{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T08:24:28Z","timestamp":1768897468437,"version":"3.49.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:00:00Z","timestamp":1630713600000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.<\/jats:p>","DOI":"10.1007\/s12559-021-09922-w","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T08:02:30Z","timestamp":1630742550000},"page":"1263-1273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests"],"prefix":"10.1007","volume":"13","author":[{"given":"Lulu","family":"Tian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9576-7401","authenticated-orcid":false,"given":"Zidong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Weibo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuhua","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Fuad E.","family":"Alsaadi","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"issue":"6","key":"9922_CR1","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.ndteint.2011.05.004","volume":"44","author":"J Wilson","year":"2011","unstructured":"Wilson J, Tian G, Mukriz I, Almond D. PEC thermography for imaging multiple cracks from rolling contact fatigue. NDT & E Int. 2011;44(6):505\u201312.","journal-title":"NDT & E Int"},{"issue":"2\u20133","key":"9922_CR2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1080\/10589750701447920","volume":"22","author":"G Zenzinger","year":"2007","unstructured":"Zenzinger G, Bamberg J, Satzger W, Carl V. Thermographic crack detection by eddy current excitation. Nondestruct Test Evaluation. 2007;22(2\u20133):101\u201311.","journal-title":"Nondestruct Test Evaluation"},{"key":"9922_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neucom.2017.02.099","volume":"277","author":"Y Cheng","year":"2018","unstructured":"Cheng Y, Tian L, Yin C, Huang X, Cao J, Bai L. Research on crack detection applications of improved PCNN algorithm in MOI nondestructive test method. Neurocomputing. 2018;277:249\u201359.","journal-title":"Neurocomputing"},{"key":"9922_CR4","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2016.11.032","volume":"226","author":"L Tian","year":"2017","unstructured":"Tian L, Cheng Y, Yin C, Ding D, Song Y, Bai L. Design of the MOI method based on the artificial neural network for crack detection. Neurocomputing. 2017;226:80\u20139.","journal-title":"Neurocomputing"},{"key":"9922_CR5","doi-asserted-by":"crossref","unstructured":"Tian L,\u00a0Wang Z,\u00a0Cheng\u00a0Y.\u00a0MOI-based stratified crack detection: A PCA approach, In: Proceedings of the 25th International Conference on Automation and Computing. Lancaster, UK.\u00a02019:1\u20137.","DOI":"10.23919\/IConAC.2019.8895098"},{"key":"9922_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.corsci.2013.09.001","volume":"78","author":"Y He","year":"2014","unstructured":"He Y, Tian GY, Pan M, Chen D, Zhang H. An investigation into eddy current pulsed thermography for detection of corrosion blister. Corros Sci. 2014;78:1\u20136.","journal-title":"Corros Sci"},{"issue":"1","key":"9922_CR7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3166\/qirt.1.21-32","volume":"1","author":"G Riegert","year":"2004","unstructured":"Riegert G, Zweschper T, Busse G. Lockin thermography with eddy current excitation. Quant InfraRed Thermogr J. 2004;1(1):21\u201332.","journal-title":"Quant InfraRed Thermogr J"},{"issue":"2","key":"9922_CR8","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1784\/insi.2010.52.2.87","volume":"52","author":"J Wilson","year":"2010","unstructured":"Wilson J, Tian GY, Abidin IZ, Yang S, Almond D. Pulsed eddy current thermography: system development and evaluation. Insight-Non-Destruct Test Cond Monit. 2010;52(2):87\u201390.","journal-title":"Insight-Non-Destruct Test Cond Monit"},{"issue":"3","key":"9922_CR9","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/TETCI.2018.2829906","volume":"2","author":"H-J Li","year":"2018","unstructured":"Li H-J, Bu Z, Wang Z, Cao J, Shi Y. Enhance the performance of network computation by a tunable weighting strategy. IEEE Trans Emerg Top Comput Intell. 2018;2(3):214\u201323.