{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:14:08Z","timestamp":1771650848221,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975220"],"award-info":[{"award-number":["51975220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National key Research and development program","doi-asserted-by":"crossref","award":["2019YFB1804200"],"award-info":[{"award-number":["2019YFB1804200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Province Science & Technology project","award":["2018B010109005"],"award-info":[{"award-number":["2018B010109005"]}]},{"name":"Guangdong Outstanding Youth Fund","award":["Guangdong Outstanding Youth Fund"],"award-info":[{"award-number":["Guangdong Outstanding Youth Fund"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2019ZD23"],"award-info":[{"award-number":["No. 2019ZD23"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00366-022-01711-9","type":"journal-article","created":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T14:33:45Z","timestamp":1658586825000},"page":"4151-4166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep emulational semi-supervised knowledge probability imaging method for plate structural health monitoring using guided waves"],"prefix":"10.1007","volume":"38","author":[{"given":"Bin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Dingmin","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2392-2556","authenticated-orcid":false,"given":"Xiaobin","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"issue":"12","key":"1711_CR1","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1109\/TUFFC.2019.2932227","volume":"66","author":"CA Chua","year":"2019","unstructured":"Chua CA, Cawley P, Nagy PB (2019) Scattering of the fundamental shear guided wave from a surface-breaking crack in plate-like structures. IEEE Trans Ultrason Ferroelectr Freq Control 66(12):1887\u20131897","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"issue":"5","key":"1711_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/25\/5\/053001","volume":"25","author":"M Mitra","year":"2016","unstructured":"Mitra M, Gopalakrishnan S (2016) Guided wave based structural health monitoring: a review. Smart Mater Struct 25(5):053001","journal-title":"Smart Mater Struct"},{"issue":"5\u20136","key":"1711_CR3","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1177\/1475921718817169","volume":"18","author":"J Moll","year":"2019","unstructured":"Moll J, Kathol J, Fritzen CP et al (2019) Open guided waves: online platform for ultrasonic guided wave measurements. Struct Health Monit 18(5\u20136):1903\u20131914","journal-title":"Struct Health Monit"},{"key":"1711_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2021.102539","volume":"124","author":"G Chen","year":"2021","unstructured":"Chen G, Guo Y, Katagiri T et al (2021) Multivariate probability of detection (POD) analysis considering the defect location for long-range, non-destructive pipe inspection using electromagnetic guided wave testing. NDT and E Int 124:102539","journal-title":"NDT and E Int"},{"issue":"2","key":"1711_CR5","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/app10020484","volume":"10","author":"H Mei","year":"2020","unstructured":"Mei H, James R, Haider MF, Giurgiutiu V (2020) Multimode guided wave detection for various composite damage types. Appl Sci 10(2):484","journal-title":"Appl Sci"},{"key":"1711_CR6","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.ymssp.2016.05.035","volume":"82","author":"M Hong","year":"2017","unstructured":"Hong M, Mao Z, Todd MD, Su Z (2017) Uncertainty quantification for acoustic nonlinearity parameter in Lamb wave-based prediction of barely visible impact damage in composites. Mech Syst Signal Process 82:448\u2013460","journal-title":"Mech Syst Signal Process"},{"issue":"1","key":"1711_CR7","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s42791-019-0012-2","volume":"1","author":"A Khan","year":"2019","unstructured":"Khan A, Kim N, Shin JK et al (2019) Damage assessment of smart composite structures via machine learning: a review. JMST Adv 1(1):107\u2013124","journal-title":"JMST Adv"},{"issue":"112403","key":"1711_CR8","first-page":"1","volume":"246","author":"A Mardanshahi","year":"2020","unstructured":"Mardanshahi A, Nasir V, Kazemirad S et al (2020) Detection and classification of matrix cracking in laminated composites using guided wave propagation and artificial neural networks. Compos Struct 246(112403):1\u201329","journal-title":"Compos