• 전기학회논문지P
• /
• 제59권4호
• /
• pp.384-389
• /
• 2010

Workers maintaining at the railroad trackside may collide with the train since they cannot recognize the train approaching because of the sensory block phenomenon occurred due to their long hours of continued monotonous maintenance work. To reduce these casualty accidents of maintenance workers working at the trackside of railroad, we developed the wireless communication-based safety equipment for preventing accidents. The motor-cars for maintaining trackside facility have unique operational patterns suitable for urban environment. The several mechanism for developed safety equipment are represented in this paper.

• 한국자동차공학회논문집
• /
• 제12권2호
• /
• pp.205-214
• /
• 2004

This paper presents design concepts, specifications and performances of a newly developed Black Box, the reconstruction analysis tool with the records, and results of validation tests. The Black Box can detect crash accidents automatically, and record the vehicle's motion and driver's maneuvers during a pre-defined time period before and after the accident. The items of the Black Box included the acceleration, yaw-rate, vehicle speed, engine RPM, braking application, steering and several digital inputs for recording driver's maneuvers. To detect the accident-related-crash, it is important to understand characteristics of the crash signal, which are much different from those of normal driving. Therefore, analytical considerations should be taken in designing pre-filtering circuits and selecting appropriate parameters for identifying crash accidents. And, it is necessary to select proper combination of motion sensors and design proper pre-filtering circuits in order to describe the vehicle's motion. The analysis algorithms were developed and implemented which can perform accurate detection of crash accidents, simulating pre-crash trajectories, and calculating parameters for reconstruction analysis of crash accidents. The developed Black Box was installed on passenger cars and several types of validation tests were conducted. Through the tests, the accuracy of the recorded data and usefulness of the analysis tool for reconstruction have been validated.

• 항공우주시스템공학회지
• /
• 제1권4호
• /
• pp.7-12
• /
• 2007

From the beginning of aviation history, in-flight smoke/fire events have been a serious issue. As aircraft are getting larger and are becoming more auto-piloted and aircraft systems are getting more complex, it is an increasing risk of in-flight smoke/fire accidents accompanied with fire events. Therefore, we review the statistics of fire/smoke accidents in order to enhance an understanding for risk of in-flight smoke events, and present the certification requirements for smoke per KAS Part 25. In addition, we provide acceptable methods of complying with related requirements, such as smoke detection test, smoke penetration test and smoke evacuation test.

• 한국항행학회논문지
• /
• 제27권4호
• /
• pp.391-395
• /
• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.361-369
• /
• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• 전기학회논문지
• /
• 제68권2호
• /
• pp.359-363
• /
• 2019

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제15권1호
• /
• pp.27-34
• /
• 2015

For a vision-based driver assistance system, unusual motion detection is one of the important means of preventing accidents. In this paper, we propose a real-time unusual-motion-detection model, which contains two stages: salient region detection and unusual motion detection. In the salient-region-detection stage, we present an improved temporal attention model. In the unusual-motion-detection stage, three kinds of factors, the speed, the motion direction, and the distance, are extracted for detecting unusual motion. A series of experimental results demonstrates the proposed method and shows the feasibility of the proposed model.

• 반도체디스플레이기술학회지
• /
• 제23권1호
• /
• pp.110-116
• /
• 2024

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

• 한국산학기술학회논문지
• /
• 제18권5호
• /
• pp.302-307
• /
• 2017

북극해 지역은 해수면의 변화와 다양한 환경적 요인들로 인해 유빙들이 형성되고 이는 자원 개발을 위한 해양시스템 및 운항선박과의 충돌사고에 의한 피해를 유발하고 있다. 극지방의 유빙은 운항중인 석박뿐만 아니라 한 장소에서 오랜 기간 작업을 수행하는 해양자원 시추 및 생산 시스템에 대한 잠재적 사고요인이 된다. 유빙과의 충돌사고 방지를 위해 북극해의 해양자원 시추 및 생산 시스템과 북극 항로를 운항하는 선박에서는 위성 영상 정보 및 탐지 레이더를 이용하여 유빙을 탐지하고 있다. 하지만 가시광선 위성영상은 야간 활용이 불가능하고, 레이더에 의한 탐지도 소형 유빙에 대해서는 탐지확률이 현격히 저조해지는 문제가 있다. 본 연구에서는 유빙의 탐지를 위해 주야간 운용이 모두 가능한 열화상 시스템의 이용 방안에 주목하고 유빙의 탐지특성에 관한 실험적 연구를 수행하였다. 열화상 시스템의 야간 운용성을 파악할 수 있도록 실험조건을 설정하고 계측 각도 변화에 따른 열화상을 계측하였으며, 실험과 동일 조건에 대한 유빙과 해수의 복사에너지를 이론적으로 계한함으로써 계측 결과와의 상호 관계를 파악하였다.

• 한국전자통신학회논문지
• /
• 제13권5호
• /
• pp.1051-1058
• /
• 2018

지하철 스크린도어(PSD)에서 발생할 수 있는 안전사고 예방을 위한 영상처리 알고리듬을 제안한다. 우선 지하철 스크린도어 영상에 대해 에지를 검출 하고, 사람의 스크린도어 접근 여부를 판단하기 위해 호프변환을 이용하여 직선을 검출한다. 이를 위해 스크린도어 경계면에 일직선을 긋고 이 직선의 끊김 여부로 사람의 접근을 판단한다. 일반적으로 에지는 영상의 가장 기본적인 특징을 나타내며, 에지 검출은 영상처리 및 컴퓨터 비전 분야에서 매우 중요하다. 에지 검출 방법에는 로버츠, 소벨, 프리윗, 라플라시안 등 고정된 값의 마스크를 사용하는 방법과 영상을 형태학적 관점에서 접근하여 처리하는 모폴로지 방법 및 캐니에지 검출 방법 등이 있다. 본 논문에서는 캐니에지 검출방법과 호프변환을 이용하여 지하철 스크린도어에서 사람의 접근 여부에 대한 감지 알고리듬을 제안하고 실제 그 결과를 컴퓨터 시뮬레이션으로 나타내었다.