{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T04:52:29Z","timestamp":1749099149811,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T00:00:00Z","timestamp":1591833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T00:00:00Z","timestamp":1591833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Developing a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component\u2019s all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward\u2019s hierarchical clustering with multivariate Euclidean distance calculation and Kohonen self-organizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.<\/jats:p>","DOI":"10.1007\/s42979-020-00202-2","type":"journal-article","created":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T15:04:15Z","timestamp":1591887855000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Monitoring Pneumatic Actuators\u2019 Behavior Using Real-World Data Set"],"prefix":"10.1007","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7408-998X","authenticated-orcid":false,"given":"Tibor","family":"Kovacs","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-1143","authenticated-orcid":false,"given":"Andrea","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,11]]},"reference":[{"key":"202_CR1","doi-asserted-by":"publisher","DOI":"10.5162\/sensor2015\/D8.1","author":"N Helwig","year":"2015","unstructured":"Helwig N, Pignanelli E, Sch\u00fctze A. Detecting and compensating sensor faults in a hydraulic condition monitoring system. Proc Sens. 2015. https:\/\/doi.org\/10.5162\/sensor2015\/D8.1.","journal-title":"Proc Sens"},{"key":"202_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1754414.1754419","volume":"6","author":"AB Sharma","year":"2010","unstructured":"Sharma AB, Golubchik L, Govindan R. Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans Sens Netw. 2010;6:1\u201339. https:\/\/doi.org\/10.1145\/1754414.1754419.","journal-title":"ACM Trans Sens Netw"},{"key":"202_CR3","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","volume":"48","author":"F Tao","year":"2018","unstructured":"Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. J Manuf Syst. 2018;48:157\u201369. https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.006.","journal-title":"J Manuf Syst"},{"key":"202_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/JPROC.2015.2388958","volume":"103","author":"S Yin","year":"2015","unstructured":"Yin S, Kaynak O. Big data for modern industry: challenges and trends. Proc IEEE. 2015;103:143\u20136. https:\/\/doi.org\/10.1109\/JPROC.2015.2388958.","journal-title":"Proc IEEE"},{"key":"202_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0034-z","volume":"2","author":"P O\u2019Donovan","year":"2015","unstructured":"O\u2019Donovan P, Leahy K, Bruton K, O\u2019Sullivan DTJ. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J Big Data. 2015;2:1\u201326. https:\/\/doi.org\/10.1186\/s40537-015-0034-z.","journal-title":"J Big Data"},{"key":"202_CR6","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi A, Haider M. Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag. 2015;35:137\u201344. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2014.10.007.","journal-title":"Int J Inf Manag"},{"key":"202_CR7","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1243\/095440505X32274","volume":"219","author":"DT Pham","year":"2005","unstructured":"Pham DT, Afify AA. Machine-learning techniques and their applications in manufacturing. Proc Inst Mech Eng Part B J Eng Manuf. 2005;219:395\u2013412. https:\/\/doi.org\/10.1243\/095440505X32274.","journal-title":"Proc Inst Mech Eng Part B J Eng Manuf"},{"key":"202_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-4018-5","volume-title":"Missing data","author":"JW Graham","year":"2012","unstructured":"Graham JW. Missing data. New York: Springer; 2012."},{"key":"202_CR9","unstructured":"Kabacoff RI, editors. Advanced methods for missing data. In: R in action data analysis and graphics with R. Shelter Island: Manning Publications Co., 2011; p. 472."},{"key":"202_CR10","doi-asserted-by":"publisher","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","volume":"63","author":"Y Lei","year":"2016","unstructured":"Lei Y, Jia F, Lin J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron. 2016;63:3137\u201347.","journal-title":"IEEE Trans Ind Electron"},{"key":"202_CR11","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1098\/rsta.2006.1929","volume":"365","author":"SD Fassois","year":"2007","unstructured":"Fassois SD, Sakellariou JS. Time-series methods for fault detection and identification in vibrating structures. Philos Trans R Soc A Math Phys Eng Sci. 2007;365:411\u201348. https:\/\/doi.org\/10.1098\/rsta.2006.1929.","journal-title":"Philos Trans R Soc A Math Phys Eng Sci"},{"key":"202_CR12","first-page":"893","volume":"2014","author":"S Munirathinam","year":"2014","unstructured":"Munirathinam S, Ramadoss B. Big data predictive analytics for proactive semiconductor equipment maintenance. IEEE Int Conf Big Data (Big Data). 