후 원격탐사 기반의 선박 모니터링 시스템에 주요 역할을 할 것으로 기대된다.

배관 용접부 표면결함 검출 및 비교 (Detection and Comparison of Surface Defects in Pipe Welds)

  • 정윤수;고가진;안태형;김재열
    • 한국기계가공학회지
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    • 제19권1호
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    • pp.43-48
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    • 2020
  • At present, 24 nuclear power plants are in operation nationwide as the main power source responsible for about 27% of Korea's electricity, and five nuclear power plants are currently under construction. Issues of nuclear safety and reliability have always existed, but after the Fukushima accident, ensuring reliability has become an even more important issue for safety. Compared to other kinds of accidents, the initial response after a nuclear accident is more important than any other accident. Prior to accidents, it is important to be able to predict and judge the accident in advance for the sake of prevention. In this research, non-destructive inspection methods for existing pipe welds include radiographic, ultrasonic, magnetic particle practice, and liquid penetration testing. For this experiment, carbon steel pipes like that of the material used in nuclear pipes were adopted, and specimen welded to the flange (Flange) were manufactured. After testing, the weld specimen were not damaged through the infrared thermography (IRT) experiment. This study attempted to improve the safety of carbon steel pipes through a comparative analysis of finite element analysis.

영상에서 객체 추출을 통한 적응형 통행 우선순위 교통신호 제어 시뮬레이션 (Simulation of Traffic Signal Control with Adaptive Priority Order through Object Extraction in Images)

  • 윤재홍;지유강
    • 한국멀티미디어학회논문지
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    • 제11권8호
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    • pp.1051-1058
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    • 2008
  • 영상처리와 통신 기술의 진보는 통합된 시스템의 일부분으로써 긴급 차량 우선권과 통행 우선 방법 모두를 수용하기 위한 현행 교통 신호 제어기들과 차량 탐지 기술을 가능하게 만들고 있다. 횡단보도에서 현행 교통 신호제어는 고정된 신호 주기에 따라 변하도록 구성되어 있다. 고정된 신호 주기의 신호제어 체계는 통행량이 없는 상황에서도 일정한 신호주기가 주어지기 때문에 상대적으로 교통량이 많은 곳에서는 해당 진행 신호가 부여될 때까지 대기해야 한다. 이러한 대기 시간은 신호 위반에 따른 교통사고의 위험과 교통체증을 유발하게 된다. 본 논문에서는 교통사고의 위험과 교통 체증을 해소하기 위한 방안으로 객체 검출영상을 통하여 현장상황에 맞게 우선적으로 신호가 부여될 수 있도록 적응형 우선순위 교통신호 제어 시스템을 설계하고 시뮬레이션 하였다.

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