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"9922_CR10","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.inffus.2020.02.006","volume":"60","author":"D Liu","year":"2020","unstructured":"Liu D, Wang Z, Liu Y, Alsaadi FE. Extended Kalman filtering subject to random transmission delays: Dealing with packet disorders. Inf Fusion. 2020;60:80\u20136.","journal-title":"Inf Fusion"},{"key":"9922_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neunet.2020.08.023","volume":"132","author":"S Liu","year":"2020","unstructured":"Liu S, Wang Z, Chen Y, Wei G. Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach. Neural Netw. 2020;132:211\u20139.","journal-title":"Neural Netw"},{"issue":"6","key":"9922_CR12","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1109\/TCYB.2018.2819695","volume":"49","author":"H Zhang","year":"2018","unstructured":"Zhang H, Yue D, Dou C, Zhao W, Xie X. Data-driven distributed optimal consensus control for unknown multiagent systems with input-delay. IEEE Trans Cybern. 2018;49(6):2095\u2013105.","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"9922_CR13","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1109\/JSEN.2018.2882131","volume":"19","author":"P Zhu","year":"2018","unstructured":"Zhu P, Cheng Y, Bai L, Tian L. Local sparseness and image fusion for defect inspection in eddy current pulsed thermography. IEEE Sensors J. 2018;19(4):1471\u20137.","journal-title":"IEEE Sensors J"},{"issue":"1","key":"9922_CR14","first-page":"544","volume":"5","author":"K Murali","year":"2018","unstructured":"Murali K, Reddy DRK, Mulaveesala R. Application of image fusion for the IR images in frequency modulated thermal wave imaging for Non Destructive Testing (NDT). Mater Today: Proc. 2018;5(1):544\u20139.","journal-title":"Mater Today: Proc"},{"key":"9922_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.infrared.2018.01.030","volume":"90","author":"M Doaei","year":"2018","unstructured":"Doaei M, Tavallali MS. Intelligent screening of electrofusion-polyethylene joints based on a thermal NDT method. Infrared Phys Technol. 2018;90:1\u20137.","journal-title":"Infrared Phys Technol"},{"key":"9922_CR16","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ndteint.2017.03.010","volume":"89","author":"A Thiam","year":"2017","unstructured":"Thiam A, Kneip JC, Cicala E, Caulier Y, Jouvard JM, Mattei S. Modeling and optimization of open crack detection by flying spot thermography. NDT & E Int. 2017;89:67\u201373.","journal-title":"NDT & E Int"},{"key":"9922_CR17","doi-asserted-by":"crossref","unstructured":"Tian G,\u00a0Gao Y,\u00a0Li K,\u00a0Wang Y,\u00a0Gao B,\u00a0He\u00a0Y.\u00a0Eddy current pulsed thermography with different excitation configurations for metallic material and defect characterization. Sensors 2016;16(6):843.","DOI":"10.3390\/s16060843"},{"key":"9922_CR18","doi-asserted-by":"crossref","unstructured":"He Y,\u00a0Pan M,\u00a0Luo F.\u00a0Defect characterisation based on heat diffusion using induction thermography testing. Rev Sci Instrum.\u00a02012;83(10):104702.","DOI":"10.1063\/1.4756211"},{"issue":"4","key":"9922_CR19","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TIM.2011.2174095","volume":"61","author":"K Chatterjee","year":"2011","unstructured":"Chatterjee K, Tuli S. Image enhancement in transient lock-in thermography through time series reconstruction and spatial slope correction. IEEE Trans Instrum Meas. 2011;61(4):1079\u201389.","journal-title":"IEEE Trans Instrum Meas"},{"issue":"5","key":"9922_CR20","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.1063\/1.362662","volume":"79","author":"X Maldague","year":"1996","unstructured":"Maldague X, Marinetti S. Pulse phase infrared thermography. J Appl Phys. 1996;79(5):2694\u20138.","journal-title":"J Appl Phys"},{"issue":"4","key":"9922_CR21","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/S0263-8223(02)00161-7","volume":"58","author":"N Rajic","year":"2002","unstructured":"Rajic N. Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures. Compos Struct. 