Struct"},{"key":"1711_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.125628","volume":"317","author":"Z Wang","year":"2022","unstructured":"Wang Z, Huang S, Shen G et al (2022) High resolution tomography of pipeline using multi-helical Lamb wave based on compressed sensing. Constr Build Mater 317:125628","journal-title":"Constr Build Mater"},{"issue":"19","key":"1711_CR10","doi-asserted-by":"publisher","first-page":"27277","DOI":"10.1364\/OE.397509","volume":"28","author":"Z Peng","year":"2020","unstructured":"Peng Z, Jian J, Wen H et al (2020) Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions. Opt Express 28(19):27277\u201327292","journal-title":"Opt Express"},{"key":"1711_CR11","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.renene.2019.06.135","volume":"146","author":"AA Jim\u00e9nez","year":"2020","unstructured":"Jim\u00e9nez AA, Zhang L, Mu\u00f1oz CQG et al (2020) Maintenance management based on Machine Learning and nonlinear features in wind turbines. Renewable Energy 146:316\u2013328","journal-title":"Renewable Energy"},{"key":"1711_CR12","doi-asserted-by":"publisher","DOI":"10.12783\/shm2019\/32388","author":"JB Harley","year":"2019","unstructured":"Harley JB, Alguri KS, Tetali HV et al (2019) Learning guided wave dispersion curves from multi-path reflections with compressive sensing. Struct Health Monit. https:\/\/doi.org\/10.12783\/shm2019\/32388","journal-title":"Struct Health Monit"},{"issue":"5","key":"1711_CR13","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/s11340-020-00591-8","volume":"60","author":"ZH Liu","year":"2020","unstructured":"Liu ZH, Peng QL, Li X et al (2020) Acoustic emission source localization with generalized regression neural network based on time difference mapping method. Exp Mech 60(5):679\u2013694","journal-title":"Exp Mech"},{"issue":"50","key":"1711_CR14","first-page":"1","volume":"5","author":"A Ebrahimkhanlou","year":"2018","unstructured":"Ebrahimkhanlou A, Salamone S (2018) Single-sensor acoustic emission source localization in plate-like structures using deep learning. Aerospace 5(50):1\u201322","journal-title":"Aerospace"},{"issue":"3567","key":"1711_CR15","first-page":"1","volume":"19","author":"L Xu","year":"2019","unstructured":"Xu L, Yuan S, Chen J et al (2019) Guided wave-convolutional neural network based fatigue crack diagnosis of aircraft structures. Sensors 19(3567):1\u201318","journal-title":"Sensors"},{"key":"1711_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2020.106338","volume":"111","author":"KS Alguri","year":"2021","unstructured":"Alguri KS, Chia CC, Harley JB (2021) Sim-to-Real: Employing ultrasonic guided wave digital surrogates and transfer learning for damage visualization. Ultrasonics 111:106338","journal-title":"Ultrasonics"},{"issue":"14","key":"1711_CR17","doi-asserted-by":"publisher","first-page":"5784","DOI":"10.1109\/JSEN.2019.2908838","volume":"19","author":"C Su","year":"2019","unstructured":"Su C, Jiang M, Lv S et al (2019) Improved damage localization and quantification of CFRP using Lamb waves and convolution neural network. IEEE Sens J 19(14):5784\u20135791","journal-title":"IEEE Sens J"},{"key":"1711_CR18","first-page":"147592172110107","volume":"21","author":"B Zhang","year":"2021","unstructured":"Zhang B, Hong X, Liu Y (2021) Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves. Struct Health Monit 21:14759217211010708","journal-title":"Struct Health Monit"},{"key":"1711_CR19","first-page":"1","volume":"147592172092460","author":"J Mao","year":"2020","unstructured":"Mao J, Wang H, Spencer BF Jr (2020) Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders. Struct Health Monit 1475921720924601:1\u201318","journal-title":"Struct Health Monit"},{"key":"1711_CR20","first-page":"1","volume":"147592172095922","author":"X Lei","year":"2020","unstructured":"Lei X, Sun L, Xia Y (2020) Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks. Struct Health Monit 1475921720959226:1\u201319","journal-title":"Struct Health Monit"},{"issue":"08","key":"1711_CR21","doi-asserted-by":"publisher","first-page":"1950092","DOI":"10.1142\/S0219455419500925","volume":"19","author":"J Xiong","year":"2019","unstructured":"Xiong J, Chen J (2019) A generative adversarial network model for simulating various types of human-induced loads. Int J Struct Stab Dyn 19(08):1950092 (1-21)","journal-title":"Int J Struct Stab Dyn"},{"issue":"23","key":"1711_CR22","doi-asserted-by":"publisher","first-page":"14391","DOI":"10.1109\/JSEN.2020.3009194","volume":"20","author":"B