{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:32:29Z","timestamp":1743096749617,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466601"},{"type":"electronic","value":"9783031466618"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46661-8_43","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"646-660","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer Based Driving Behavior Safety Prediction for\u00a0New Energy Vehicles"],"prefix":"10.1007","author":[{"given":"Hao","family":"Lin","sequence":"first","affiliation":[]},{"given":"Junjie","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"issue":"6","key":"43_CR1","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.pmcj.2009.07.007","volume":"5","author":"J Liua","year":"2009","unstructured":"Liua, J., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uwave: accelerometer-based personalized gesture recognition and its applications. Pervas. Mobile Comput. 5(6), 657\u2013675 (2009)","journal-title":"Pervas. Mobile Comput."},{"key":"43_CR2","first-page":"199","volume":"2","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests, machine learning 45. J. Clin. Microbiol. 2, 199\u2013228 (2001)","journal-title":"J. Clin. Microbiol."},{"key":"43_CR3","unstructured":"Brush, A., Krumm, J., Scott, J., Saponas, S.: Recognizing activities from mobile sensor data: challenges and opportunities (2011)"},{"key":"43_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"43_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Hu, A., Jiang, Y.: Radio frequency fingerprint-based dsrc intelligent vehicle networking identification mechanism in high mobility environment. Sustainability 14 (2022)","DOI":"10.3390\/su14095037"},{"issue":"3","key":"43_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"5","key":"43_CR7","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34(5), 1454\u20131495 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"43_CR8","unstructured":"Devi, U.S., Harsha, K.S.S., Rao, B.S.: Abnormal driving behaviors detection with smart phones (2018)"},{"key":"43_CR9","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"43_CR10","doi-asserted-by":"crossref","unstructured":"Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. Elsevier (2015)","DOI":"10.21236\/ADA623249"},{"key":"43_CR11","unstructured":"Fortuin, V., H\u00fcser, M., Locatello, F., Strathmann, H., R\u00e4tsch, G.: Deep self-organization: Interpretable discrete representation learning on time series, arXiv preprint arXiv:1806.02199 (2018)"},{"issue":"5","key":"43_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182382","volume":"12","author":"L Jason","year":"2018","unstructured":"Jason, L., Sarah, T., Anthony, B.: Time series classification with hive-cote. ACM Trans. Knowl. Discov. Data 12(5), 1\u201335 (2018)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"43_CR13","doi-asserted-by":"crossref","unstructured":"Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate lstm-fcns for time series classification. Neural Netw. 116 (2018)","DOI":"10.1016\/j.neunet.2019.04.014"},{"issue":"6","key":"43_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009086","volume":"17","author":"A Kopf","year":"2021","unstructured":"Kopf, A., Fortuin, V., Somnath, V.R., Claassen, M.: Mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations on single cell data. PLoS Comput. Biol. 17(6), e1009086 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"43_CR15","unstructured":"Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, 32 (2019)"},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Lim, B., Ark, S., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. International J. Forecast. (1) (2021)","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"43_CR17","unstructured":"Liu, L.: On the variance of the adaptive learning rate and beyond. In: International Conference on Learning Representations (2020)"},{"key":"43_CR18","unstructured":"Lyu, X., Hueser, M., Hyland, S.L., Zerveas, G., Raetsch, G.: Improving clinical predictions through unsupervised time series representation learning. arXiv preprint arXiv:1812.00490 (2018)"},{"key":"43_CR19","unstructured":"J. Ma, Z., Shou, A., Zareian, H., Mansour, A.V., Chang S.-F.: Cdsa: cross-dimensional self-attention for multivariate, geo-tagged time series imputation. arXiv preprint arXiv:1905.09904 (2019)"},{"key":"43_CR20","unstructured":"Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017)"},{"key":"43_CR21","doi-asserted-by":"crossref","unstructured":"Nawaz, S., Mascolo, C.: Mining users\u2019 significant driving routes with low-power sensors (2014)","DOI":"10.1145\/2668332.2668348"},{"key":"43_CR22","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"F Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F., Roggen, D.: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 115 (2016)","journal-title":"Sensors"},{"key":"43_CR23","doi-asserted-by":"crossref","unstructured":"Ouyang, Z., Niu, J., Guizani, M.: Improved vehicle steering pattern recognition by using selected sensor data. IEEE Trans. Mobile Comput. (2018)","DOI":"10.1109\/TMC.2017.2762679"},{"key":"43_CR24","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. MIT Press (2015)"},{"issue":"3","key":"43_CR25","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s10618-020-00679-8","volume":"34","author":"A Shifaz","year":"2020","unstructured":"Shifaz, A., Pelletier, C., Petitjean, F., Webb, G.I.: Ts-chief: a scalable and accurate forest algorithm for time series classification. Data Min. Knowl. Disc. 34(3), 742\u2013775 (2020)","journal-title":"Data Min. Knowl. Disc."},{"key":"43_CR26","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.)editors, (n: Advances in Neural Information Processing Systems, vol. 30. Curran Associates Inc (2017)"},{"key":"43_CR27","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: A neural image caption generator. IEEE (2015)","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"43_CR28","unstructured":"Yan, W., Jie, Y., Liu, H., Chen, Y., Martin, R.P.: Sensing vehicle dynamics for determining driver phone use. In: Proceeding of the 11th Annual International Conference on mobile systems, applications, and services (2013)"},{"key":"43_CR29","unstructured":"Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., Shonali, P.K.: Deep convolutional neural networks on multichannel time series for human activity recognition. In Proc, IJCAI (2015)"},{"key":"43_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-319-08010-9_33","volume-title":"Web-Age Information Management","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298\u2013310. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08010-9_33"},{"key":"43_CR31","series-title":"SpringerBriefs in Electrical and Computer Engineering","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-89770-7","volume-title":"Sensing Vehicle Conditions for Detecting Driving Behaviors","author":"J Yu","year":"2018","unstructured":"Yu, J., Chen, Y., Xu, X.: Sensing Vehicle Conditions for Detecting Driving Behaviors. SECE, Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-89770-7"},{"key":"43_CR32","doi-asserted-by":"crossref","unstructured":"Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114\u20132124 (2021)","DOI":"10.1145\/3447548.3467401"},{"key":"43_CR33","doi-asserted-by":"crossref","unstructured":"X. Zhang, Y. Gao, J. Lin, and C. T. Lu. Tapnet: Multivariate time series classification with attentional prototypical network. pages 6845\u20136852, 2020","DOI":"10.1609\/aaai.v34i04.6165"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46661-8_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:08:07Z","timestamp":1699103287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46661-8_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466601","9783031466618"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46661-8_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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)"}}]}}