2002;58(4):521\u20138.","journal-title":"Compos Struct"},{"key":"9922_CR22","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.ndteint.2018.09.010","volume":"101","author":"P Zhu","year":"2019","unstructured":"Zhu P, Cheng Y, Banerjee P, Tamburrino A, Deng Y. A novel machine learning model for eddy current testing with uncertainty. NDT & E Int. 2019;101:104\u201312.","journal-title":"NDT & E Int"},{"key":"9922_CR23","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.conbuildmat.2019.07.293","volume":"226","author":"R Ali","year":"2019","unstructured":"Ali R, Cha YJ. Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer. Constr Build Mater. 2019;226:376\u201387.","journal-title":"Constr Build Mater"},{"key":"9922_CR24","doi-asserted-by":"crossref","unstructured":"Cao Y,\u00a0Dong Y,\u00a0Cao Y,\u00a0Yang J,\u00a0Yang MY.\u00a0Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals. NDT & E Int.\u00a02020;112:102246.","DOI":"10.1016\/j.ndteint.2020.102246"},{"key":"9922_CR25","doi-asserted-by":"crossref","unstructured":"Liu W,\u00a0Wang Z,\u00a0Tian L,\u00a0Lauria S,\u00a0Liu\u00a0X.\u00a0Melt pool segmentation for additive manufacturing: A generative adversarial network approach. Comput Electr Eng.\u00a02021;92:107183.","DOI":"10.1016\/j.compeleceng.2021.107183"},{"key":"9922_CR26","doi-asserted-by":"publisher","unstructured":"Wang H,\u00a0Cheng R,\u00a0Zhou J,\u00a0Tao L,\u00a0Kwan\u00a0HK.\u00a0Multistage model for robust face alignment using deep neural networks. Cogn Comput.\u00a0in press.\u00a0https:\/\/doi.org\/10.1007\/s12559-021-09846-5.","DOI":"10.1007\/s12559-021-09846-5"},{"key":"9922_CR27","doi-asserted-by":"publisher","unstructured":"F.\u00a0Zhang, X.\u00a0Wang, T.\u00a0Sun and X.\u00a0Xu, SE-DCGAN: a new method of semantic image restoration. Cogn Comput.\u00a0in press.\u00a0https:\/\/doi.org\/10.1007\/s12559-021-09877-y.","DOI":"10.1007\/s12559-021-09877-y"},{"key":"9922_CR28","doi-asserted-by":"publisher","unstructured":"Ma Y,\u00a0Zhong G,\u00a0Liu W,\u00a0Wang Y,\u00a0Jiang P,\u00a0Zhang R.\u00a0ML-CGAN: Conditional generative adversarial network with a meta-learner structure for high-quality image generation with few training data. Cogn Comput. 2021;13(2):418\u201330.\u00a0https:\/\/doi.org\/10.1007\/s12559-020-09796-4.","DOI":"10.1007\/s12559-020-09796-4"},{"issue":"10","key":"9922_CR29","doi-asserted-by":"publisher","first-page":"8261","DOI":"10.1109\/TIM.2020.2983595","volume":"69","author":"K Liu","year":"2020","unstructured":"Liu K, Li Y, Yang J, Liu Y, Yao Y. Generative principal component thermography for enhanced defect detection and analysis. IEEE Trans Instrum Meas. 2020;69(10):8261\u20139.","journal-title":"IEEE Trans Instrum Meas"},{"key":"9922_CR30","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.ijthermalsci.2015.11.016","volume":"102","author":"Y Wang","year":"2016","unstructured":"Wang Y, Gao B, Tian G, Woo WL, Miao Y. Diffusion and separation mechanism of transient electromagnetic and thermal fields. Int J Therm Sci. 2016;102:308\u201318.","journal-title":"Int J Therm Sci"},{"key":"9922_CR31","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.physa.2016.06.113","volume":"462","author":"J Cao","year":"2016","unstructured":"Cao J, Bu Z, Gao G, Tao H. Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks. Phys A: Stat Mech Appl. 2016;462:386\u201395.","journal-title":"Phys A: Stat Mech Appl"},{"issue":"2","key":"9922_CR32","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s13042-020-01186-4","volume":"12","author":"W Liu","year":"2021","unstructured":"Liu W, Wang Z, Zeng N, Yuan Y, Alsaadi FE, Liu X. A novel randomised particle swarm optimizer. Int J Mach Learn Cybern. 2021;12(2):529\u201340.","journal-title":"Int J Mach Learn Cybern"},{"issue":"4","key":"9922_CR33","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1109\/TEVC.2018.2878536","volume":"23","author":"W Liu","year":"2018","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Bell D. A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans Evol Comput. 2018;23(4):632\u201344.","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"9922_CR34","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1109\/TCYB.2019.2925015","volume":"51","author":"W Liu","year":"2019","unstructured":"Liu W, Wang Z, Yuan Y, Zeng N, Hone K, Liu X. A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Trans Cybern. 