Olowokere, D. O., & Ajofoyinbo, A. (2015, June), A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education  Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23385

\bibitem{asee_peer_23385}
  Olowokere, D. O., \& Ajofoyinbo, A. (2015, June), \emph{A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education}
  Paper presented at 2015 ASEE Annual Conference \& Exposition, Seattle, Washington.
  10.18260/p.23385

David O. Olowokere and Abayomi Ajofoyinbo Ph.D..  "A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education".  2015 ASEE Annual Conference & Exposition, Seattle, Washington, 2015, June.  ASEE Conferences, 2015.  https://strategy.asee.org/23385 Internet.  03 Jul, 2024

\bibitem{asee_peer_23385}
  David O. Olowokere and Abayomi Ajofoyinbo Ph.D..
  "A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education".
  \emph{2015 ASEE Annual Conference \& Exposition, Seattle, Washington, 2015, June}.
  ASEE Conferences, 2015.
  https://strategy.asee.org/23385 Internet.  03 Jul, 2024

@INPROCEEDINGS{asee_peer_23385
author = "David O. Olowokere and Abayomi Ajofoyinbo Ph.D."
title = "A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education"
booktitle = "2015 ASEE Annual Conference \& Exposition"
year = "2015"
month = "June"
address = "Seattle, Washington"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/23385}
number = {10.18260/p.23385}
}

TY  - CPAPER
AB  -      ANFIS based intelligent system model for self-compensation of sensor                                measurementsPhysical quantities measured by sensors such as temperature, humidity, pressure, displacement,etc., are continuous in nature. Output signal of a sensor can become “corrupted” with unwantedsignals that prevent the actual useful signal from being measured correctly. The characteristics ofa sensor may be classified as being static or dynamic. Whereas static characteristics describe theproperties of a sensor after all transient effects have stabilized to their steady state, dynamiccharacteristics describe the transient properties (or time-dependent characteristics) of a sensor.In the literature, researchers have investigated different aspects of sensor with differentimprovement objectives but mostly focusing on static characteristics. However, the need toreduce both systematic and random errors to improve quality of sensor measurements hasbecome a critical requirement for design of modern day measurement systems. This paperpresents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent system model forself-compensation of sensor measurements; in which unwanted signals are removed, and therebyimproving the quality of measurement data. The intelligent self-compensation system modelconsists of four modules namely: sensing, measurement error-correction, ANFIS, and Digital-to-Analog (A/D) conversion modules. The ANFIS builds a Fuzzy Inference System (FIS) whosemembership function parameters are tuned to learn from the data being modeled. It is noted thatour consideration in the current paper is not on errors related to sampling and A/D conversion,which can be controlled by selecting appropriate multiplexers, sample-and-hold circuits and A/Dconverter. A sensor that incorporates two energy storage components is considered in this paper;for example, an accelerometer. Such a sensor is modeled by a second-order differentialequation, which is translated into a state-space model for analysis and determination of“corrupted” output of the sensing module. For this case, the input to the sensing module is afunction of the actual sensed quantity, effects of temperature, humidity and other environmentalfactors. Moreover, in view of the effects of environmental conditions on sensor’s performance,this “corrupted” output serves as input to an error-correction module whose output consequentlybecomes input to the ANFIS module. It is noted that the self-compensation occurs in the error-correction module. Output of the ANFIS module is subsequently sent to the A/D conversionmodule for further processing. The efficacy of this intelligent system model is tested using theMATLAB Fuzzy Logic Toolbox. It is shown by the results obtained that the ANFIS basedintelligent model presented in this paper can indeed be implemented for self-compensation ofsensor measurements, and thereby improving quality of measurement data.                                                                                                 1References[1] Wang L., Yan Y.; Mathematical modeling and experimental validation of electrostatic sensors for    rotational speed measurement. Measurement Science and Technology, 2014, vol. 25, 12pp.[2] Miljic N.L., Tomic M.V.; A Neuro-Fuzzy based combustion sensor for the control of optimal engine    combustion efficiency. Thermal Science, 2013, vol. 17, no. 1, pp. 135-151.[3] Rivera-Mejia, J., Carrillo-Romero, M., Herrera-Ruiz, G.; Self-compensation to build reconfigurable    measurement systems. Instrumentation & Measurement Magazine, IEEE, April 2013, vol.16, no.2,    pp.10-19.[4] Maria-Alexandra P., Jean-Michel S., M. Kayal.; Offset and Drift Analysis of the Hall Effect sensors:    The geometrical parameters influence. Digest Journal of Nanomaterials and Biostructures, July -    September 2012, vol. 7, no. 3, pp. 883–891.[5] Mahajan A, Oesch C., Padmanaban H., Utterback L., Chitikeshi S., Figueroa F.; Physical and virtual    intelligent sensors for integrated health management systems. International Journal on Smart Sensing    and Intelligent Systems, 2012, vol. 5, no. 3, pp. 559-575.[6] Pop, S. , Pitica, D. , Ciascai, I. ; Sensor measurement errors detection methods; IEEE 34th    International Spring Seminar on Electronics Technology (ISSE), 11-15 May 2011; Tratanska    Lomnica, pp. 414–418.[7] Petra N., Zweck J., Minkoff S.E., Kosterev A. A., Doty J.H.; Modeling and design optimization of a    resonant optothermoacoustic trace gas sensor. Society for Industrial and Applied Mathematics    Journal of Applied Mathematics, 2011, vol. 71, no. 1, pp. 309-332.[8] Nanayakara T., Halgamuge M. N., Sridhar P. and Madni A. M.; Intelligent sensing in dynamic    environments using Markov decision process. Sensors, 2011, vol. 11, pp. 1229-1242.[9] Sanchez J.E.R., Vellasco M.M.B.R., Tanscheit R.; An intelligent model for self-compensation and    self-validation of sensor measurements. Fuzzy logic and applications: Proceedings of the 9th    International Workshop 9th International Workshop, WILF 2011, Trani, Italy, August 29-31, 2011,    pp. 269-276.[10] Liu H., Zhang Y. , Liu Y.-W., Jin M.-H.; Measurement errors in the scanning of resistive sensor    arrays, (Elsevier) Sensors and Actuators A: Physical, September 2010, vol. 163, issue no. 1,    pp. 198–204.[11] Fraden J.; Sensor Characteristics. Handbook of modern sensors, 2010, pp. 13-52.[12] Bolton W.; Instrumentation and Control. Elsevier Publishing, First Edition, 2008.[13] Chan E., Wang D., Pasquier M.; Towards intelligent self-care: multi-sensor monitoring and neuro-     fuzzy behavior modeling. Proceedings of IEEE International Conference on Systems, Man and     Cybernetics, SMC, Singapore, 2008, pp. 3083-3088.[14] Ausserlechner U., Infineon T.A.G., Austria M.M. , Holliber M.; Drift of magnetic sensitivity of     smart Hall sensors due to moisture absorbed by the IC-package [automotive applications];     Proceedings of 3rd IEEE International Conference on Sensors, 2004; Vienna, Austria, vol.1,     pp. 455–458.[15] Jang J-S.R.; ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on     Systems, Man, and Cybernetics, vol. 23, no. 3, 1993, pp. 665-684.                                                                                                      2
AU  - David O. Olowokere
AU  - Abayomi Ajofoyinbo Ph.D.
CY  - Seattle, Washington
DA  - 2015/06/14
PB  - ASEE Conferences
TI  - A Framework for Integrating Intelligent Sensor Measurement Data into Engineering Education
UR  - https://strategy.asee.org/23385
DO  - 10.18260/p.23385
ER  -