Zhang","year":"2020","unstructured":"Zhang B, Hong X, Liu Y (2020) Multi-task deep transfer learning method for guided wave-based integrated health monitoring using piezoelectric transducers. IEEE Sens J 20(23):14391\u201314400","journal-title":"IEEE Sens J"},{"issue":"5","key":"1711_CR23","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/j.wavemoti.2013.04.004","volume":"50","author":"P Huthwaite","year":"2013","unstructured":"Huthwaite P, Simonetti F (2013) High-resolution guided wave tomography. Wave Motion 50(5):979\u2013993","journal-title":"Wave Motion"},{"issue":"4","key":"1711_CR24","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1088\/0964-1726\/15\/4\/007","volume":"15","author":"TR Hay","year":"2006","unstructured":"Hay TR, Royer RL, Gao H (2006) A comparison of embedded sensor Lamb wave ultrasonic tomography approaches for material loss detection. Smart Mater Struct 15(4):946\u2013951","journal-title":"Smart Mater Struct"},{"issue":"5","key":"1711_CR25","doi-asserted-by":"publisher","first-page":"N73","DOI":"10.1088\/0964-1726\/13\/5\/N01","volume":"13","author":"SM Prasad","year":"2004","unstructured":"Prasad SM, Balasubramaniam K, Krishnamurthy CV (2004) Structural health monitoring of composite structures using Lamb wave tomography. Smart Mater Struct 13(5):N73","journal-title":"Smart Mater Struct"},{"issue":"7","key":"1711_CR26","first-page":"1","volume":"23","author":"ZS Khodaei","year":"2014","unstructured":"Khodaei ZS, Aliabadi MH (2014) Assessment of delay-and-sum algorithms for damage detection in aluminium and composite plates. Smart Mater Struct 23(7):1\u201320","journal-title":"Smart Mater Struct"},{"issue":"11","key":"1711_CR27","doi-asserted-by":"publisher","first-page":"8702","DOI":"10.1109\/TIM.2020.2995441","volume":"69","author":"Z Chen","year":"2020","unstructured":"Chen Z, He G, Li J et al (2020) Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery. IEEE Trans Instrum Meas 69(11):8702\u20138712","journal-title":"IEEE Trans Instrum Meas"},{"key":"1711_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.2987413","volume":"70","author":"R Huang","year":"2020","unstructured":"Huang R, Li J, Liao Y et al (2020) Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task. IEEE Trans Instrum Meas 70:1\u201311","journal-title":"IEEE Trans Instrum Meas"},{"issue":"10","key":"1711_CR29","first-page":"8064","volume":"69","author":"Y Liao","year":"2020","unstructured":"Liao Y, Huang R, Li J et al (2020) Deep semi-supervised domain generalization network for rotary machinery fault diagnosis under variable speed. IEEE Trans Instrum Meas 69(10):8064\u20138075","journal-title":"IEEE Trans Instrum Meas"},{"key":"1711_CR30","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1109\/TMECH.2020.3025615","volume":"26","author":"J Li","year":"2020","unstructured":"Li J, Huang R, He G et al (2020) A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults. IEEE\/ASME Trans Mechatron 26:1591\u20131601","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"1711_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108087","volume":"166","author":"Y Liu","year":"2020","unstructured":"Liu Y, Hong X, Zhang B (2020) A novel velocity anisotropy probability imaging method using ultrasonic guided waves for composite plates. Measurement 166:108087","journal-title":"Measurement"},{"issue":"2510610","key":"1711_CR32","first-page":"1","volume":"70","author":"B Zhang","year":"2021","unstructured":"Zhang B, Hong X, Liu Y (2021) Deep convolutional neural network probability imaging for plate structural health monitoring using guided waves. IEEE Trans Instrum Meas 70(2510610):1\u201310","journal-title":"IEEE Trans Instrum Meas"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-022-01711-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-022-01711-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-022-01711-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T12:13:23Z","timestamp":1668255203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-022-01711-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,23]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1711"],"URL":"https:\/\/doi.org\/10.1007\/s00366-022-01711-9","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,23]]},"assertion":[{"value":"3 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial or non-financial interests that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}