2014;2014:893\u2013902.","journal-title":"IEEE Int Conf Big Data (Big Data)"},{"key":"202_CR13","first-page":"470","volume":"11","author":"MAS Al Tobi","year":"2017","unstructured":"Al Tobi MAS, Bevan G, Ramachandran KP, et al. Experimental set-up for investigation of fault diagnosis of a centrifugal pump. World Acad Sci Eng Technol Int J Mech Aerosp Ind Mechatron Manuf Eng. 2017;11:470\u20134.","journal-title":"World Acad Sci Eng Technol Int J Mech Aerosp Ind Mechatron Manuf Eng"},{"key":"202_CR14","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.ymssp.2010.07.013","volume":"25","author":"K Worden","year":"2011","unstructured":"Worden K, Staszewski WJ, Hensman JJ. Natural computing for mechanical systems research: a tutorial overview. Mech Syst Signal Process. 2011;25:4\u2013111.","journal-title":"Mech Syst Signal Process"},{"key":"202_CR15","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1109\/TIE.2013.2261033","volume":"61","author":"Y Shatnawi","year":"2013","unstructured":"Shatnawi Y, Al-Khassaweneh M. Fault diagnosis in internal combustion engines using extension neural network. IEEE Trans Ind Electron. 2013;61:1434\u201343.","journal-title":"IEEE Trans Ind Electron"},{"key":"202_CR16","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TIE.2014.2319216","volume":"62","author":"D You","year":"2014","unstructured":"You D, Gao X, Katayama S. WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans Ind Electron. 2014;62:628\u201336.","journal-title":"IEEE Trans Ind Electron"},{"key":"202_CR17","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.ymssp.2007.07.013","volume":"22","author":"Y Lei","year":"2008","unstructured":"Lei Y, He Z, Zi Y, Chen X. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mech Syst Signal Process. 2008;22:419\u201335.","journal-title":"Mech Syst Signal Process"},{"key":"202_CR18","doi-asserted-by":"publisher","first-page":"6418","DOI":"10.1109\/TIE.2014.2301773","volume":"61","author":"S Yin","year":"2014","unstructured":"Yin S, Ding SX, Xie X, Luo H. A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron. 2014;61:6418\u201328.","journal-title":"IEEE Trans Ind Electron"},{"key":"202_CR19","unstructured":"Piddington C, Pegram M. An IMS test case\u2014global manufacturing. In: Proceedings of the IFIP TC5\/WG5.7 Fifth international conference on advances in production management systems. North-Holland Publishing Co., Amsterdam, The Netherlands; 1993. p. 11\u201320."},{"key":"202_CR20","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1147\/rd.116.0601","volume":"11","author":"AL Samuel","year":"1967","unstructured":"Samuel AL. Some studies in machine learning using the game of checkers. II\u2014Recent progress. IBM J Res Dev. 1967;11:601\u201317. https:\/\/doi.org\/10.1147\/rd.116.0601.","journal-title":"IBM J Res Dev"},{"key":"202_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest T, Weimer D, Irgens C, Thoben K-D. Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res. 2016;4:23\u201345. https:\/\/doi.org\/10.1080\/21693277.2016.1192517.","journal-title":"Prod Manuf Res"},{"key":"202_CR22","first-page":"1058","volume-title":"Springer","author":"D Cohn","year":"2011","unstructured":"Cohn D. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning. Boston: Springer; 2011. p. 1058."},{"key":"202_CR23","unstructured":"Schwabacher M, Goebel K. A survey of artificial intelligence for prognostics. In: AAAI fall symposium; 2007. p. 107\u201314."},{"key":"202_CR24","doi-asserted-by":"crossref","unstructured":"Byington CS, Watson M, Edwards D, Dunkin B. In-line health monitoring system for hydraulic pumps and motors. In: 2003 IEEE aerospace conference proceedings (Cat. No. 03TH8652). IEEE, p. 3279\u201387.","DOI":"10.1109\/AERO.2003.1234171"},{"key":"202_CR25","unstructured":"Kubat M, Matwin S et al. Addressing the curse of imbalanced training sets: one-sided selection. In: ICML; 1997. p. 179\u2013186."},{"key":"202_CR26","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10845-015-1110-0","volume":"29","author":"P Santos","year":"2018","unstructured":"Santos P, Maudes J, Bustillo A. Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. J Intell Manuf. 