{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:18:54Z","timestamp":1768256334403,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.<\/jats:p>","DOI":"10.3390\/s21155188","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:44:32Z","timestamp":1627854272000},"page":"5188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6742-4305","authenticated-orcid":false,"given":"Mitsugu","family":"Hasegawa","sequence":"first","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daiki","family":"Kurihara","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1519-3311","authenticated-orcid":false,"given":"Yasuhiro","family":"Egami","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota 470-0392, Aichi, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5420-280X","authenticated-orcid":false,"given":"Hirotaka","family":"Sakaue","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandar","family":"Jemcov","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sellers, M.E., Nelson, M.A., Roozeboom, N.H., and Burnside, N.J. (2017, January 9\u201313). Evaluation of unsteady pressure sensitive paint measurement technique for space launch vehicle buffet determination. Proceedings of the AIAA SciTech Forum\u201455th AIAA Aerospace Sciences Meeting, Grapevine, TX, USA.","DOI":"10.2514\/6.2017-1402"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1007\/s00348-012-1380-7","article-title":"Characterization of pressure dynamics in an axisymmetric separating\/reattaching flow using fast-responding pressure-sensitive paint","volume":"53","author":"Bitter","year":"2012","journal-title":"Exp. Fluids"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s00348-018-2665-2","article-title":"Hypersonic boundary-layer separation detection with pressure-sensitive paint for a cone at high angle of attack","volume":"60","author":"Running","year":"2019","journal-title":"Exp. Fluids"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4404","DOI":"10.3390\/s130404404","article-title":"Pressure-Sensitive Paint Measurements of Transient Shock Phenomena","volume":"13","author":"Quinn","year":"2013","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lakowicz, J.R. (2006). Principles of Fluorescence Spectroscopy, Springer. [3rd ed.].","DOI":"10.1007\/978-0-387-46312-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1146\/annurev.fluid.33.1.155","article-title":"Surface pressure measurements using luminescent coatings","volume":"33","author":"Bell","year":"2001","journal-title":"Annu. Rev. Fluid Mech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1063\/1.1141632","article-title":"Luminescent barometry in wind tunnels","volume":"61","author":"Kavandi","year":"1990","journal-title":"Rev. Sci. Instrum."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Morris, M., Benne, M., Crites, R., and Donovan, J. (1993). Aerodynamic Measurements Based on Photoluminescence, American Institute of Aeronautics and Astronautics (AIAA).","DOI":"10.2514\/6.1993-175"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/0894-1777(94)00123-P","article-title":"Pressure-sensitive paint in aerodynamic testing","volume":"10","author":"McLachlan","year":"1995","journal-title":"Exp. Therm. Fluid Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1115\/1.3101703","article-title":"Temperature- and pressure-sensitive luminescent paints in aerodynamics","volume":"50","author":"Liu","year":"1997","journal-title":"Appl. Mech. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, T., Sullivan, J.P., Asai, K., Klein, C., and Egami, Y. (2021). Pressure and Temperature Sensitive Paints, Springer International Publishing. [2nd ed.].","DOI":"10.1007\/978-3-030-68056-5"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6836","DOI":"10.3390\/s100706836","article-title":"Optimization of anodized-aluminum pressure-sensitive paint by controlling luminophore concentration","volume":"10","author":"Sakaue","year":"2010","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jlumin.2004.08.049","article-title":"Pressure-sensitive paint (PSP): Concentration quenching of platinum and magnesium porphyrin dyes in polymeric films","volume":"113","author":"Grenoble","year":"2005","journal-title":"J. Lumin."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hayashi, T., and Sakaue, H. (2017). Dynamic and steady characteristics of polymer-ceramic pressure-sensitive paint with variation in layer thickness. Sensors, 17.","DOI":"10.3390\/s17051125"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1243\/09544100JAERO243","article-title":"A review of pressure-sensitive paint for high-speed and unsteady aerodynamics","volume":"222","author":"Gregory","year":"2008","journal-title":"Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9799","DOI":"10.3390\/s101109799","article-title":"A dipping duration study for optimization of anodized-aluminum pressure-sensitive paint","volume":"10","author":"Sakaue","year":"2010","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11649","DOI":"10.3390\/s111211649","article-title":"Pressure-sensitive paint: Effect of substrate","volume":"11","author":"Quinn","year":"2011","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1016\/S0008-8846(98)00165-3","article-title":"Modeling of strength of high-performance concrete using artificial neural networks","volume":"28","author":"Yeh","year":"1998","journal-title":"Cem. Concr. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41524-019-0221-0","article-title":"Recent advances and applications of machine learning in solid-state materials science","volume":"5","author":"Schmidt","year":"2019","journal-title":"NPJ Comput. