• Title/Summary/Keyword: accident recognition algorithm

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Recognition of Dangerous Driving Using Automobile Black Boxes (차량용 블랙박스를 활용한 위험 운전 인지)

  • Han, In-Hwan;Yang, Gyeong-Su
    • Journal of Korean Society of Transportation
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    • v.25 no.5
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    • pp.149-160
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    • 2007
  • Automobile black boxes store and provide accident and driving information. The accident and driving information can be utilized to build scientific traffic-event database and can be applied in various industries. The objective of this study is to develop a recognition system of dangerous driving through analyzing the driving characteristic patterns. In this paper, possible dangerous driving models are classified into four models on the basis of vehicle behaviors(acceleration, deceleration, rotation) and accident types from existing statistical data. Dangerous driving data have been acquired through vehicle tests using automobile black boxes. Characteristics of driving patterns have been analyzed in order to classify dangerous driving models. For the recognition of dangerous driving, this study selected critical value of each dangerous driving model and developed the recognition algorithm of dangerous driving. The study has been verified by the application of recognition algorithm of dangerous driving and vehicle tests using automobile black boxes. The presented recognition methods of dangerous driving can be used for on-line/off-line management of drivers and vehicles.

Unmanned accident prevention Arduino Robot using color detection algorithm (색 검지 알고리즘을 이용한 무인 사고방지 아두이노 로봇 개발)

  • Lee, Ho-Jeong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.05a
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    • pp.493-497
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    • 2015
  • This study was started with concern about problem of increasing physical and personal injury caused by traffic accidents, despite of technological advances in transportation. As the vehicles, which is currently produced, informs the driver only detecting the proximity of an object by the front and rear sensor, this study implemented the color detection algorithm, the circular shape recognition algorithm, and the distance recognition algorithm and built the accident prevention beyond accident perception, which commends to avoid the object or to stop the robot, if object was detected by algorithms. For the simulation, we made the Arduino vehicle robot equipped with compact wireless communication camera and confirmed that the robot successfully avoids an object or stops itself in simulated driving.

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A Study on the Vehicle Black Box with Accident Prevention (사고예방이 가능한 차량용 블랙박스 시스템에 관한 연구)

  • Kim, Kang Hyo;Moon, Hae Min;Shin, Ju Hyun;Pan, Sung Bum
    • Smart Media Journal
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    • v.4 no.1
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    • pp.39-43
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    • 2015
  • A vehicle black box helps to investigate the cause of accident by recording time, and videos as wells as shock information of the time of accident Lately, intelligent black box with accident prevention as well as existing functions is being studied. This paper proposes an applicable algorithm for vehicle black boxes that prevent any accident likely to occur while a car is parked, like robbery, theft or hit-and-run. Proposed algorithm provides object recognition, face detection and alarm as the object approaches car. Tests on the algorithm prove that it can recognize an approaching object, identify and set alarm if needed, depending on each risk level.

A Study on a Wearable Smart Airbag Using Machine Learning Algorithm (머신러닝 알고리즘을 사용한 웨어러블 스마트 에어백에 관한 연구)

  • Kim, Hyun Sik;Baek, Won Cheol;Baek, Woon Kyung
    • Journal of the Korean Society of Safety
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    • v.35 no.2
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    • pp.94-99
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    • 2020
  • Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

An Efficient Broadcast Scheme for Disseminating Emergency Message in Urban Vehicular Ad-Hoc Networks (도심의 차량 네트워크에서 긴급 메시지 전파를 위한 효율적인 브로드캐스트 기법)

  • Lee, Kyu-Chang;Han, Ki-Jun;Cho, Keu-Chul;Baek, Young-Mi;Kim, Jun-Hyung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38C no.7
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    • pp.605-611
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    • 2013
  • If an accident vehicle propagates emergency messages to other vehicles close to it, the other drivers may realize and avoid the accident spot. In this letter, we propose a broadcast scheme to propagate emergency messages fast in urban VANETs (Vehicular Ad-Hoc Networks) with the help of GPS (Global Position System). In our scheme, a transmitting vehicle chooses the farthest node as the next relay vehicle to propagate emergency messages. And, we suggest an algorithm for intersection recognition and SCF (Store-Carry-Forward) task by taking advantage of periodic hello packets to reduce the propagation time and enhance the delivery ratio.

