{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:38:37Z","timestamp":1763642317648,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2017,11,18]],"date-time":"2017-11-18T00:00:00Z","timestamp":1510963200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61240047"],"award-info":[{"award-number":["61240047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Beijing","award":["4152041"],"award-info":[{"award-number":["4152041"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s00521-017-3277-0","type":"journal-article","created":{"date-parts":[[2017,11,18]],"date-time":"2017-11-18T04:48:41Z","timestamp":1510980521000},"page":"3295-3305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Dynamic hypersphere SVDD without describing boundary for one-class classification"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3398-7967","authenticated-orcid":false,"given":"Jianlin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kepeng","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqiang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,11,18]]},"reference":[{"issue":"3","key":"3277_CR1","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"11","key":"3277_CR2","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","volume":"20","author":"DMJ Tax","year":"1999","unstructured":"Tax DMJ, Duin RPW (1999) Support vector domain description. Pattern Recogn Lett 20(11):1191\u20131199","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"3277_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54(1):45\u201366","journal-title":"Mach Learn"},{"issue":"1","key":"3277_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/cem.778","volume":"17","author":"T Kourti","year":"2003","unstructured":"Kourti T (2003) Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J Chemom 17(1):93\u2013109","journal-title":"J Chemom"},{"issue":"2","key":"3277_CR5","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1021\/ie403210t","volume":"53","author":"Z Ge","year":"2014","unstructured":"Ge Z, Song Z (2014) Online monitoring and quality prediction of multiphase batch processes with uneven length problem. Ind Eng Chem Res 53(2):800\u2013811","journal-title":"Ind Eng Chem Res"},{"issue":"3","key":"3277_CR6","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1109\/TITB.2011.2113352","volume":"15","author":"JH Shin","year":"2011","unstructured":"Shin JH, Lee B, Park KS (2011) Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE T Inf Technol B 15(3):438\u2013448","journal-title":"IEEE T Inf Technol B"},{"issue":"2","key":"3277_CR7","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/j.asoc.2012.11.005","volume":"13","author":"S Wang","year":"2013","unstructured":"Wang S, Yu J, Lapira E, Lee J (2013) A modified support vector data description based novelty detection approach for machinery components. Appl Soft Comput 13(2):1193\u20131205","journal-title":"Appl Soft Comput"},{"issue":"2","key":"3277_CR8","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s12293-014-0147-5","volume":"7","author":"J Prakash","year":"2015","unstructured":"Prakash J, Singh PK (2015) An effective multi objective approach for hard partitional clustering. Memet Comput 7(2):93\u2013104","journal-title":"Memet Comput"},{"issue":"11","key":"3277_CR9","doi-asserted-by":"publisher","first-page":"3273","DOI":"10.1080\/00207543.2013.870362","volume":"52","author":"Q Jiang","year":"2014","unstructured":"Jiang Q, Yan X, Lv Z, Guo M (2014) Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description. Int J Prod Res 52(11):3273\u20133286","journal-title":"Int J Prod Res"},{"issue":"10","key":"3277_CR10","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1016\/j.jprocont.2009.05.001","volume":"19","author":"Z Ge","year":"2009","unstructured":"Ge Z, Xie L, Kruger U, Lamout L, Song Z (2009) Sensor fault identification and isolation for multivariate non-Gaussian processes. J Process Control 19(10):1707\u20131715","journal-title":"J Process Control"},{"key":"3277_CR11","first-page":"141","volume":"283","author":"YC Chen","year":"2016","unstructured":"Chen YC, Su CT (2016) Distance-based margin support vector machine for classification. Appl Math Comput 283:141\u2013152","journal-title":"Appl Math Comput"},{"issue":"34","key":"3277_CR12","doi-asserted-by":"publisher","first-page":"9205","DOI":"10.1021\/acs.iecr.6b00901","volume":"55","author":"Z Lv","year":"2016","unstructured":"Lv Z, Yan X (2016) Hierarchical support vector data description for batch process monitoring. Ind Eng Chem Res 55(34):9205\u20139214","journal-title":"Ind Eng Chem Res"},{"issue":"13","key":"3277_CR13","doi-asserted-by":"publisher","first-page":"2975","DOI":"10.1080\/1352816031000075224","volume":"41","author":"R Sun","year":"2003","unstructured":"Sun R, Tsung F (2003) A kernel-distance-based multivariate