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Data Mining for Speed Bump Detection from Car Wheels and GPS Sensors using Random Forest
  • Joon, Jo Hae ;
  • Renata, Dionysius Aldion ;
  • Yoon, Su Hyun ;
  • Youngjune, Choi ;
  • Jembre, Yalew Zelalem
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Publication Year
2019-10-01
Journal
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, pp.740-743
Keyword
data miningrandom forestvehicleswireless sensor network
Mesh Keyword
Controller area networkbusLarge amountsRandom forest classificationRandom forest classifierRandom forestsSignal dataVehicle sensorsWheel speed
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsComputer Science ApplicationsInformation Systems and ManagementManagement of Technology and InnovationSafety, Risk, Reliability and QualityMedia TechnologyControl and Optimization
Abstract
Wireless Sensor Network is several sensor nodes that form a network capable of communicating with each other wirelessly. Data mining is useful for finding a pattern from a large amount of data, for driving condition monitoring can be obtained through Controller Area Network (CAN)-bus data. Vehicle sensors for this research provide signal data, including wheel speed and GPS data. Random Forest is used for this project because of it easy to use and understand. Random Forest Classification is one of the popular methods of data mining. Sensors, applications, and users who want access the vehicle information are integrated to communicate with each other using Mobius IoT platform. The accuracy of Random Forest Classifier obtained through this research is 80.9%.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36445
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078223367&origin=inward
DOI
https://doi.org/10.1109/ictc46691.2019.8939736
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8932631
Type
Conference
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Choi, Youngjune최영준
Department of Software and Computer Engineering
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