Search

Results 31-40 of 63 (Search time: 0.004 seconds).

Actuator fault detection for unmanned ground vehicles considering friction coefficients

  • 2021-11
  • Na, Gyujin. (2021-11). Actuator fault detection for unmanned ground vehicles considering friction coefficients. Sensors, 21(22). doi: 10.3390/s21227674
  • MDPI
  • View : 591
  • Download : 114
  • Kim, J.
  • Park, Jae Geun
  • Eun, Y.
  • 2022-05
  • Kim, J. (2022-05). Precise Stop Control and Experimental Validation for Metro Train Overcoming Delays and Nonlinearities. IEEE Transactions on Vehicular Technology, 71(5), 4776–4787. doi: 10.1109/TVT.2022.3158370
  • Institute of Electrical and Electronics Engineers
  • View : 582
  • Download : 0
  • 2022-11
  • An, Youngwoo. (2022-11). Online Fault Detection for Four Wheeled Skid Steered UGV Using Neural Network. Actuators, 11(11). doi: 10.3390/act11110307
  • MDPI
  • View : 442
  • Download : 0
  • 2024-03
  • Kim, Seunghyeon. (2024-03). Throughput Approximation by Neural Network for Serial Production Lines With High Up/Downtime Variability. IEEE Transactions on Industrial Informatics, 20(3), 4227–4235. doi: 10.1109/TII.2023.3321026
  • Institute of Electrical and Electronics Engineers
  • View : 592
  • Download : 0

Deep Reinforcement Learning-Driven Scheduling in Multijob Serial Lines: A Case Study in Automotive Parts Assembly

  • 2024-02
  • Lee, Sanghoon. (2024-02). Deep Reinforcement Learning-Driven Scheduling in Multijob Serial Lines: A Case Study in Automotive Parts Assembly. IEEE Transactions on Industrial Informatics, 20(2), 2932–2943. doi: 10.1109/TII.2023.3292538
  • IEEE Computer Society
  • View : 529
  • Download : 55
  • 2022-11
  • Gyujin Na. (2022-11). A Probing Signal-based Replay Attack Detection Method Avoiding Control Performance Degradation. International Journal of Control, Automation, and Systems, 20(11), 3637–3649. doi: 10.1007/s12555-021-0852-z
  • 제어·로봇·시스템학회
  • View : 428
  • Download : 0
  • 2018-07
  • Bouk, Safdar Hussain. (2018-07). LAPEL: Hop Limit Based Adaptive PIT Entry Lifetime for Vehicular Named Data Networks. IEEE Transactions on Vehicular Technology, 67(7), 5546–5557. doi: 10.1109/TVT.2018.2797693
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 1035
  • Download : 0

EDOVE: Energy and depth variance-based opportunistic void avoidance scheme for underwater acoustic sensor networks

  • 2017-10
  • Bouk, Safdar Hussain. (2017-10). EDOVE: Energy and depth variance-based opportunistic void avoidance scheme for underwater acoustic sensor networks. Sensors, 17(10). doi: 10.3390/s17102212
  • MDPI AG
  • View : 869
  • Download : 182
  • Kim, Kwangsoo
  • Kwon, Minseok
  • Park, Jaegeun
  • Eun, Yongsoon
  • 2016
  • Kim, Kwangsoo. (2016). Dynamic Vehicular Route Guidance Using Traffic Prediction Information. Mobile Information Systems, 2016, 1–11. doi: 10.1155/2016/3727865
  • Hindawi Publishing Corporation
  • View : 863
  • Download : 0
  • 2015-08
  • Chang, Dong Eui. (2015-08). On the method of energy shaping via static output feedback for stabilization of mechanical systems. Journal of the Franklin Institute, 352(8), 3394–3404. doi: 10.1016/j.jfranklin.2014.08.014
  • Elsevier Ltd
  • View : 695
  • Download : 0