Detect the mechanical structure of the vehicle with neural network

被引：0
|
作者：
Chien, Hui-Hung [1 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Electricl Engn, Kaohsiung, Taiwan
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TAIWAN) | 2020年
关键词：
Vehicle historical data; Kuo's SSL Neural Network; Kernel K-means-LSTM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the rise of consumers 'awareness of driving safety and the popularity of automated vehicles, people pay highly attention to vehicle data. Vehicle data is mainly divided into the following two types: "vehicle internal information" (engine, electrical equips, transmission components), "the communication between the vehicle and the external environment" (vehicle driving safety, driving experience). These type of data can be collected through the vehicle's on-board diagnostics (OBD) system. The data set will be used for analysis. The vehicle's driving profile in different states provides a series of vehicle safety services.
引用
收藏
页数：2
相关论文
共 50 条
  • [31] Neural Network Vehicle Modelling for Underwater Vehicles
    Rozenbroek, Tijs
    Rojer, Jim
    Steenstra, Lukas
    Hildmann, Hanno
    OCEANS 2023 - LIMERICK, 2023,
  • [32] A Lean Convolutional Neural Network for Vehicle Classification
    Sanchez-Castro, Jonathan J.
    Rodriguez-Quinonez, Julio C.
    Ramirez-Hernandez, Luis R.
    Galaviz, Guillermo
    Hernandez-Balbuena, Daniel
    Trujillo-Hernandez, Gabriel
    Flores-Fuentes, Wendy
    Mercorelli, Paolo
    Hernandez-Perclomo, Wilmar
    Sergiyenko, Oleg
    Fernando Gonzalez-Navarro, Felix
    2020 IEEE 29TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2020, : 1365 - 1369
  • [33] Automatic vehicle classification using fast neural network and classical neural network for traffic monitoring
    Hannan, Mahammad Abdul
    Gee, Chew Teik
    Javadi, Mohammad Saleh
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2015, 23 : 2031 - 2042
  • [34] A Local Neural Network Approach to Detect Actions of Human Body
    Raghunandan, B.
    Kumaraswamy, R.
    2014 NATIONAL CONFERENCE ON COMMUNICATION, SIGNAL PROCESSING AND NETWORKING (NCCSN), 2014,
  • [35] A neural network approach to detect winding faults in electrical machine
    Imoru, OdunAyo
    Nelwamondo, Fulufhelo, V
    Jimoh, Adisa
    Ayodele, Temitope Raphael
    INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS, 2021, 22 (01) : 31 - 41
  • [36] Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
    Atencia-Jimenez, Ignacio
    Balajee, Adayabalam S.
    Ruiz-Gomez, Miguel J.
    Sendra-Portero, Francisco
    Montoro, Alegria
    Molina-Cabello, Miguel A.
    APPLIED SCIENCES-BASEL, 2024, 14 (22):
  • [37] Neural Network Rule Extraction to Detect Credit Card Fraud
    Ryman-Tubb, Nick F.
    Krause, Paul
    ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PT I, 2011, 363 : 101 - 110
  • [38] Neural Network Application in Strange Attractor Investigation to Detect a FGD
    Mehran, Y. Zandi
    Nasrabadi, A. M.
    2008 4TH INTERNATIONAL IEEE CONFERENCE INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, : 824 - +
  • [39] Neural network analysis of the volumetric capnogram to detect pulmonary embolism
    Patel, MM
    Rayburn, DB
    Browning, JA
    Kline, JA
    CHEST, 1999, 116 (05) : 1325 - 1332
  • [40] A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease
    Zafar, Afia
    Aamir, Muhammad
    Nawi, Nazri Mohd
    Ali, Sikandar
    Husnain, Mujtaba
    Samad, Ali
    MOBILE INFORMATION SYSTEMS, 2022, 2022
12345