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1049\/iet-cta.2019.0651","article-title":"Robust delay-dependent LPV synthesis for blood pressure control with real-time Bayesian parameter estimation","volume":"14","author":"Tasoujian","year":"2020","journal-title":"IET Control. Theory Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yamamoto, K., Togami, T., Yamaguchi, N., and Ninomiya, S. (2017). Machine learning-based calibration of low-cost air temperature sensors using environmental data. Sensors, 17.","DOI":"10.3390\/s17061290"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Dou, Y., Yang, W., Guo, J., Chang, X., Ding, M., and Tang, X. (2021). A new machine learning algorithm for numerical prediction of near-Earth environment sensors along the inland of East Antarctica. Sensors, 21.","DOI":"10.3390\/s21030755"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isatra.2020.01.011","article-title":"Supervisory predictive control for wheel slip prevention and tracking of desired speed profile in electric trains","volume":"101","author":"Moaveni","year":"2020","journal-title":"ISA Trans."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karimi, M., Jahanshahi, A., Mazloumi, A., and Sabzi, H.Z. (2019). Border gateway protocol anomaly detection using neural network. Proc. IEEE Int. Conf. Big Data, 6092\u20136094.","DOI":"10.1109\/BigData47090.2019.9006201"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/S0890-6955(99)00090-5","article-title":"Application of neural network and FEM for metal forming processes","volume":"40","author":"Kim","year":"2000","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.actamat.2018.08.002","article-title":"A machine learning approach for engineering bulk metallic glass alloys","volume":"159","author":"Ward","year":"2018","journal-title":"Acta Mater."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.matdes.2018.11.060","article-title":"Using deep neural network with small dataset to predict material defects","volume":"162","author":"Feng","year":"2019","journal-title":"Mater. Des."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1016\/j.aej.2020.11.043","article-title":"Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline\u2019s scale layer thickness","volume":"60","author":"Roshani","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Voghoei, S., Tonekaboni, N.H., Yazdansepas, D., Soleymani, S., Farahani, A., and Arabnia, H.R. (2020, January 2\u20134). Personalized feedback emails. Proceedings of the ACM SE \u201820: 2020 ACM Southeast Conference, Tampa, FL, USA.","DOI":"10.1145\/3374135.3385274"},{"key":"ref_31","first-page":"545","article-title":"Optimum locations for intercity bus terminals with the AHP approach\u2014Case study of the city of Esfahan","volume":"9","author":"Arabi","year":"2015","journal-title":"Int. J. Environ. Ecol. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2019.03.001","article-title":"A high-bias, low-variance in-troduction to machine learning for physicists","volume":"810","author":"Mehta","year":"2019","journal-title":"Phys. Rep."},{"key":"ref_33","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MIS.2009.36","article-title":"The unreasonable effectiveness of data","volume":"24","author":"Halevy","year":"2009","journal-title":"IEEE Intell. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, Y., Kee Wong, D.W., Wong, T.Y., and Liu, J. (2015, January 25\u201329). Glaucoma detection based on deep convolutional neural network. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318462"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1198\/10618600152418584","article-title":"The art of data augmentation","volume":"10","author":"Meng","year":"2001","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3390\/a1020130","article-title":"Machine learning: A crucial tool for sensor design","volume":"1","author":"Zhao","year":"2008","journal-title":"Algorithms"},{"key":"ref_38","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2015, January 5\u20138). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","article-title":"Mean absolute percentage error for regression models","volume":"192","author":"Golden","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Amit, D.J. (1989). Modeling Brain Function, Cambridge University Press.","DOI":"10.1017\/CBO9780511623257"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010;, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of the COMPSTAT 2010\u201419th International Conference on Computational Statistics, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.rser.2017.02.023","article-title":"A review and analysis of regression and machine learning models on commercial building electricity load forecasting","volume":"73","author":"Yildiz","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","article-title":"Logistic regression and artificial neural network classification models: A methodology review","volume":"35","author":"Dreiseitl","year":"2002","journal-title":"J. Biomed. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1002\/gepi.20642","article-title":"Brief review of regression-based and machine learning methods in genetic epidemiology: The Genetic Analysis Workshop 17 experience","volume":"35","author":"Dasgupta","year":"2011","journal-title":"Genet. Epidemiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J. Clin. Epidemiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:38:00Z","timestamp":1760164680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,30]]},"references-count":49,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21155188"],"URL":"https:\/\/doi.org\/10.3390\/s21155188","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,30]]}}}