2018;29:333\u201351. https:\/\/doi.org\/10.1007\/s10845-015-1110-0.","journal-title":"J Intell Manuf"},{"key":"202_CR27","doi-asserted-by":"publisher","first-page":"11994","DOI":"10.1016\/j.eswa.2009.05.029","volume":"36","author":"C-F Tsai","year":"2009","unstructured":"Tsai C-F, Hsu Y-F, Lin C-Y, Lin W-Y. Intrusion detection by machine learning: a review. Expert Syst Appl. 2009;36:11994\u20132000. https:\/\/doi.org\/10.1016\/j.eswa.2009.05.029.","journal-title":"Expert Syst Appl"},{"key":"202_CR28","unstructured":"Gopalakrishnan V, Ramaswamy C (2014) Sentiment Learning from Imbalanced Dataset: An Ensemble Based Method."},{"key":"202_CR29","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s11280-012-0178-0","volume":"16","author":"W Wei","year":"2013","unstructured":"Wei W, Li J, Cao L, et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web. 2013;16:449\u201375. https:\/\/doi.org\/10.1007\/s11280-012-0178-0.","journal-title":"World Wide Web"},{"key":"202_CR30","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2017.03.011","volume":"243","author":"N Ofek","year":"2017","unstructured":"Ofek N, Rokach L, Stern R, Shabtai A. Fast-CBUS: a fast clustering-based undersampling method for addressing the class imbalance problem. Neurocomputing. 2017;243:88\u2013102. https:\/\/doi.org\/10.1016\/j.neucom.2017.03.011.","journal-title":"Neurocomputing"},{"key":"202_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8","volume-title":"Encyclopedia of machine learning","author":"C Sammut","year":"2010","unstructured":"Sammut C, Webb GI. Encyclopedia of machine learning. Boston: Springer; 2010."},{"key":"202_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5923\/j.ajis.20160601.01","volume":"6","author":"K Hansson","year":"2016","unstructured":"Hansson K, Yella S, Dougherty M, Fleyeh H. Machine learning algorithms in heavy process manufacturing. Am J Intell Syst. 2016;6:1\u201313. https:\/\/doi.org\/10.5923\/j.ajis.20160601.01.","journal-title":"Am J Intell Syst"},{"key":"202_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409\u2013410","author":"W-C Lin","year":"2017","unstructured":"Lin W-C, Tsai C-F, Hu Y-H, Jhang J-S. Clustering-based undersampling in class-imbalanced data. Inf Sci. 2017;409\u2013410:17\u201326. https:\/\/doi.org\/10.1016\/j.ins.2017.05.008.","journal-title":"Inf Sci"},{"key":"202_CR34","doi-asserted-by":"publisher","first-page":"5718","DOI":"10.1016\/j.eswa.2008.06.108","volume":"36","author":"S-J Yen","year":"2009","unstructured":"Yen S-J, Lee Y-S. Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst Appl. 2009;36:5718\u201327. https:\/\/doi.org\/10.1016\/j.eswa.2008.06.108.","journal-title":"Expert Syst Appl"},{"key":"202_CR35","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1006\/mssp.1994.1039","volume":"8","author":"O Bardou","year":"1994","unstructured":"Bardou O, Sidahmed M. Early detection of leakages in the exhaust and discharge systems of reciprocating machines by vibration analysis. Mech Syst Signal Process. 1994;8:551\u201370. https:\/\/doi.org\/10.1006\/mssp.1994.1039.","journal-title":"Mech Syst Signal Process"},{"key":"202_CR36","unstructured":"R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria; 2017. https:\/\/www.R-project.org\/. Accessed 14 May 2020."},{"key":"202_CR37","unstructured":"Ulrich JM, Ryan JA, Bennett R, Joy C. R package \u2018xts\u2019; eXtensible time series; 2018. https:\/\/CRAN.R-project.org\/package=xts. Accessed 14 May 2020."},{"key":"202_CR38","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, McRae JE, Terpenning I. STL: a seasonal-trend decomposition procedure based on loess. J Off Stat. 1990;6:3\u201373.","journal-title":"J Off Stat"},{"key":"202_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v053.i09","volume":"53","author":"D M\u00fcllner","year":"2015","unstructured":"M\u00fcllner D. Fastcluster: fast hierarchical, agglomerative clustering routines for R and Python. J Stat Softw. 2015;53:1\u201318. https:\/\/doi.org\/10.18637\/jss.v053.i09.","journal-title":"J Stat Softw"},{"key":"202_CR40","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JHJ Ward","year":"1963","unstructured":"Ward JHJ. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58:236\u201344. https:\/\/doi.org\/10.1080\/01621459.1963.10500845.","journal-title":"J Am Stat Assoc"},{"key":"202_CR41","unstructured":"Calaway R, Microsoft, Weston S, Tenenbaum D. R package: doParallel; 2018. https:\/\/cran.r-project.org\/package=doParallel. Accessed 14 May 2020."},{"key":"202_CR42","unstructured":"Calaway R, Microsoft, Weston S. R package: foreach; 2017. https:\/\/cran.r-project.org\/package=foreach. Accessed 14 May 2020."