{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:38:52Z","timestamp":1783013932757,"version":"3.54.6"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62133008"],"award-info":[{"award-number":["62133008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076150"],"award-info":[{"award-number":["62076150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62003191"],"award-info":[{"award-number":["62003191"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010040","name":"Taishan Scholar Project of Shandong Province","doi-asserted-by":"publisher","award":["TSQN201812092"],"award-info":[{"award-number":["TSQN201812092"]}],"id":[{"id":"10.13039\/501100010040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020QF072"],"award-info":[{"award-number":["ZR2020QF072"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"crossref","award":["2021CXGC011205"],"award-info":[{"award-number":["2021CXGC011205"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"crossref","award":["2021TSGC1053"],"award-info":[{"award-number":["2021TSGC1053"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100016697","name":"Youth Innovation Technology Project of Higher School in Shandong Province","doi-asserted-by":"publisher","award":["2019KJN005"],"award-info":[{"award-number":["2019KJN005"]}],"id":[{"id":"10.13039\/100016697","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016697","name":"Youth Innovation Technology Project of Higher School in Shandong Province","doi-asserted-by":"publisher","award":["2022KJ204"],"award-info":[{"award-number":["2022KJ204"]}],"id":[{"id":"10.13039\/100016697","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08910-5","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T02:02:12Z","timestamp":1691632932000},"page":"22449-22467","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fault diagnosis of air handling unit via combining probabilistic slow feature analysis and attention residual network"],"prefix":"10.1007","volume":"35","author":[{"given":"Chengdong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linyuan","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"8910_CR1","volume":"27","author":"M Elnour","year":"2020","unstructured":"Elnour M, Meskin N, Al-Naemi M (2020) Sensor data validation and fault diagnosis using auto-associative neural network for hvac systems. J Build Eng 27:100935","journal-title":"J Build Eng"},{"key":"8910_CR2","volume":"234","author":"C Fan","year":"2021","unstructured":"Fan C, Liu X, Xue P, Wang J (2021) Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units. Energy Build 234:110733","journal-title":"Energy Build"},{"issue":"7","key":"8910_CR3","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TSMC.2017.2691774","volume":"49","author":"K Yan","year":"2017","unstructured":"Yan K, Ji Z, Huijuan L, Huang J, Shen W, Xue Yu (2017) Fast and accurate classification of time series data using extended elm: application in fault diagnosis of air handling units. IEEE Trans Syst Man Cybern Syst 49(7):1349\u20131356","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"8910_CR4","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105540","volume":"117","author":"C Shen","year":"2023","unstructured":"Shen C, Zhang H, Meng S, Li C (2023) Augmented data driven self-attention deep learning method for imbalanced fault diagnosis of the hvac chiller. Eng Appl Artif Intell 117:105540","journal-title":"Eng Appl Artif Intell"},{"key":"8910_CR5","volume":"250","author":"Z Liu","year":"2021","unstructured":"Liu Z, Huang Z, Wang J, Yue C, Yoon S (2021) A novel fault diagnosis and self-calibration method for air-handling units using bayesian inference and virtual sensing. Energy Build 250:111293","journal-title":"Energy Build"},{"key":"8910_CR6","volume":"252","author":"C Fan","year":"2021","unstructured":"Fan C, Li X, Zhao Y, Wang J (2021) Quantitative assessments on advanced data synthesis strategies for enhancing imbalanced ahu fault diagnosis performance. Energy Build 252:111423","journal-title":"Energy Build"},{"key":"8910_CR7","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.applthermaleng.2015.09.121","volume":"111","author":"Y Zhao","year":"2017","unstructured":"Zhao Y, Jin Wen F, Xiao XY, Wang S (2017) Diagnostic bayesian networks for diagnosing air handling units faults-part i: faults in dampers, fans, filters and sensors. Appl Therm Eng 111:1272\u20131286","journal-title":"Appl Therm Eng"},{"key":"8910_CR8","volume":"203","author":"G Li","year":"2021","unstructured":"Li