{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T01:23:28Z","timestamp":1766712208155,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T00:00:00Z","timestamp":1492560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National High Technology Research and Development Program of China","award":["2014AA042001"],"award-info":[{"award-number":["2014AA042001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.<\/jats:p>","DOI":"10.3390\/s17040894","type":"journal-article","created":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T10:22:01Z","timestamp":1492597321000},"page":"894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine"],"prefix":"10.3390","volume":"17","author":[{"given":"Ji","family":"Li","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China"},{"name":"Department of Mechatronics Engineering, School of Mechanical &amp; Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Zou","sequence":"additional","affiliation":[{"name":"Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, School of Mechanical &amp; Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jahangir","family":"Alam SM","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, School of Mechanical &amp; Automotive Engineering, South China University of Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/JPROC.2009.2013612","article-title":"Review: Semiconductor Piezoresistance for Microsystems","volume":"97","author":"Barlian","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82121","DOI":"10.1039\/C5RA13425K","article-title":"Piezoresistive effect of p-type silicon nanowires fabricated by a top-down process using FIB implantation and wet etching","volume":"5","author":"Kozeki","year":"2015","journal-title":"RSC. Adv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1063\/1.3595485","article-title":"Self-heating in piezoresistive cantilevers","volume":"98","author":"Doll","year":"2011","journal-title":"Appl. Phys. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Maboudian, R., Carraro, C., Senesky, D.G., and Roper, C.S. (2013). Advances in silicon carbide science and technology at the micro- and nanoscales. J. Vac. Sci. Technol. A, 31.","DOI":"10.1116\/1.4807902"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1109\/JMEMS.2015.2470132","article-title":"The Piezoresistive Effect of SiC for MEMS Sensors at High Temperatures: A Review","volume":"24","author":"Nakamura","year":"2015","journal-title":"J. Microelectromech. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"035024","DOI":"10.1088\/0268-1242\/31\/3\/035024","article-title":"Strain- and temperature-induced effects in AlGaN\/GaN high electron mobility transistors","volume":"31","author":"Yalamarthy","year":"2016","journal-title":"Semicond. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0924-4247(95)00845-4","article-title":"Effects of process variations in a CMOS circuit for temperature compensation of piezoresistive pressure sensors","volume":"48","author":"Gakkestad","year":"1995","journal-title":"Sens. Actuators A Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.measurement.2014.11.032","article-title":"A novel temperature compensated piezoresistive pressure sensor","volume":"63","author":"Aryafar","year":"2015","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yao, Z., Liang, T., Jia, P., Hong, Y., Qi, L., Lei, C., Zhang, B., Li, W., Zhang, D., and Xiong, J. (2016). Passive resistor temperature compensation for a high-temperature piezoresistive pressure sensor. Sensors, 16.","DOI":"10.3390\/s16071142"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s10470-010-9580-7","article-title":"Analog ASIC for improved temperature drift compensation of a high sensitive porous silicon pressure sensor","volume":"67","author":"Futane","year":"2011","journal-title":"Analog Integr. Circuits Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.sna.2013.10.029","article-title":"An analytical thermal-structural model of a gas-sealed capacitive pressure sensor with a mechanical temperature compensation structure","volume":"205","author":"Hao","year":"2014","journal-title":"Sens. Actuators A Phys."