{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:40:06Z","timestamp":1777704006100,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2017,8,3]],"date-time":"2017-08-03T00:00:00Z","timestamp":1501718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,8,24]]},"abstract":"<jats:p>Inspired by unsupervised feature learning and deep learning, this paper provides a novel classification method for advanced suspension system based on Deep Neural Networks (DNNs). Sparse autoencoder and softmax regression are chosen to form deep structure and the parameters are trained by deep learning. Aiming at showing the superiority of DNNs based road classification method, a simulation of a B-class vehicle with skyhook control is performed in CarSim, and three measurable system responses, i.e., centre of gravity (C.G.) of sprung mass acceleration, rattle space and unsprung mass acceleration are chosen and three independent classifiers are established. Simulation results show that the classifier using unsprung mass acceleration has the highest accuracy and better performance than existing methods. Because of the adaptive learning ability and the deep structure, the proposed method can save work and provide higher classification accuracy.<\/jats:p>","DOI":"10.3233\/jifs-161860","type":"journal-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T11:37:29Z","timestamp":1501846649000},"page":"1907-1918","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":54,"title":["Road excitation classification for semi-active suspension system with deep neural networks"],"prefix":"10.1177","volume":"33","author":[{"given":"Yechen","family":"Qin","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Peolple\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Langari","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Engineering, Texas A&amp;M University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Peolple\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changle","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Peolple\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingming","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Peolple\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,8,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/00423114.2010.532223"},{"key":"e_1_3_2_3_2","unstructured":"PatersonW.D. Road deterioration and maintenance effects: Models for planning and management. Maryland Usa: Johns Hopkins University Press 1987."},{"issue":"6","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1109\/TCST.2015.2413937","article-title":"Adaptive road profile estimation in semiactive car suspensions","volume":"23","author":"Martinez J.C.T.","year":"2015","unstructured":"MartinezJ.C.T., FerganiS., SenameO., et al., Adaptive road profile estimation in semiactive car suspensions, IEEE Transactions On Control Systems Technology 23(6) (2015), 2293\u20132305.","journal-title":"IEEE Transactions On Control Systems Technology"},{"key":"e_1_3_2_5_2","first-page":"1533","volume-title":"IEEE Control Decision Conference(CDC)","author":"Qin Y.","year":"2015","unstructured":"QinY., DongM., ZhaoF., et al., Road profile classification for vehicle semi-active suspension system based on adaptive neuro-fuzzy inference system, IEEE Control Decision Conference(CDC), Osaka, Japan, 2015, pp. 1533\u20131538."},{"key":"e_1_3_2_6_2","volume-title":"ASME Dynamic System Control Conference(DSCC)","author":"Qin Y.","year":"2014","unstructured":"QinY., LangariR. and LiangG., The use of vehicle dynamic response to estimate road profile input in time domain, ASME Dynamic System Control Conference(DSCC), San Antonio, TX, USA, 2014, pp. V002T27A."},{"key":"e_1_3_2_7_2","volume-title":"American Control Conference (ACC)","author":"Doumiati M.","year":"2011","unstructured":"DoumiatiM., VictorinoA., ChararaA., et al., Estimation of road profile for vehicle dynamics motion: Experimental validation, American Control Conference (ACC), San Francisco, CA, USA, 2011."},{"key":"e_1_3_2_8_2","volume-title":"IEEE Region 5 Technical Conference","author":"Mccann R.","year":"2007","unstructured":"MccannR. and NguyenS., System identification for a model-based observer of a road roughness profiler, IEEE Region 5 Technical Conference, Fayetteville, Ar, USA, 2007."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/24\/11\/115029"},{"issue":"6","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1080\/00423110701485050","article-title":"The use of vehicle acceleration measurements to estimate road roughness","volume":"46","author":"Gonzalez A.","year":"2008","unstructured":"GonzalezA., O\u2019brienE.J. and CashellK., The use of vehicle acceleration measurements to estimate road roughness, Vehicle System Dynamics 46(6) (2008), 483\u2013499.","journal-title":"Vehicle System Dynamics"},{"key":"e_1_3_2_11_2","volume-title":"American Control Conference (ACC)","author":"Qin Y.","year":"2017","unstructured":"QinY., LangariR., WangZ., et al., Random road profile estimation using adaptive kalman filter and adaptive super-twisting observer, American Control Conference (ACC), Seattle, USA, 2017."