A study on Recognition of Inpatient Room Acoustic Pattern for Hospital safety (병원안전을 위한 입원실 음향패턴 인식 관한 연구)

  • Ryu, Han-Sul;Ahn, Jong-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.3
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    • pp.169-173
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    • 2021
  • Currently, safety accidents in hospitals are steadily occurring. In particular, safety accidents of elderly patients with weak immunity, such as nursing hospitals, continue to occur, and countermeasures are needed. Most accidents are caused by patient movement. As a method of reducing safety accidents by analyzing and recognizing the sound of the inpatient room according to the movement of the patient, this paper classifies the sound pattern for sound recognition in the hospital inpatient room using DTW (Dynamic Time Warping), an algorithm applicable to time-series pattern recognition. It was analyzed by applying it to the inpatient room environment.

Study on Vision based Object Detection Algorithm for Passenger' s Safety in Railway Station (철도 승강장 승객안전을 위한 비전기반 물체 검지 알고리즘 연구)

  • Oh, Seh-Chan;Park, Sung-Hyuk;Jeong, Woo-Tae
    • Proceedings of the KSR Conference
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    • 2008.06a
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    • pp.553-558
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    • 2008
  • Advancement in information technology have enabled applying vision sensor to railway, such as CCTV. CCTV has been widely used in railway application, however the CCTV is a passive system that provide limited capability to maintain safety from boarding platform. The station employee should monitor continuously CCTV monitors. Therefore immediate recognition and response to the situation is difficultin emergency situation. Recently, urban transit operators are pursuing applying an unattended station operation system for their cost reduction. Therefore, an intelligent monitoring system is need for passenger's safety in railway. The paper proposes a vision based monitoring system and object detection algorithm for passenger's safety in railway platform. The proposed system automatically detects accident in platform and analyzes level of danger using image processing technology. The system uses stereo vision technology with multi-sensors for minimizing detection error in various railway platform conditions.

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A Study on improving the performance of License Plate Recognition (자동차 번호판 인식 성능 향상에 관한 연구)

  • Eom, Gi-Yeol
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.11a
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    • pp.203-207
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    • 2006
  • Nowadays, Cars are continuing to grow at an alarming rate but they also cause many problems such as traffic accident, pollutions and so on. One of the most effective methods that prevent traffic accidents is the use of traffic monitoring systems, which are already widely used in many countries. The monitoring system is beginning to be used in domestic recently. An intelligent monitoring system generates photo images of cars as well as identifies cars by recognizing their plates. That is, the system automatically recognizes characters of vehicle plates. An automatic vehicle plate recognition consists of two main module: a vehicle plate locating module and a vehicle plate number identification module. We study for a vehicle plate number identification module in this paper. We use image preprocessing, feature extraction, multi-layer neural networks for recognizing characters of vehicle plates and we present a feature-comparison method for improving the performance of vehicle plate number identification module. In the experiment on identifying vehicle plate number, 300 images taken from various scenes were used. Of which, 8 images have been failed to identify vehicle plate number and the overall rate of success for our vehicle plate recognition algorithm is 98%.

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Fault Diagnosis for Rotating Machine Using Feature Extraction and Minimum Detection Error Algorithm (특징 추출과 검출 오차 최소화 알고리듬을 이용한 회전기계의 결함 진단)

  • Chong, Ui-pil;Cho, Sang-jin;Lee, Jae-yeal
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.16 no.1 s.106
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    • pp.27-33
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    • 2006
  • Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

Analysis of articles on water quality accidents in the water distribution networks using big data topic modelling and sentiment analysis (빅데이터 토픽모델링과 감성분석을 활용한 물공급과정에서의 수질사고 기사 분석)

  • Hong, Sung-Jin;Yoo, Do-Guen
    • Journal of Korea Water Resources Association
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    • v.55 no.spc1
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    • pp.1235-1249
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    • 2022
  • This study applied the web crawling technique for extracting big data news on water quality accidents in the water supply system and presented the algorithm in a procedural way to obtain accurate water quality accident news. In addition, in the case of a large-scale water quality accident, development patterns such as accident recognition, accident spread, accident response, and accident resolution appear according to the occurrence of an accident. That is, the analysis of the development of water quality accidents through key keywords and sentiment analysis for each stage was carried out in detail based on case studies, and the meanings were analyzed and derived. The proposed methodology was applied to the larval accident period of Incheon Metropolitan City in 2020 and analyzed. As a result, in a situation where the disclosure of information that directly affects consumers, such as water quality accidents, is restricted, the tone of news articles and media reports about water quality accidents with long-term damage in the event of an accident and the degree of consumer pride clearly change over time. could check This suggests the need to prepare consumer-centered policies to increase consumer positivity, although rapid restoration of facilities is very important for the development of water quality accidents from the supplier's point of view.