},{"key":"ref_12","first-page":"272","article-title":"Compensation and Signal Conditioning of Capacitive Pressure Sensors","volume":"41","author":"Mozek","year":"2011","journal-title":"Inform. Midem"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.sna.2014.07.015","article-title":"Research of radiosonde humidity sensor with temperature compensation function and experimental verification","volume":"218","author":"Luo","year":"2014","journal-title":"Sens. Actuators A Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/JSEN.2012.2185690","article-title":"A Study of Compensation for Temporal and Spatial Physical Temperature Variation in Total Power Radiometers","volume":"12","author":"Chae","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_15","first-page":"684","article-title":"Temperature compensation of pressure sensor based on the interpolation of splines","volume":"32","author":"Fan","year":"2006","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1080\/10739149.2013.816965","article-title":"Back propagation neural network model for temperature and humidity compensation of a non dispersive infrared methane sensor","volume":"41","author":"Wang","year":"2013","journal-title":"Instrum. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18711","DOI":"10.3390\/s141018711","article-title":"Laser Gyro Temperature Compensation Using Modified RBFNN","volume":"14","author":"Ding","year":"2014","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11189","DOI":"10.3390\/s150511189","article-title":"Modification of an RBF ANN-Based Temperature Compensation Model of Interferometric Fiber Optical Gyroscopes","volume":"15","author":"Cheng","year":"2015","journal-title":"Sensors"},{"key":"ref_19","first-page":"1443","article-title":"Compensation of capacitive differential pressure sensor using multi layer perceptron neural network","volume":"8","author":"Moallem","year":"2015","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_20","first-page":"2272","article-title":"Temperature compensation of light addressable potentiometric sensor based on support vector machine","volume":"26","author":"Qiu","year":"2015","journal-title":"J. Optoelectron. Laser"},{"key":"ref_21","first-page":"1175","article-title":"Thermal error modeling of a coordinate boring machine based on fuzzy clustering and SVM","volume":"48","author":"Yang","year":"2014","journal-title":"J. Shanghai Jiaotong Univ."},{"key":"ref_22","first-page":"803","article-title":"Temperature compensation of FBG sensor based on support vector machine","volume":"21","author":"Shao","year":"2010","journal-title":"J. Optoelectron. Laser"},{"key":"ref_23","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, John Wiley&Sons Inc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: A survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.renene.2014.08.075","article-title":"Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search","volume":"74","author":"Wong","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, J., Hu, G., Zhou, Y., Zou, C., Peng, W., and Jahangir Alam, S.M. (2016). A temperature compensation method for piezo-resistive pressure sensor utilizing chaotic ions motion algorithm optimized hybrid kernel LSSVM. Sensors, 16.","DOI":"10.3390\/s16101707"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13320","DOI":"10.1364\/OE.23.013320","article-title":"Temperature compensation method using readout signals of ring laser gyroscope","volume":"23","author":"Li","year":"2015","journal-title":"Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TSMCB.2009.2020435","article-title":"Coupled Simulated Annealing","volume":"40","author":"Suykens","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization By Simulated Annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.ejor.2006.06.034","article-title":"A hybrid simplex search and particle swarm optimization for unconstrained optimization","volume":"181","author":"Fan","year":"2007","journal-title":"Eur. J. Oper. Res."},{"key":"ref_32","unstructured":"(2017, April 17). Pressure\/Differential-Pressure Transmitter for Use in Industrial-Progress Measure and Control Systems\u2014Part1:Genneral Specification. Available online: http:\/\/dbpub.cnki.net\/grid2008\/dbpub\/detail.aspx?QueryID=31&CurRec=6&dbcode=SCHF&dbname=SCSF&filename=SCSF00038855&urlid=&yx=&uid=WEEvREcwSlJHSldRa1FhdkJkdjFtWWtTRkFDSFVtVnR6NTdKV1M5eE5IVT0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4ggI8Fm4gTkoUKaID8j8gFw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.compbiomed.2015.12.011","article-title":"Application of robust generalised cross-validation to the inverse problem of electrocardiology","volume":"69","author":"Barnes","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"Acm Trans. Intell. Syst. Technol."},{"key":"ref_35","unstructured":"(2017, April 17). LS-SVMlab1.8. Available online: http:\/\/www.esat.kuleuven.be\/sista\/lssvmlab\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:59Z","timestamp":1760207579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,19]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["s17040894"],"URL":"https:\/\/doi.org\/10.3390\/s17040894","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,4,19]]}}}