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A new method of inductive sensors impedance measurement applied to the identification of vehicle parameters

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Inductive loop sensors are widely used for detection of presence, measurement of parameters as well as classification of vehicles. Vehicle classification may be performed based on their magnetic profiles. The magnetic profile is a signal which is proportional to the resultant of an impedance change of the sensor, caused by the measured object (the changes are minor - of the order of 1%). Generator and bridge circuits are most commonly used as conditioning circuits for such sensors. As a result we can obtain one output signal proportional to total changes of sensor parameters (R and L). In this paper, a modified bridge circuit that allows independent measurement of the components (R and L) of the sensor's impedance, has been proposed. With that provided, it is possible to receive broader information on the object, which allows higher classification resolution. This paper provides the concept of a circuit, model testing results, processing algorithms used and the test results of a real circuit.
Rocznik
Strony
69--76
Opis fizyczny
Bibliogr. 11 poz., rys., wykr.
Twórcy
autor
autor
  • AGH - University of Science and Technology, Department of Measurement and Instrumentation, Al. Mickiewicza 30, 30-059 Cracow, Poland, antic@agh.edu.pl
Bibliografia
  • [1] May, A.D. (1990). Traffic Flow Fundamentals. Prentice Hall. Englewood Cliffs. New Jersey 07632.
  • [2] Klein, L.A. (2001). Sensor Technologies and Data Requirements for ITS. Artech House.
  • [3] Boel, R., Mihaylova, L. (2004). Modeling Freeway Networks by Hybrid Stochastic Models. Proceedings of IEEE Intelligent Vehicles Symposium. Parma, 182-187.
  • [4] Hepner, H., Stroppe, H. (1972). Magnetic and inductive testing of metals. Slask Publishing. (in Polish).
  • [5] Sroka, R. (2002). Multisensing in road traffic measurements. Multisensor Fusion. Kluwer Academic Publisher. NATO Science Series - Mathematics. Physics and Chemistry, 70, 715-747.
  • [6] Gajda, J., Stencel, M. (1997). Determination of Road Vehicle Types Using an Inductive Loop Detector. Proceedings of XIV IMEKO Congress. Tampere. Finland, 231-236.
  • [7] Ki, Y.K., Baik, D.K. (2006). Vehicle Classification Algorithm for Loop Detectors using Neural Network. IEEE Transactions on Vehicular Technology, 55(6), 1704-1711.
  • [8] Gajda, J., Sroka, R., Żegleń, T. (2007). Multisensor Data Fusion in the Process of Weighing and Classification of the Moving Vehicles. Advances and Challenges in Multisensor Data and Information Processing. IOS Press. Amsterdam, 324 - 330.
  • [9] Gajda, J., Sroka, R. (2000). Vehicle Classification by Parametric Identification of the Measured Signals. Proceedings of XVI IMEKO World Congress. Vienna, 9, 199-204.
  • [10] Burnos, P., Gajda, J., Piwowar, P., Sroka, R., Stencel, M., Żegleń, T. (2007). Measurements of Road Traffic Parameters Using Inductive Loops and Piezoelectric Sensors. Metrology and Measurement Systems, 14(2), 187-203.
  • [11] Sidor, T. (2006). Electrical and Electronic Measurements and Instrumentation. AGH - Academic Press. Cracow.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0075-0018
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