G, Yao Q, Fan C, Zhou C, Guanghai W, Zhou Z, Fang X (2021) An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems. Build Environ 203:108057","journal-title":"Build Environ"},{"key":"8910_CR9","volume":"5","author":"N Torabi","year":"2022","unstructured":"Torabi N, Gunay HB, O\u2019Brien W, Barton T (2022) Common human errors in design, installation, and operation of vav ahu control systems-a review and a practitioner interview. Build Environ 5:109333","journal-title":"Build Environ"},{"key":"8910_CR10","volume":"253","author":"H Zhang","year":"2021","unstructured":"Zhang H, Li C, Li D, Zhang Y, Peng W (2021) Fault detection and diagnosis of the air handling unit via an enhanced kernel slow feature analysis approach considering the time-wise and batch-wise dynamics. Energy Build 253:111467","journal-title":"Energy Build"},{"key":"8910_CR11","volume":"269","author":"H Zhang","year":"2022","unstructured":"Zhang H, Li C, Wei Q, Zhang Y (2022) Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic sfa and the lstm network. Energy Build 269:112241","journal-title":"Energy Build"},{"key":"8910_CR12","volume":"56","author":"Yu Yulong","year":"2022","unstructured":"Yulong Yu, Zhang H, Peng W, Wang R, Li C (2022) Images based fault diagnosis of air handling unit via combining kernel slow feature analysis and deep learning method. J Build Eng 56:104718","journal-title":"J Build Eng"},{"issue":"2","key":"8910_CR13","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1109\/TASE.2017.2669892","volume":"14","author":"Yan Ying","year":"2017","unstructured":"Ying Yan, Luh Peter B, Pattipati Krishna R (2017) Fault diagnosis of hvac air-handling systems considering fault propagation impacts among components. IEEE Trans Autom Sci Eng 14(2):705\u2013717","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"3","key":"8910_CR14","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ifacol.2019.06.014","volume":"52","author":"L Sun","year":"2019","unstructured":"Sun L, Li Y, Jia H, Ying Yu (2019) Research on fault detection method for air handling units system. IFAC-PapersOnLine 52(3):79\u201384","journal-title":"IFAC-PapersOnLine"},{"key":"8910_CR15","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.enbuild.2013.08.044","volume":"68","author":"S Li","year":"2014","unstructured":"Li S, Wen J (2014) A model-based fault detection and diagnostic methodology based on pca method and wavelet transform. Energy Build 68:63\u201371","journal-title":"Energy Build"},{"key":"8910_CR16","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jprocont.2015.12.004","volume":"39","author":"C Shang","year":"2016","unstructured":"Shang C, Huang B, Yang F, Huang D (2016) Slow feature analysis for monitoring and diagnosis of control performance. J Process Control 39:21\u201334","journal-title":"J Process Control"},{"issue":"11","key":"8910_CR17","doi-asserted-by":"crossref","first-page":"3666","DOI":"10.1002\/aic.14888","volume":"61","author":"C Shang","year":"2015","unstructured":"Shang C, Yang F, Gao X, Huang X, Suykens JAK, Huang D (2015) Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE J 61(11):3666\u20133682","journal-title":"AIChE J"},{"issue":"5","key":"8910_CR18","doi-asserted-by":"crossref","first-page":"3773","DOI":"10.1109\/TIE.2018.2853603","volume":"66","author":"S Zhang","year":"2018","unstructured":"Zhang S, Zhao C (2018) Slow-feature-analysis-based batch process monitoring with comprehensive interpretation of operation condition deviation and dynamic anomaly. IEEE Trans Industr Electron 66(5):3773\u20133783","journal-title":"IEEE Trans Industr Electron"},{"key":"8910_CR19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neucom.2018.02.091","volume":"293","author":"H Zhang","year":"2018","unstructured":"Zhang H, Tian X, Deng X, Cao Y (2018) Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis. Neurocomputing 293:64\u201386","journal-title":"Neurocomputing"},{"issue":"11","key":"8910_CR20","doi-asserted-by":"crossref","first-page":"8895","DOI":"10.1109\/TIE.2018.2811358","volume":"65","author":"C Shang","year":"2018","unstructured":"Shang C, Yang F, Huang B, Huang D (2018) Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Trans Industr Electron 65(11):8895\u20138905","journal-title":"IEEE Trans Industr Electron"},{"issue":"6","key":"8910_CR21","first-page":"3311","volume":"15","author":"Yu Wanke","year":"2018","unstructured":"Wanke Yu, Zhao C (2018) Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification. IEEE Trans Industr Inf 