Determination of flow regimes from raw capacitance tomography data using neural networks

被引：0
|
作者：
Yan, H [1 ]
Liu, CT [1 ]
Liu, YH [1 ]
机构：
[1] Shenyang Univ Technol, Sch Informat Sci & Engn, Shenyang 110023, Peoples R China
来源：
ICEMI'2003: PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOLS 1-3 | 2003年
关键词：
electrical capacitance tomography; flow-regime identification; back-propagation network; feature parameter extraction;
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：
An application of electrical capacitance tomography for two-component flow identification without the need for time-consuming image reconstruction and analysis is presented 10 feature parameters are extracted straight from the capacitance measurements and translated into regime information via a back-propagation (BP) network. The extraction of feature parameters, the architecture and the training of the BP network are give. Simulation results show that the new identification method has good precision and fast speed The use of feature parameters and the BP network for flow-regime identification is promising.
引用
收藏
页码：2313 / 2317
页数：5
相关论文
共 50 条
  • [1] Determination of multi-component flow process parameters based on electrical capacitance tomography data using artificial neural networks
    Mohamad-Saleh, J
    Hoyle, BS
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2002, 13 (12) : 1815 - 1821
  • [2] Identification of flow regimes using back-propagation networks trained on simulated data based on a capacitance tomography sensor
    Yan, H
    Liu, YH
    Liu, CT
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2004, 15 (02) : 432 - 436
  • [3] Educational presentation of raw data in electrical capacitance tomography
    Sankowski, D
    Chaniecki, Z
    EXPERIENCE OF DESIGNING AND APPLICATION OF CAD SYSTEMS IN MICROELECTRONICS, 2003, : 114 - 116
  • [4] Funnel Flow Dynamics Prediction Based on Electrical Capacitance Tomography and Artificial Neural Networks
    Garbaa, Hela
    Jackowska-Strumillo, Lidia
    Grudzien, Krzysztof
    Romanowski, Andrzej
    INTERNATIONAL INTERDISCIPLINARY PHD WORKSHOP 2016, 2016, : 58 - 62
  • [5] Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks
    Zhang, Licheng
    Wu, Xihong
    Luo, Dingsheng
    2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2015, : 865 - 870
  • [6] Object classification on raw radar data using convolutional neural networks
    Han, Heejae
    Kim, Jeonghwan
    Park, Junyoung
    Lee, Yujin
    Jo, Hyunwoo
    Park, Yonghyeon
    Matson, Eric T.
    Park, Seongha
    2019 IEEE SENSORS APPLICATIONS SYMPOSIUM (SAS), 2019,
  • [7] Reconstruction of capacitance tomography images of simulated two-phase flow regimes
    Gomez, S
    Ono, M
    Gamio, C
    Fraguela, A
    APPLIED NUMERICAL MATHEMATICS, 2003, 46 (02) : 197 - 208
  • [8] An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data
    Zainal-Mokhtar, Khursiah
    Mohamad-Saleh, Junita
    SENSORS, 2013, 13 (09): : 11385 - 11406
  • [9] Tomography using neural networks
    Demeter, G
    REVIEW OF SCIENTIFIC INSTRUMENTS, 1997, 68 (03): : 1438 - 1443
  • [10] Time delay neural networks for the classification of flow regimes
    Yamashita, Y
    COMPUTERS & CHEMICAL ENGINEERING, 1997, 21 : S367 - S371
12345