{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T04:46:07Z","timestamp":1726029967056},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030209117"},{"type":"electronic","value":"9783030209124"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-20912-4_59","type":"book-chapter","created":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T19:02:29Z","timestamp":1558897349000},"page":"652-662","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of Large Scale Power Systems via LASSO Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Miros\u0142aw","family":"Pawlak","sequence":"first","affiliation":[]},{"given":"Jiaqing","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"59_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5451-6","volume-title":"Automatic Learning Techniques in Power Systems","author":"LA Wehenkel","year":"1998","unstructured":"Wehenkel, L.A.: Automatic Learning Techniques in Power Systems. Kluwer, Boston (1998)"},{"key":"59_CR2","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TPWRS.2008.926708","volume":"23","author":"BA Archer","year":"2008","unstructured":"Archer, B.A., Annakkage, U.D., Jayasekara, B., Wijetunge, P.: Accurate prediction of damping in large interconnected power systems with the aid of regression analysis. IEEE Trans. Power Syst. 23, 1170\u20131178 (2008)","journal-title":"IEEE Trans. Power Syst."},{"key":"59_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TPWRS.2010.2050344","volume":"26","author":"VJ Gutierrez-Martinez","year":"2011","unstructured":"Gutierrez-Martinez, V.J., Ca\u00f1izares, C.A., Fuerte-Esquivel, C.R., Pizano-Martinez, A., Gu, X.: Neural-network security-boundary constrained optimal power flow. IEEE Trans. Power Syst. 26, 63\u201372 (2011)","journal-title":"IEEE Trans. Power Syst."},{"key":"59_CR4","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TPWRS.2004.826018","volume":"19","author":"LS Moulin","year":"2004","unstructured":"Moulin, L.S., da Silva, A.P.A., El-Sharkawi, M.A., Marks II, R.J.: Support vector nachines for transient stability analysis of large-scale power systems. IEEE Trans. Power Syst. 19, 818\u2013825 (2004)","journal-title":"IEEE Trans. Power Syst."},{"key":"59_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009). \n                      https:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"59_CR6","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TPWRS.2006.881111","volume":"41","author":"B Jayasekara","year":"2006","unstructured":"Jayasekara, B., Annakkage, U.: Deviation of an accurate polynomial representation of the transient stability boundary. IEEE Trans. Power Syst. 41, 1856\u20131863 (2006)","journal-title":"IEEE Trans. Power Syst."},{"key":"59_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20192-9","volume-title":"Statistics for High-Dimensional Data: Methods, Theory and Applications","author":"P B\u00fchlmann","year":"2011","unstructured":"B\u00fchlmann, P., Van De Geer, S.: Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer, Heidelberg (2011). \n                      https:\/\/doi.org\/10.1007\/978-3-642-20192-9"},{"key":"59_CR8","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou, H.: The adaptive LASSO and its oracle properties. J. Am. Stat. Assoc. 101, 1418\u20131429 (2006)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"59_CR9","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1214\/07-AOAS131","volume":"1","author":"J Friedman","year":"2007","unstructured":"Friedman, J., Hastie, T., Hofling, H., Tibshirani, R.: Pathwise coordinate optimization. Ann. Appl. Stat. 1(2), 302\u2013332 (2007)","journal-title":"Ann. Appl. Stat."},{"key":"59_CR10","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TPWRS.2012.2197763","volume":"28","author":"J Lv","year":"2013","unstructured":"Lv, J., Pawlak, M., Annakkage, U.D.: Prediction of the transient stability boundary using the LASSO. IEEE Trans. Power Eng. 28, 281\u2013288 (2013)","journal-title":"IEEE Trans. Power Eng."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20912-4_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T19:20:08Z","timestamp":1558898408000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20912-4_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030209117","9783030209124"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20912-4_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaisc.eu\/About","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"}},{"value":"Own online software","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"333","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"122","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"37% - 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"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}