15(6):3311\u20133323","journal-title":"IEEE Trans Industr Inf"},{"key":"8910_CR22","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhou D, Chen M, Hong X (2022) Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring. arXiv preprint arXiv:2202.11295","DOI":"10.1109\/TASE.2022.3219125"},{"key":"8910_CR23","doi-asserted-by":"crossref","first-page":"107456","DOI":"10.1016\/j.compchemeng.2021.107456","volume":"154","author":"VK Puli","year":"2021","unstructured":"Puli VK, Raveendran R, Huang B (2021) Complex probabilistic slow feature extraction with applications in process data analytics. Comput Chem Eng 154:107456","journal-title":"Comput Chem Eng"},{"issue":"8","key":"8910_CR24","doi-asserted-by":"crossref","DOI":"10.7566\/JPSJ.86.084802","volume":"86","author":"T Omori","year":"2017","unstructured":"Omori T, Sekiguchi T, Okada M (2017) Belief propagation for probabilistic slow feature analysis. J Phys Soc Jpn 86(8):084802","journal-title":"J Phys Soc Jpn"},{"key":"8910_CR25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.chemolab.2015.12.017","volume":"151","author":"F Guo","year":"2016","unstructured":"Guo F, Shang C, Huang B, Wang K, Yang F, Huang D (2016) Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemom Intell Lab Syst 151:115\u2013125","journal-title":"Chemom Intell Lab Syst"},{"issue":"12","key":"8910_CR26","doi-asserted-by":"crossref","first-page":"4126","DOI":"10.1002\/aic.14937","volume":"61","author":"C Shang","year":"2015","unstructured":"Shang C, Huang B, Yang F, Huang D (2015) Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE J 61(12):4126\u20134139","journal-title":"AIChE J"},{"issue":"3","key":"8910_CR27","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1002\/aic.16481","volume":"65","author":"L Fan","year":"2019","unstructured":"Fan L, Kodamana H, Huang B (2019) Semi-supervised dynamic latent variable modeling: I\/o probabilistic slow feature analysis approach. AIChE J 65(3):964\u2013979","journal-title":"AIChE J"},{"key":"8910_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2017.12.009","volume":"173","author":"L Fan","year":"2018","unstructured":"Fan L, Kodamana H, Huang B (2018) Identification of robust probabilistic slow feature regression model for process data contaminated with outliers. Chemom Intell Lab Syst 173:1\u201313","journal-title":"Chemom Intell Lab Syst"},{"key":"8910_CR29","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ijleo.2016.10.117","volume":"130","author":"Z Tang","year":"2017","unstructured":"Tang Z, Li C, Sun S (2017) Single-trial eeg classification of motor imagery using deep convolutional neural networks. Optik 130:11\u201318","journal-title":"Optik"},{"key":"8910_CR30","unstructured":"Athiwaratkun B, Kang K (2015) Feature representation in convolutional neural networks. arXiv preprint arXiv:1507.02313"},{"key":"8910_CR31","volume":"34","author":"J Gao","year":"2021","unstructured":"Gao J, Han H, Ren Z, Fan Y (2021) Fault diagnosis for building chillers based on data self-production and deep convolutional neural network. J Build Eng 34:102043","journal-title":"J Build Eng"},{"issue":"2","key":"8910_CR32","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","volume":"33","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yi S, Liang G, Hongli G, Xin H, Hongliang S (2020) A new bearing fault diagnosis method based on modified convolutional neural networks. Chin J Aeron 33(2):439\u2013447","journal-title":"Chin J Aeron"},{"key":"8910_CR33","doi-asserted-by":"crossref","first-page":"115299","DOI":"10.1109\/ACCESS.2020.3004489","volume":"8","author":"N Riaz","year":"2020","unstructured":"Riaz N, Shah S, Irtiza A, Rehman F, Gilani SO, Udin E (2020) A novel 2-d current signal-based residual learning with optimized softmax to identify faults in ball screw actuators. IEEE Access 8:115299\u2013115313","journal-title":"IEEE Access"},{"key":"8910_CR34","doi-asserted-by":"crossref","first-page":"115877","DOI":"10.1016\/j.energy.2019.115877","volume":"187","author":"YH Eom","year":"2019","unstructured":"Eom YH, Yoo JW, Hong SB, Kim MS (2019) Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving. Energy 187:115877","journal-title":"Energy"},{"issue":"7","key":"8910_CR35","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","volume":"65","author":"L Wen","year":"2017","unstructured":"Wen L, Li X, Gao L, Zhang Y (2017) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Industr Electron 65(7):5990\u20135998","journal-title":"IEEE Trans Industr