{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:48:02Z","timestamp":1742957282535,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030788100"},{"type":"electronic","value":"9783030788117"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78811-7_31","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:22:37Z","timestamp":1625613757000},"page":"321-330","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sequential Stacked AutoEncoder-Based Artificial Neural Network and Improved Sheep Optimization for Tool Wear Prediction"],"prefix":"10.1007","author":[{"given":"Fei","family":"Ding","sequence":"first","affiliation":[]},{"given":"Mingyan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Dongfeng","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Falei","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Haiyan","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","volume":"98","author":"A Fatemeh","year":"2018","unstructured":"Fatemeh, A., Antoine, T., Marc, T.: Tool condition monitoring using spectral sub traction and convolutional neural networks in milling process. Int. J. Adv. Manuf. Technol. 98, 3217\u20133227 (2018)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yao, X., Zhang, J., Jin, H.: Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors (Basel, Switzerland) 16(6), 795 (2016)","DOI":"10.3390\/s16060795"},{"issue":"1","key":"31_CR3","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2174\/1872212114999200423115526","volume":"15","author":"D Du","year":"2021","unstructured":"Du, D., Zhang, J., Si, X., Hu, C.: Remaining useful life estimation: A review on stochastic process-based approaches. Recent Pat. Eng. 15(1), 69\u201376 (2021)","journal-title":"Recent Pat. Eng."},{"issue":"3","key":"31_CR4","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s00170-020-05449-w","volume":"109","author":"G Serin","year":"2020","unstructured":"Serin, G., Sener, B., Ozbayoglu, A.M., Unver, H.O.: Review of tool condition monitoring in machining and opportunities for deep learning. Int. J. Adv. Manuf. Technol. 109(3), 953\u2013974 (2020). https:\/\/doi.org\/10.1007\/s00170-020-05449-w","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"31_CR5","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ijfatigue.2013.02.002","volume":"58","author":"N Shamsaei","year":"2014","unstructured":"Shamsaei, N., Fatemi, A.: Small fatigue crack growth under multiaxial stresses. Int. J. Fatigue 58(1), 126\u2013135 (2014)","journal-title":"Int. J. Fatigue"},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.procir.2018.08.253","volume":"77","author":"A Gouarir","year":"2018","unstructured":"Gouarir, A., Mart\u0131nez-Arellano, G., Terrazas, G., Benardos, P., Ratchev, S.: Inprocess tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP 77, 501\u2013504 (2018)","journal-title":"Procedia CIRP"},{"issue":"1","key":"31_CR7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10845-016-1235-9","volume":"30","author":"G Wang","year":"2016","unstructured":"Wang, G., Zhang, Y., Liu, C., Xie, Q., Xu, Y.: A new tool wear monitoring method based on multi-scale PCA. J. Intell. Manuf. 30(1), 113\u2013122 (2016). https:\/\/doi.org\/10.1007\/s10845-016-1235-9","journal-title":"J. Intell. Manuf."},{"key":"31_CR8","doi-asserted-by":"publisher","unstructured":"Novak, E., Ritter, K.: The Curse of Dimension and a Universal Method For Numerical Integration. In: N\u00fcrnberger, G., Schmidt, J.W., Walz, G. (eds) Multivariate Approximation and Splines. ISNM International Series of Numerical Mathematics, vol. 125, pp 177-187. Birkh\u00e4user, Basel (1997). https:\/\/doi.org\/10.1007\/978-3-0348-8871-4_15","DOI":"10.1007\/978-3-0348-8871-4_15"},{"issue":"1","key":"31_CR9","first-page":"377","volume":"130","author":"L Chen","year":"2017","unstructured":"Chen, L., Wang, Z.Y., Qin, W.L., Ma, J.: Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Sig. Processing 130(1), 377\u2013388 (2017)","journal-title":"Sig. Processing"},{"issue":"99","key":"31_CR10","first-page":"185","volume":"67","author":"J Sun","year":"2017","unstructured":"Sun, J., Yan, C., Wen, J.: Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning. IEEE Trans. Inst. Measur. 67(99), 185\u2013195 (2017)","journal-title":"IEEE Trans. Inst. Measur."},{"key":"31_CR11","unstructured":"Arpit, D., Zhou, Y., Kota, B., Govindaraju, V.: Normalization propagation: A parametric technique for removing internal covariate shift in deep networks. In: International Conference on Machine Learning, pp. 1168\u20131176. PMLR (2016)"},{"issue":"4","key":"31_CR12","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2(4), 303\u2013314 (1989). https:\/\/doi.org\/10.1007\/BF02551274","journal-title":"Mathematics of Control, Signals and Systems"},{"issue":"2","key":"31_CR13","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251\u2013257 (1991)","journal-title":"Neural Netw."},{"issue":"6","key":"31_CR14","first-page":"1300","volume":"46","author":"D Qu","year":"2018","unstructured":"Qu, D., Xu, L., Lu, Y., Yuan, X., Huang, M., Wang, X.: A new swarm intelligence algorithm for simulating herd behavior. Acta Electron. Sinica 46(6), 1300\u20131305 (2018)","journal-title":"Acta Electron. Sinica"},{"key":"31_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.rcim.2016.05.010","volume":"45","author":"J Wang","year":"2017","unstructured":"Wang, J., Xie, J., Zhao, R., Zhang, L., Duan, L.: Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robot. Comput. Integr. Manuf. 45, 47\u201358 (2017)","journal-title":"Robot. Comput. Integr. Manuf."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78811-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:24:40Z","timestamp":1625613880000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78811-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030788100","9783030788117"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78811-7_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","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":"104","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":"0","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":"59% - 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":"2,5","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":"4-5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}