2019;51(2):1085\u201393.","journal-title":"IEEE Trans Cybern"},{"key":"9922_CR35","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neucom.2018.12.022","volume":"332","author":"Y Liu","year":"2019","unstructured":"Liu Y, Cheng Q, Gan Y, Wang Y, Li Z, Zhao J. Multi-objective optimization of energy consumption in crude oil pipeline transportation system operation based on exergy loss analysis. Neurocomputing. 2019;332:100\u201310.","journal-title":"Neurocomputing"},{"key":"9922_CR36","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neucom.2018.12.021","volume":"332","author":"Y Liu","year":"2019","unstructured":"Liu Y, Chen S, Guan B, Xu P. Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy. Neurocomputing. 2019;332:159\u201383.","journal-title":"Neurocomputing"},{"issue":"1","key":"9922_CR37","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s12559-016-9442-4","volume":"9","author":"B Song","year":"2017","unstructured":"Song B, Wang Z, Zou L. On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cogn Comput. 2017;9(1):5\u201317.","journal-title":"Cogn Comput"},{"key":"9922_CR38","doi-asserted-by":"crossref","unstructured":"Li Q,\u00a0Shen B,\u00a0Wang Z,\u00a0Sheng W.\u00a0Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach. Automatica.\u00a02020;113:108681.","DOI":"10.1016\/j.automatica.2019.108681"},{"issue":"5","key":"9922_CR39","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TCYB.2018.2818941","volume":"49","author":"Q Li","year":"2019","unstructured":"Li Q, Shen B, Wang Z, Huang T, Luo J. Synchronization control for a class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Trans Cybern. 2019;49(5):1979\u201386.","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"9922_CR40","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1080\/00207721.2020.1768453","volume":"51","author":"X Li","year":"2020","unstructured":"Li X, Han F, Hou N, Dong H, Liu H. Set-membership filtering for piecewise linear systems with censored measurements under Round-Robin protocol. Int J Syst Sci. 2020;51(9):1578\u201388.","journal-title":"Int J Syst Sci"},{"issue":"6","key":"9922_CR41","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1080\/00207721.2021.1872118","volume":"52","author":"L Liu","year":"2021","unstructured":"Liu L, Ma L, Zhang J, Bo Y. Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks. Int J Syst Sci. 2021;52(6):1192\u2013205.","journal-title":"Int J Syst Sci"},{"key":"9922_CR42","doi-asserted-by":"crossref","unstructured":"Liu Y, Shen B, Shu H. Finite-time resilient H\u221e state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism. Neural Networks. 2020;121:356\u201365.","DOI":"10.1016\/j.neunet.2019.09.006"},{"issue":"6","key":"9922_CR43","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1080\/00207721.2020.1868615","volume":"52","author":"J Mao","year":"2021","unstructured":"Mao J, Sun Y, Yi X, Liu H, Ding D. Recursive filtering of networked nonlinear systems: A survey. Int J Syst Sci. 2021;52(6):1110\u201328.","journal-title":"Int J Syst Sci"},{"issue":"1","key":"9922_CR44","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2010.2055155","volume":"41","author":"C Peng","year":"2010","unstructured":"Peng C, Tian Y-C, Yue D. Output feedback control of discrete-time systems in networked environments. IEEE Trans Syst Man Cybern Syst Hum. 2010;41(1):185\u201390.","journal-title":"IEEE Trans Syst Man Cybern Syst Hum"},{"key":"9922_CR45","doi-asserted-by":"crossref","unstructured":"Qian W, Li Y, Chen Y, Liu W. L2-L\u221e\u00a0filtering for stochastic delayed systems with randomly occurring nonlinearities and sensor saturation. Int J Syst Sci. 2020;51(13):2360\u201377.","DOI":"10.1080\/00207721.2020.1794080"},{"key":"9922_CR46","doi-asserted-by":"crossref","unstructured":"Qian W, Li Y, Zhao Y, Chen Y. New optimal method for L2-L\u221e state estimation of delayed neural networks. Neurocomputing. 