Electron"},{"key":"8910_CR36","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.isatra.2022.02.048","volume":"129","author":"Y Li","year":"2022","unstructured":"Li Y, Xiaoqiang D, Wang X, Si S (2022) Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging. ISA Trans 129:309\u2013320","journal-title":"ISA Trans"},{"key":"8910_CR37","doi-asserted-by":"crossref","unstructured":"Khan RU, Zhang X, Kumar R, Aboagye EO (2018) Evaluating the performance of resnet model based on image recognition. In: Proceedings of the 2018 international conference on computing and artificial intelligence, pp. 86\u201390","DOI":"10.1145\/3194452.3194461"},{"issue":"1","key":"8910_CR38","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/TCBB.2020.2986544","volume":"18","author":"X Yu","year":"2020","unstructured":"Yu X, Kang C, Guttery DS, Kadry S, Chen Y, Zhang Y-D (2020) Resnet-scda-50 for breast abnormality classification. IEEE\/ACM Trans Comput Biol Bioinf 18(1):94\u2013102","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"8910_CR39","unstructured":"Farooq M, Hafeez A (2020) Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395"},{"key":"8910_CR40","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","volume":"32","author":"L Wen","year":"2020","unstructured":"Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on resnet-50. Neural Comput Appl 32:6111\u20136124","journal-title":"Neural Comput Appl"},{"key":"8910_CR41","unstructured":"Wightman R, Touvron H, J\u00e9gou H (2021) Resnet strikes back: an improved training procedure in timm. arXiv preprint arXiv:2110.00476"},{"key":"8910_CR42","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Yg (2018) In\u00a0So Kweon. Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"18","key":"8910_CR43","doi-asserted-by":"crossref","first-page":"17431","DOI":"10.1109\/JSEN.2021.3062442","volume":"22","author":"S-H Wang","year":"2021","unstructured":"Wang S-H, Fernandes SL, Zhu Z, Zhang Y-D (2021) Avnc: attention-based vgg-style network for covid-19 diagnosis by cbam. IEEE Sens J 22(18):17431\u201317438","journal-title":"IEEE Sens J"},{"issue":"1","key":"8910_CR44","doi-asserted-by":"crossref","first-page":"11399","DOI":"10.1038\/s41598-019-47765-6","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma A, Vans E, Shigemizu D, Boroevich KA, Tsunoda T (2019) Deepinsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep 9(1):11399","journal-title":"Sci Rep"},{"key":"8910_CR45","volume":"224","author":"J-S Chou","year":"2021","unstructured":"Chou J-S, Truong D-N, Kuo C-C (2021) Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning. Energy 224:120100","journal-title":"Energy"},{"key":"8910_CR46","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14, pp 630\u2013645. Springer","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"8910_CR47","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8910_CR48","doi-asserted-by":"crossref","unstructured":"Huo T, Fan J, Li X, Chen H, Gao B, Li X (2020) Traffic sign recognition based on resnet-20 and deep mutual learning. In: 2020 Chinese Automation Congress (CAC), pp 4770\u20134774. IEEE","DOI":"10.1109\/CAC51589.2020.9327282"},{"issue":"8","key":"8910_CR49","doi-asserted-by":"crossref","first-page":"380","DOI":"10.3390\/info11080380","volume":"11","author":"B Chen","year":"2020","unstructured":"Chen B, Zhang Z, Liu N, Tan Y, Liu X, Chen T (2020) Spatiotemporal convolutional neural network with convolutional block attention module for micro-expression recognition. Information 11(8):380","journal-title":"Information"},{"issue":"21","key":"8910_CR50","doi-asserted-by":"crossref","first-page":"12080","DOI":"10.3390\/ijms222112080","volume":"22","author":"Yu Minzhe","year":"2021","unstructured":"Minzhe Yu, Duan Y, Li Z, Zhang Y (2021) Prediction of peptide detectability based on capsnet and convolutional block attention module. Int J Mol Sci 22(21):12080","journal-title":"Int J Mol Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08910-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08910-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08910-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:07:29Z","timestamp":1694876849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08910-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":50,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8910"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08910-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"assertion":[{"value":"10 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}