},{"key":"202_CR43","unstructured":"Yau C. R package \u2018rpud\u2019: R functions for computation on GPU; 2015. http:\/\/www.r-tutor.com\/gpu-computing. Accessed 14 May 2020."},{"key":"202_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1\u201326. https:\/\/doi.org\/10.18637\/jss.v028.i05.","journal-title":"J Stat Softw"},{"key":"202_CR45","first-page":"13","volume-title":"Imbalanced learning: foundations, algorithms, and applications: foundations of imbalanced learning","author":"GM Weiss","year":"2012","unstructured":"Weiss GM. Imbalanced learning: foundations, algorithms, and applications: foundations of imbalanced learning. Hoboken: Wiley; 2012. p. 13\u201341."},{"key":"202_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v021.i05","volume":"21","author":"R Wehrens","year":"2007","unstructured":"Wehrens R, Buydens LMC. Self- and super-organizing maps in R: the Kohonen package. J Stat Softw. 2007;21:1\u201319. https:\/\/doi.org\/10.18637\/jss.v021.i05.","journal-title":"J Stat Softw"},{"key":"202_CR47","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen T. The self-organizing map. Proc IEEE. 1990;78:1464\u201380. https:\/\/doi.org\/10.1109\/5.58325.","journal-title":"Proc IEEE"},{"key":"202_CR48","doi-asserted-by":"crossref","unstructured":"Ahmad A, Yusoff R, Ismail MN, Rosli NR. Clustering the imbalanced datasets using modified Kohonen self-organizing map (KSOM). In: 2017 computing conference. IEEE. p. 751\u20135.","DOI":"10.1109\/SAI.2017.8252180"},{"key":"202_CR49","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/S0167-9473(01)00040-8","volume":"38","author":"MY Kiang","year":"2001","unstructured":"Kiang MY. Extending the Kohonen self-organizing map networks for clustering analysis. Comput Stat Data Anal. 2001;38:161\u201380. https:\/\/doi.org\/10.1016\/S0167-9473(01)00040-8.","journal-title":"Comput Stat Data Anal"},{"key":"202_CR50","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1159\/000266245","volume":"45","author":"L Leinonen","year":"1993","unstructured":"Leinonen L, Mujunen R, Kangas J, Torkkola K. Acoustic pattern recognition of fricative-vowel coarticulation by the self-organizing map. Folia Phoniatrica et Logopaedica. 1993;45:173\u201381. https:\/\/doi.org\/10.1159\/000266245.","journal-title":"Folia Phoniatrica et Logopaedica"},{"key":"202_CR51","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1049\/ip-f-2.1993.0022","volume":"140","author":"CN Manikopoulos","year":"1993","unstructured":"Manikopoulos CN. Finite state vector quantisation with neural network classification of states. IEE Proc F Radar Signal Process. 1993;140:153. https:\/\/doi.org\/10.1049\/ip-f-2.1993.0022.","journal-title":"IEE Proc F Radar Signal Process"},{"key":"202_CR52","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/32.245741","volume":"19","author":"A Del Bimbo","year":"1993","unstructured":"Del Bimbo A, Campanai M, Nesi P. A three-dimensional iconic environment for image database querying. IEEE Trans Softw Eng. 1993;19:997\u20131011. https:\/\/doi.org\/10.1109\/32.245741.","journal-title":"IEEE Trans Softw Eng"},{"key":"202_CR53","unstructured":"Vercauteren L, Sieben G, Praet M, et al. The classification of brain tumours by a topological map. In: Proceedings of the international neural networks conference, Paris; 1990. p. 387\u201391."},{"key":"202_CR54","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique Nitesh. J Artif Intell Res. 2002;16:321\u201357. https:\/\/doi.org\/10.1613\/jair.953.","journal-title":"J Artif Intell Res"},{"key":"202_CR55","unstructured":"He H, Bai Y, Garcia EA, Li S. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence); 2008. p. 1322\u20138."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00202-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-020-00202-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00202-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T12:34:50Z","timestamp":1723034090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-020-00202-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,11]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["202"],"URL":"https:\/\/doi.org\/10.1007\/s42979-020-00202-2","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"type":"print","value":"2662-995X"},{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2020,6,11]]},"assertion":[{"value":"7 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"Authors are not aware of any conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Available upon request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}],"article-number":"196"}}