},{"issue":"10","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1109\/TVT.2014.2373434","article-title":"Simultaneous estimation of road profile and tire road friction for automotive vehicle","volume":"64","author":"Rath J.","year":"2015","unstructured":"RathJ., VeluvoluK. and DefoortM., Simultaneous estimation of road profile and tire road friction for automotive vehicle, IEEE Transactions on Vehicular Technology 64(10) (2015), 4461\u20134471.","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jterra.2014.03.002","article-title":"Reconstruction of road defects and road roughness classification using artificial neural networks simulation and vehicle dynamic responses: Application to experimental data","volume":"53","author":"Ngwangwa H.M.","year":"2014","unstructured":"NgwangwaH.M., HeynsP.S., BreytenbachH.G.A., et al., Reconstruction of road defects and road roughness classification using artificial neural networks simulation and vehicle dynamic responses: Application to experimental data, Journal Of Terramechanics 53(6) (2014), 1\u201318.","journal-title":"Journal Of Terramechanics"},{"issue":"5","key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00423114.2016.1145243","article-title":"Estimation of road profile variability from measured vehicle responses","volume":"54","author":"Fauriat W.","year":"2016","unstructured":"FauriatW., MattrandC., GaytonN., et al., Estimation of road profile variability from measured vehicle responses, Vehicle System Dynamics 54(5) (2016), 1\u201321.","journal-title":"Vehicle System Dynamics"},{"issue":"4","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"04014015","DOI":"10.1061\/(ASCE)CP.1943-5487.0000285","article-title":"Comparison of machine learning methods for evaluating pavement roughness based on vehicle response","volume":"28","author":"Nitsche P.","year":"2012","unstructured":"NitscheP., StR.T., KammerM., et al., Comparison of machine learning methods for evaluating pavement roughness based on vehicle response, Journal Of Computing In Civil Engineering 28(4) (2012), 04014015.","journal-title":"Journal Of Computing In Civil Engineering"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.1434265"},{"key":"e_1_3_2_17_2","article-title":"Road excitation classification for semi-active suspension system based on system response","author":"Qin Y.","year":"2017","unstructured":"QinY., ZhaoF., WangZ., et al., Road excitation classification for semi-active suspension system based on system response, Journal of Viration and Control (2017). doi: 10.1177\/1077546317693432","journal-title":"Journal of Viration and Control"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"e_1_3_2_22_2","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky A.","year":"2012","unstructured":"KrizhevskyA., SutskeverI. and HintonG.E., Imagenet classification with deep convolutional neural networks; Proceedings Of The Advances In Neural Information Processing Systems, F, 2012.","journal-title":"Proceedings Of The Advances In Neural Information Processing Systems"},{"key":"e_1_3_2_23_2","unstructured":"ISO. Mechanical vibration-road surface profiles-reporting of measured data. International Organization For Standardization. ISO-8608 1995."},{"issue":"5","key":"e_1_3_2_24_2","first-page":"70","article-title":"Simulation research on time domain model of road roughness with time-space correlation","volume":"32","author":"Wang Y.","year":"2013","unstructured":"WangY., ChenS. and KaifengZ., Simulation research on time domain model of road roughness with time-space correlation, Journal Of Vibration And Shock 32(5) (2013), 70\u201374.","journal-title":"Journal Of Vibration And Shock"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1115\/1.3438373"},{"issue":"3","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"031006","DOI":"10.1115\/1.4035700","article-title":"Comprehensive analysis for influence of controllable damper time delay on semi-active suspension control strategies","volume":"139","author":"Qin Y.","year":"2016","unstructured":"QinY., DongM., ZhaoF., et al., Comprehensive analysis for influence of controllable damper time delay on semi-active suspension control strategies, Journal of vibration and acoustics Transactions of ASME 139(3) (2016), 031006.","journal-title":"Journal of vibration and acoustics Transactions of ASME"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1080\/00423114.2016.1267374"},{"key":"e_1_3_2_28_2","article-title":"Sparse deep belief net model for visual area v2;","author":"Lee H.","year":"2008","unstructured":"LeeH., EkanadhamC. and NgA.Y., Sparse deep belief net model for visual area v2;, Proceedings Of The Advances In Neural Information Processing Systems, 2008.","journal-title":"Proceedings Of The Advances In Neural Information Processing Systems"},{"key":"e_1_3_2_29_2","unstructured":"PalmR.B. Prediction as a candidate for learning deep hierarchical models of data. Technical University Of Denmark 2012."},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"BishopC.M. Neural networks for pattern recognition Oxford University Press 1995.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/11941439_114"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cta.2015.1317"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/636739"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.3390\/app7060570"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161860","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-161860","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:11Z","timestamp":1777455611000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-161860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,3]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017,8,24]]}},"alternative-id":["10.3233\/JIFS-161860"],"URL":"https:\/\/doi.org\/10.3233\/jifs-161860","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,3]]}}}