control chart using support vector methods. Int J Prod Res 41(13):2975\u20132989","journal-title":"Int J Prod Res"},{"issue":"3\u20134","key":"3277_CR14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/cem.1281","volume":"24","author":"S Kittiwachana","year":"2010","unstructured":"Kittiwachana S, Ferreira DLS, Lloyd GR, Fido LA, Thompson DR, Escott RE, Brereton RG (2010) One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process. J Chemom 24(3\u20134):96\u2013110","journal-title":"J Chemom"},{"issue":"3","key":"3277_CR15","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1002\/aic.14335","volume":"60","author":"Q Jiang","year":"2014","unstructured":"Jiang Q, Yan X (2014) Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring. AIChE J 60(3):949\u2013965","journal-title":"AIChE J"},{"issue":"2","key":"3277_CR16","doi-asserted-by":"publisher","first-page":"2166","DOI":"10.1016\/j.eswa.2011.07.045","volume":"39","author":"IB Khediri","year":"2012","unstructured":"Khediri IB, Weihs C, Limam M (2012) Kernel k-means clustering based local support vector domain description fault detection of multimodal processes. Expert Syst Appl 39(2):2166\u20132171","journal-title":"Expert Syst Appl"},{"issue":"1","key":"3277_CR17","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1080\/00207540500216037","volume":"44","author":"S Kumar","year":"2006","unstructured":"Kumar S, Choudhary AK, Kumar M, Shankar R, Tiwari MK (2006) Kernel distance-based robust support vector methods and its application in developing a robust K-chart. Int J Prod Res 44(1):77\u201396","journal-title":"Int J Prod Res"},{"issue":"4","key":"3277_CR18","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1080\/0740817X.2012.712237","volume":"45","author":"X Ning","year":"2013","unstructured":"Ning X, Tsung F (2013) Improved design of kernel distance-based charts using support vector methods. IIE Trans 45(4):464\u2013476","journal-title":"IIE Trans"},{"issue":"8","key":"3277_CR19","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1016\/j.jprocont.2013.06.010","volume":"23","author":"Z Ge","year":"2013","unstructured":"Ge Z, Song Z (2013) Bagging support vector data description model for batch process monitoring. J Process Control 23(8):1090\u20131096","journal-title":"J Process Control"},{"issue":"10","key":"3277_CR20","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1016\/j.jprocont.2009.07.011","volume":"19","author":"S Mahadevan","year":"2009","unstructured":"Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Control 19(10):1627\u20131639","journal-title":"J Process Control"},{"issue":"2","key":"3277_CR21","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1080\/07408170903019150","volume":"42","author":"T Sukchotrat","year":"2009","unstructured":"Sukchotrat T, Kim SB, Tsung F (2009) One-class classification-based control charts for multivariate process monitoring. IIE Trans 42(2):107\u2013120","journal-title":"IIE Trans"},{"issue":"7","key":"3277_CR22","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1016\/j.jprocont.2014.05.015","volume":"24","author":"M Yao","year":"2014","unstructured":"Yao M, Wang H, Xu W (2014) Batch process monitoring based on functional data analysis and support vector data description. J Process Control 24(7):1085\u20131097","journal-title":"J Process Control"},{"key":"3277_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/9780470746462","volume-title":"Chemometrics for pattern recognition","author":"R Brereton","year":"2009","unstructured":"Brereton R (2009) Chemometrics for pattern recognition. Wiley, New York"},{"issue":"6","key":"3277_CR24","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1002\/qre.1688","volume":"30","author":"P Phaladiganon","year":"2014","unstructured":"Phaladiganon P, Kim SB, Chen VC (2014) A density-focused support vector data description method. Qual Reliab Eng Int 30(6):879\u2013890","journal-title":"Qual Reliab Eng Int"},{"key":"3277_CR25","unstructured":"Tax DMJ (2015a) Data sets for one-class classification. \n                    http:\/\/homepage.tudelft.nl\/n9d04\/occ\/\n                    \n                  . Accessed August 2016"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3277-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T07:45:32Z","timestamp":1568965532000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3277-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,18]]},"references-count":25,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["3277"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3277-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,11,18]]},"assertion":[{"value":"2 January 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2017","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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}