A Piezoresistive Pressure Sensor Modeling by Artificial Neural Networks

محورهای موضوعی : فصلنامه نانوساختارهای اپتوالکترونیکی

Behzad Hadi ¹ , Farzin Shama ² , Hamid Sherafat Vaziri ³ , Mohsen Eghbalkhah ⁴

1 - Mechanical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran
2 - Electrical Engineering Department, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran E-mail: [e-mail]
3 - Electrical Engineering Department, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran E-mail: [e-mail]
4 - Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran

تاریخ دریافت : 1402/10/07 تاریخ پذیرش : 1403/02/11 تاریخ انتشار : 1403/08/25

کلید واژه: ANN, Modelling, Piezoresistive sensor,

چکیده مقاله :

An efficient artificial neural network (ANN) model for a pressure sensor is presented in this paper. This pressure sensor has a piezoresistive structure and its experimental data is used in this work to train and test the proposed ANN model. The selected network is a multi-layer perceptions ANN type, which has one hidden layer with five neurons inside. The proposed MLP modelled system has been trained to recognize the output of the pressure sensor, which is a generated voltage in millivolts, according to the temperature and pressure levels. The obtained mean square relative error (MSRE) error of this method is only 0.137 for test data set.

چکیده انگلیسی:

An efficient artificial neural network (ANN) model for a pressure sensor is presented in this paper. This pressure sensor has a piezoresistive structure and its experimental data is used in this work to train and test the proposed ANN model. The selected network is a multi-layer perceptions ANN type, which has one hidden layer with five neurons inside. The proposed MLP modelled system has been trained to recognize the output of the pressure sensor, which is a generated voltage in millivolts, according to the temperature and pressure levels. The obtained mean square relative error (MSRE) error of this method is only 0.137 for test data set.

منابع و مأخذ:

[1] B. Stephen, MEMS mechanical sensors, Artech House, 2004.
[2] G. Cao, et al. A micromachined piezoresistive pressure sensor with a shield layer. Sensors. 16 (8) (2016) 1286. Available: https://doi.org/10.3390/s16081286
[3] J. G. Taylor, Neural Networks and Their Applications, John Wiley & Sons, West Sussex, UK, 1996.
[4] A. R. Gallant and H. White. On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Networks. 5(1) (1992) 129-138.‏ Available: https://doi.org/10.1016/S0893-6080(05)80011-5
[5] G. H Roshani, et al. Prediction of materials density according to number of scattered gamma photons using optimum artificial neural network. Journal of Computational Methods in Physics. 2014 (2014).‏ Available : https://doi.org/10.1155/2014/305345
[6] M. Hayati, et al. Linearization design method in class-F power amplifier using artificial neural network. Journal of Computational Electronics, 13(2014) 943-949.‏ Available: https://doi.org/10.1007/s10825-014-0612-x
[7] G. Sadeghi, et al. Energy and exergy evaluation of the evacuated tube solar collector using Cu2O/water nanofluid utilizing ANN methods. Sustainable Energy Technologies and Assessments, 37 (2020), 100578.‏ Available: https://doi.org/10.1016/j.seta.2019.100578
[8] G. H. Roshani, et al. Designing a simple radiometric system to predict void fraction percentage independent of flow pattern using radial basis function. Metrology and Measurement Systems, 25(2) (2018) 347-358.‏ Available: https://doi.org/10.24425/119560
[9] G. Sadeghi, et al. Empirical data-driven multi-layer perceptron and radial basis function techniques in predicting the performance of nanofluid-based modified tubular solar collectors. Journal of Cleaner Production, 295 (2021) 126409.‏ Available: https://doi.org/10.1016/j.jclepro.2021.126409
[10] G. H. Roshani, et al. Volume fraction determination of the annular three-phase flow of gas-oil-water using adaptive neuro-fuzzy inference system. Computational and Applied Mathematics, 37 (2018) 4321-4341.‏ Available: https://doi.org/10.1007/s40314-018-0578-6
[11] G. H. Roshani, et al. Utilizing features extracted from registered 60Co gamma-ray spectrum in one detector as inputs of artificial neural network for independent flow regime void fraction prediction. MAPAN, 34(2) (2019), 189-196.‏ Available: https://doi.org/10.1007/s12647-018-0298-9
[12] S. Hosseini, et al. Increasing efficiency of two-phase flowmeters using frequency-domain feature extraction and neural network in the detector output spectrum. Journal of Modeling in Engineering, 19(67) (2021), 47-57.‏ Available: https://doi.org/10.22075/JME.2021.19817.1860
[13] A. Abdykian and Z. Safi. Finding electrostatics modes in metal thin films by using of quantum hydrodynamic model. Journal of Optoelectronical Nanostructures, 1(3) (2016), 43-50. Available: https://jopn.marvdasht.iau.ir/article_2193.html
[14] A. Abdikian, et al. Electrostatics Modes in Mono-Layered Graphene. Journal of Optoelectronical Nanostructures, 1(2) (2016), 1-8.‏ Available: https://jopn.marvdasht.iau.ir/article_2044.html
[15] P. Ripka and A. Tipek. Modern sensors handbook. John Wiley & Sons; (2007).
[16] O. N. Tufte, et al. Silicon diffused-element piezoresistive diaphragms. Journal of Applied Physics, 33(11) (1962), 3322-3327.‏ Available: https://doi.org/10.1063/1.1931164
[17] N. Bhalla, et al. Finite element analysis of MEMS square piezoresistive accelerometer designs with low crosstalk. Semiconductor Conference (CAS), 2011 International (2011 Oct 17), Vol. 2, pp. 353-356
[18] A.Farmani, et al. Numerical modeling of a metamaterial biosensor for cancer tissues detection. Journal of Optoelectronical Nanostructures, 5(1) (2020), 1-18.‏ Available: https://jopn.marvdasht.iau.ir/article_4030_526.html
[19] H. Bahramiyan and S. Bagheri. Linear and nonlinear optical properties of a modified Gaussian quantum dot: pressure, temperature and impurity effect. journal of optoelectronical nanostructures, 3(3) (2018), 79-100.‏ Available: https://jopn.marvdasht.iau.ir/article_3047_0a2d460925ad6686daf5ac62c9082227.pdf
[20] A. Moftakharzadeh, et al. Noise Equivalent Power Optimization of Graphene-Superconductor Optical Sensors in the Current Bias Mode. Journal of Optoelectronical Nanostructures, 3(3) (2018), 1-12.‏ Available: https://jopn.marvdasht.iau.ir/article_3040_c11f2d8d2fa24f1f5bb3d07f5a0660f1.pdf
[21] G. H. Roshani, et al. Combined application of neutron activation analysis using IECF device and neural network for prediction of cement elements. Radiation detection technology and methods, 1 (2017), 1-7. Available: https://doi.org/10.1007/s41605-017-0025-z
[22] G. H. Roshani, et al. Some applications of artificial neural network in nuclear engineering. (2013) LAP LAMBERT Academic Publishing.
[23] M. A. Sattari, et al. A miniaturized filter design approach using GMDH neural networks. Microwave and Optical Technology Letters, 65(9) (2023), 2507-2516. Available: https://doi.org/10.1002/mop.33741
[24] A. Sadighzadeh, et al. Prediction of Neutron Yield of IR‐IECF Facility in High Voltages Using Artificial Neural Network. Journal of Engineering, 2014(1) (2014), 798160. Available: https://doi.org/10.1155/2014/798160
[25] F. Fouladinia, et al. A novel metering system consists of capacitance-based sensor, gamma-ray sensor and ANN for measuring volume fractions of three-phase homogeneous flows. Nondestructive Testing and Evaluation, (2024) 1-27. Available: https://doi.org/10.1080/10589759.2024.2375575
[26] S. Amiri and G. H. Roshani. Investigating the Impact of Gamification on the Consumer Buying Behavior using Artificial Neural Network. Journal of Business Management, 14(4) (2022), 647-674. Available: https://doi.org/10.22059/JIBM.2022.334038.4249
[27] S. Amiri and G. H. Roshani. Modeling the impact of Covid-19’s stress and resilience on job burnout using Radial Basis Functions-Artificial Neural Network The case of knowledge-based companies. Research in Production and Operations Management, 13(2) (2022), 23-43. Available: https://doi.org/10.22108/JPOM.2022.131194.1407
[28] A. R. Shahani, et al. Prediction of influence parameters on the hot rolling process using finite element method and neural network. Journal of materials processing technology, 209(4) (2009), 1920-1935. Available: https://doi.org/10.1016/j.jmatprotec.2008.04.055
[29] N. Ghaemi, et al. Application of ANN and ANFIS to predict the effect of fatty acids on the performance of CA composite membranes in removal of pesticides from water. Desalination and Water Treatment, 78 (2017) 132-140. Available: https://doi.org/10.5004/dwt.2017.20730
[30] E. K. Vahidi, et al. Application of ANN and RBF to Optimize the Properties of the RCC Pavement Containing RHA. American Journal of Civil and Environmental Engineering, 2(5) (2017), 57-66.