for Journals by Title or ISSN
for Articles by Keywords
Followed Journals
Journal you Follow: 0
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Jurnals are published.
Already have an account? Sign In to see the journals you follow.
Journal Cover   Measurement and Control
  [SJR: 0.105]   [H-I: 15]   [28 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0020-2940
   Published by Sage Publications Homepage  [821 journals]
  • Commercial News
    • Pages: 190 - 191
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599506
      Issue No: Vol. 48, No. 7 (2015)
  • From the President's Pen
    • Pages: 192 - 192
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599488
      Issue No: Vol. 48, No. 7 (2015)
  • Letter to the Editor
    • Pages: 194 - 195
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599489
      Issue No: Vol. 48, No. 7 (2015)
  • InstMC Prizes & Awards - 2015
    • Pages: 197 - 197
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599490
      Issue No: Vol. 48, No. 7 (2015)
  • Surrey and Sussex Section SS Robin Visit May 2015
    • Pages: 198 - 198
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599493
      Issue No: Vol. 48, No. 7 (2015)
  • Manchester and Chester
    • Pages: 200 - 200
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599498
      Issue No: Vol. 48, No. 7 (2015)
  • Companion Company News
    • Pages: 200 - 200
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599508
      Issue No: Vol. 48, No. 7 (2015)
  • InstMC Membership Elections and Transfers
    • Pages: 201 - 202
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599504
      Issue No: Vol. 48, No. 7 (2015)
  • Interface News
    • Pages: 203 - 209
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015599507
      Issue No: Vol. 48, No. 7 (2015)
  • Promoting Professionalism: Am I Bovvered?
    • Authors: Dearden; H.
      Pages: 210 - 210
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015595996
      Issue No: Vol. 48, No. 7 (2015)
  • Rotating Machine Fault Diagnosis Based on Locality Preserving Projection
           and Back Propagation Neural Network-Support Vector Machine Model
    • Authors: Dong, S; Xu, X, Liu, J, Gao, Z.
      Pages: 211 - 216
      Abstract: In order to effectively recognize the rotating machine fault, a new method based on locality preserving projection and back propagation neural network–support vector machine model is proposed. First, the gathered vibration signals are decomposed by the empirical mode decomposition, and the corresponding intrinsic mode functions are obtained. Then, Shannon entropies of the intrinsic mode functions are used as the original features. But the extracted features have the problems of high dimension and redundancy. So, the manifold learning algorithm locality preserving projection is introduced to extract the characteristic features and reduce the dimension. The characteristic features are inputted to the back propagation neural network–support vector machine model to train and construct the fault diagnosis model, and the rotating machine fault condition identification is realized. The running states of a normal inner race and several inner races with different degrees of fault were recognized; the results validate the effectiveness of the proposed algorithm.
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015595995
      Issue No: Vol. 48, No. 7 (2015)
  • A Practically Validated Adaptive Calibration Technique using Optimized
           Artificial Neural Network for Level Measurement by Capacitance Level
    • Authors: KV, S; Roy, B. K.
      Pages: 217 - 224
      Abstract: Design of an adaptive calibration technique using an optimized artificial neural network for liquid-level measurement is discussed in this paper. The objective of the present work is to design and validate an adaptive calibration technique so as (1) to extend the linearity range of measurement to 100% of full-scale input range and (2) to make the measurement technique adaptive of variations in tank diameter, permittivity of liquid, liquid temperature, and to achieve objectives (1) and (2) using an optimized neural network. An optimized artificial neural network is a network having least possible number of hidden layers to achieve minimum mean square error between outputs and targets by comparing various algorithms, schemes, and transfer functions of neuron. The output of capacitance level sensor is capacitance. A data conversion unit is used to convert it to voltage. A suitable optimized artificial neural network is designed and used in place of conventional calibration circuit. The proposed technique is tested with simulated data and validated with practical data. Results show that proposed technique has fulfilled the set objectives.
      PubDate: 2015-09-08T02:50:45-07:00
      DOI: 10.1177/0020294015595998
      Issue No: Vol. 48, No. 7 (2015)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2015