2020;415:258\u201365.","DOI":"10.1016\/j.neucom.2020.06.118"},{"issue":"1","key":"9922_CR47","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TSMC.2018.2876497","volume":"51","author":"Y Cui","year":"2021","unstructured":"Cui Y, Liu Y, Zhang W, Alsaadi FE. Sampled-based consensus for nonlinear multiagent systems with deception attacks: The decoupled method. IEEE Trans Syst Man Cybern Syst Hum. 2021;51(1):561\u201373.","journal-title":"IEEE Trans Syst Man Cybern Syst Hum"},{"issue":"6","key":"9922_CR48","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1080\/00207721.2021.1885082","volume":"52","author":"J Hu","year":"2021","unstructured":"Hu J, Zhang H, Liu H, Yu X. A survey on sliding mode control for networked control systems. Int J Syst Sci. 2021;52(6):1129\u201347.","journal-title":"Int J Syst Sci"},{"key":"9922_CR49","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.inffus.2018.12.011","volume":"49","author":"Y Liu","year":"2019","unstructured":"Liu Y, Wang Z, Ma L, Alsaadi FE. A partial-nodes-based information fusion approach to state estimation for discrete-time delayed stochastic complex networks. Inf Fusion. 2019;49:240\u20138.","journal-title":"Inf Fusion"},{"key":"9922_CR50","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.neucom.2019.04.065","volume":"357","author":"Y Liu","year":"2019","unstructured":"Liu Y, Shen B, Li Q. State estimation for neural networks with Markov-based nonuniform sampling: The partly unknown transition probability case. Neurocomputing. 2019;357:261\u201370.","journal-title":"Neurocomputing"},{"key":"9922_CR51","doi-asserted-by":"publisher","unstructured":"Luo X,\u00a0Liu Z,\u00a0Jin L,\u00a0Zhou Y,\u00a0Zhou\u00a0M.\u00a0Symmetric non-negative matrix factorization-based community detection models and their convergence analysis. IEEE Trans Neural Netw Learn Syst. 2021. in press.\u00a0https:\/\/doi.org\/10.1109\/TNNLS.2020.3041360.","DOI":"10.1109\/TNNLS.2020.3041360"},{"key":"9922_CR52","doi-asserted-by":"publisher","unstructured":"Luo X,\u00a0Yuan Y,\u00a0Zhou M,\u00a0Liu Z,\u00a0Shang\u00a0M.\u00a0Non-negative latent factor model based on $$\\beta$$-divergence for recommender systems. IEEE Trans Syst Man Cybern Syst Hum.\u00a02019. in press.\u00a0https:\/\/doi.org\/10.1109\/TSMC.2019.2931468.","DOI":"10.1109\/TSMC.2019.2931468"},{"issue":"5","key":"9922_CR53","doi-asserted-by":"publisher","first-page":"1844","DOI":"10.1109\/TCYB.2019.2894283","volume":"50","author":"X Luo","year":"2019","unstructured":"Luo X, Zhou M, Li S, Hu L, Shang M. Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications. IEEE Trans Cybern. 2019;50(5):1844\u201355.","journal-title":"IEEE Trans Cybern"},{"key":"9922_CR54","doi-asserted-by":"publisher","unstructured":"Zhu K,\u00a0Hu J,\u00a0Liu Y,\u00a0Alotaibi ND,\u00a0Alsaadi FE.\u00a0On $$\\ell _{2}$$-$$\\ell _{\\infty }$$ output-feedback control scheduled by stochastic communication protocol for Two-dimensional switched systems. Int J Syst Sci.\u00a0in press.\u00a0https:\/\/doi.org\/10.1080\/00207721.2021.1914768.","DOI":"10.1080\/00207721.2021.1914768"},{"key":"9922_CR55","doi-asserted-by":"publisher","unstructured":"Zou L,\u00a0Wang Z,\u00a0Hu J,\u00a0Liu Y,\u00a0Liu\u00a0X.\u00a0Communication-protocol-based analysis and synthesis of networked systems: Progress, prospects and challenges. Int J Syst Sci.\u00a0in press. https:\/\/doi.org\/10.1080\/00207721.2021.1917721.","DOI":"10.1080\/00207721.2021.1917721"},{"key":"9922_CR56","doi-asserted-by":"crossref","unstructured":"Zou L,\u00a0Wang Z,\u00a0Zhou\u00a0DH.\u00a0Moving horizon estimation with non-uniform sampling under component-based dynamic event-triggered transmission.\u00a0Automatica.\u00a02020;120:109154.","DOI":"10.1016\/j.automatica.2020.109154"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-021-09922-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-021-09922-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-021-09922-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T11:06:12Z","timestamp":1634209572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-021-09922-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["9922"],"URL":"https:\/\/doi.org\/10.1007\/s12559-021-09922-w","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9]]},"assertion":[{"value":"17 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare that they have no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}