{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:54:11Z","timestamp":1743062051371,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030600280"},{"type":"electronic","value":"9783030600297"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60029-7_18","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T23:07:40Z","timestamp":1600729660000},"page":"194-201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fast Dynamic Density Outlier Detection Algorithm for Power Quality Disturbance Data"],"prefix":"10.1007","author":[{"given":"Siyu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jun","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"issue":"1","key":"18_CR1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1080\/00051144.2017.1343328","volume":"58","author":"L Banjanovicmehmedovic","year":"2017","unstructured":"Banjanovicmehmedovic, L., Hajdarevic, A., Kantardzic, M., Mehmedovic, F., Dzananovic, I.: Neural network-based data-driven modelling of anomaly detection in thermal power plant. Automatika 58(1), 69\u201379 (2017)","journal-title":"Automatika"},{"key":"18_CR2","unstructured":"Chen, H., Ji, M., Guo, Z., Xia, Y.: A dynamic density clustering algorithm for time series data. Control Theor. Appl. 8 (2019)"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Qian, J., Saligrama, V.: A new one-class SVM for anomaly detection, pp. 3567\u20133571 (2013)","DOI":"10.1109\/ICASSP.2013.6638322"},{"issue":"1","key":"18_CR4","first-page":"26","volume":"34","author":"M Duan","year":"2013","unstructured":"Duan, M., Chaolin, T.: Realization of clustering algorithm based on density. J. Jishou Univ. (Nat. Sci. Edn.) 34(1), 26\u201327 (2013)","journal-title":"J. Jishou Univ. (Nat. Sci. Edn.)"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Fu, P., Hu, X.: Biased-sampling of density-based local outlier detection algorithm, pp. 1246\u20131253 (2016)","DOI":"10.1109\/FSKD.2016.7603357"},{"key":"18_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-30952-7_1","volume-title":"Web Information Systems and Applications","author":"M Gao","year":"2019","unstructured":"Gao, M., et al.: Online anomaly detection via incremental tensor decomposition. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 3\u201314. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30952-7_1"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Huang, T., et al.: An LOF-based adaptive anomaly detection scheme for cloud computing, pp. 206\u2013211 (2013)","DOI":"10.1109\/COMPSACW.2013.28"},{"issue":"011","key":"18_CR8","first-page":"1301","volume":"9","author":"Q Liu","year":"2015","unstructured":"Liu, Q., Wang, K., Rao, W.: Non-equal time series clustering algorithm with sliding window STS distance. J. Front. Comput. Sci. Technol. 9(011), 1301\u20131313 (2015)","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Susto, G.A., Beghi, A., Mcloone, S.: Anomaly detection through on-line isolation forest: an application to plasma etching, pp. 89\u201394 (2017)","DOI":"10.1109\/ASMC.2017.7969205"},{"issue":"4","key":"18_CR10","first-page":"356","volume":"10","author":"CD Truong","year":"2015","unstructured":"Truong, C.D., Anh, D.T.: An efficient method for motif and anomaly detection in time series based on clustering. Int. J. Bus. Intell. Data Min. 10(4), 356\u2013377 (2015)","journal-title":"Int. J. Bus. Intell. Data Min."},{"issue":"16","key":"18_CR11","doi-asserted-by":"publisher","first-page":"16911","DOI":"10.1007\/s11042-016-3638-1","volume":"76","author":"C Yin","year":"2016","unstructured":"Yin, C., Zhang, S.: Parallel implementing improved k-means applied for image retrieval and anomaly detection. Multimed. Tools Appl. 76(16), 16911\u201316927 (2016). https:\/\/doi.org\/10.1007\/s11042-016-3638-1","journal-title":"Multimed. Tools Appl."},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ijepes.2018.03.025","volume":"101","author":"S Yip","year":"2018","unstructured":"Yip, S., Tan, W., Tan, C., Gan, M., Wong, K.: An anomaly detection framework for identifying energy theft and defective meters in smart grids. Int. J. Electr. Power Energy Syst. 101, 189\u2013203 (2018)","journal-title":"Int. J. Electr. Power Energy Syst."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60029-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:41:26Z","timestamp":1709811686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60029-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030600280","9783030600297"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60029-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa22020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wisa.pmease.cn\/wisa2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CCF Consys","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"165","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7.2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}