{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:50:54Z","timestamp":1742968254042,"version":"3.40.3"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031436802"},{"type":"electronic","value":"9783031436819"}],"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\/978-3-031-43681-9_10","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T06:02:16Z","timestamp":1694844136000},"page":"170-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robustness Verification of\u00a0Deep Neural Networks Using Star-Based Reachability Analysis with\u00a0Variable-Length Time Series Input"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5978-8168","authenticated-orcid":false,"given":"Neelanjana","family":"Pal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0721-1241","authenticated-orcid":false,"given":"Diego Manzanas","family":"Lopez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-9923","authenticated-orcid":false,"given":"Taylor T","family":"Johnson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"10_CR1","unstructured":"Predict battery state of charge using deep learning - MATLAB & ; Simulink \u2013 mathworks.com. https:\/\/www.mathworks.com\/help\/deeplearning\/ug\/predict-soc-using-deep-learning.html"},{"key":"10_CR2","unstructured":"Prognostics center of excellence - data repository. https:\/\/ti.arc.nasa.gov\/tech\/dash\/groups\/pcoe\/prognostic-data-repository\/#turbofan"},{"key":"10_CR3","unstructured":"Remaining useful life estimation using convolutional neural network - MATLAB & ; Simulink \u2013 mathworks.com. https:\/\/www.mathworks.com\/help\/predmaint\/ug\/remaining-useful-life-estimation-using-convolutional-neural-network.html"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Anderson, G., Pailoor, S., Dillig, I., Chaudhuri, S.: Optimization and abstraction: a synergistic approach for analyzing neural network robustness. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 731\u2013744 (2019)","DOI":"10.1145\/3314221.3314614"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bogomolov, S., Forets, M., Frehse, G., Potomkin, K., Schilling, C.: JuliaReach: a toolbox for set-based reachability. In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pp. 39\u201344 (2019)","DOI":"10.1145\/3302504.3311804"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Borgi, T., Hidri, A., Neef, B., Naceur, M.S.: Data analytics for predictive maintenance of industrial robots. In: 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 412\u2013417. IEEE (2017)","DOI":"10.1109\/ASET.2017.7983729"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A., Misener, R.: Efficient verification of ReLU-based neural networks via dependency analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3291\u20133299 (2020)","DOI":"10.1609\/aaai.v34i04.5729"},{"key":"10_CR8","unstructured":"DeLillo, D.: White noise. Penguin (1999)"},{"key":"10_CR9","unstructured":"EASA, Aerospace, C.: Formal methods use for learning assurance (formula). Tech. Rep. (2023)"},{"issue":"2","key":"10_CR10","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1086\/259002","volume":"73","author":"CE Ferguson","year":"1965","unstructured":"Ferguson, C.E.: Time-series production functions and technological progress in American manufacturing industry. J. Polit. Econ. 73(2), 135\u2013147 (1965)","journal-title":"J. Polit. Econ."},{"key":"10_CR11","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"10_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-030-25540-4_26","volume-title":"Computer Aided Verification","author":"G Katz","year":"2019","unstructured":"Katz, G., et al.: The marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 443\u2013452. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_26"},{"key":"10_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2020.101876","volume":"113","author":"S Kauffman","year":"2021","unstructured":"Kauffman, S., Dunne, M., Gracioli, G., Khan, W., Benann, N., Fischmeister, S.: Palisade: a framework for anomaly detection in embedded systems. J. Syst. Archit. 113, 101876 (2021)","journal-title":"J. Syst. Archit."},{"key":"10_CR14","first-page":"2020","volume":"3","author":"P Kollmeyer","year":"2020","unstructured":"Kollmeyer, P., Vidal, C., Naguib, M., Skells, M.: LG 18650hg2 Li-ion battery data and example deep neural network xEV SOC estimator script. Mendeley Data 3, 2020 (2020)","journal-title":"Mendeley Data"},{"key":"10_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"issue":"1","key":"10_CR16","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/72.554195","volume":"8","author":"S Lawrence","year":"1997","unstructured":"Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98\u2013113 (1997)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"11","key":"10_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"3","key":"10_CR18","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1109\/LRA.2019.2918684","volume":"4","author":"CY Lin","year":"2019","unstructured":"Lin, C.Y., Hsieh, Y.M., Cheng, F.T., Huang, H.C., Adnan, M.: Time series prediction algorithm for intelligent predictive maintenance. IEEE Robot. Autom. Lett. 4(3), 2807\u20132814 (2019)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR19","doi-asserted-by":"publisher","unstructured":"Lopez, D.M., Choi, S.W., Tran, H.D., Johnson, T.T.: NNV 2.0: the neural network verification tool. In: International Conference on Computer Aided Verification, pp. 397\u2013412. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-37703-7_19","DOI":"10.1007\/978-3-031-37703-7_19"},{"issue":"9","key":"10_CR20","doi-asserted-by":"publisher","DOI":"10.1002\/cem.2912","volume":"31","author":"F Lv","year":"2017","unstructured":"Lv, F., Wen, C., Liu, M., Bao, Z.: Weighted time series fault diagnosis based on a stacked sparse autoencoder. J. Chemometr. 31(9), e2912 (2017)","journal-title":"J. Chemometr."},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Martinez, C.M., Cao, D.: iHorizon-Enabled energy management for electrified vehicles. Butterworth-Heinemann (2018)","DOI":"10.1016\/B978-0-12-815010-8.00002-8"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Mohapatra, J., Weng, T.W., Chen, P.Y., Liu, S., Daniel, L.: Towards verifying robustness of neural networks against a family of semantic perturbations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 244\u2013252 (2020)","DOI":"10.1109\/CVPR42600.2020.00032"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"10_CR24","unstructured":"M\u00fcller, M.N., Brix, C., Bak, S., Liu, C., Johnson, T.T.: The third international verification of neural networks competition (VNN-comp 2022): summary and results. arXiv preprint arXiv:2212.10376 (2022)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Pal, N., Lopez, D.M., Johnson, T.T.: Robustness verification of deep neural networks using star-based reachability analysis with variable-length time series input. arXiv preprint arXiv:2307.13907 (2023)","DOI":"10.1007\/978-3-031-43681-9_10"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science 2(11), 559\u2013572 (1901)","DOI":"10.1080\/14786440109462720"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Priemer, R.: Introductory signal processing, vol. 6. World Scientific (1991)","DOI":"10.1142\/0864"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Priemer, R.: Signals and signal processing. Introductory Signal Processing, pp. 1\u20139 (1991)","DOI":"10.1142\/9789814434409_0001"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"de Riberolles, T., Zou, Y., Silvestre, G., Lochin, E., Song, J.: Anomaly detection for ICS based on deep learning: a use case for aeronautical radar data. Ann. Telecommun., pp. 1\u201313 (2022)","DOI":"10.1007\/s12243-021-00902-7"},{"key":"10_CR30","unstructured":"Saxena, A., Goebel, K.: Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository, pp. 1551\u20133203 (2008)"},{"issue":"1","key":"10_CR31","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1111\/1468-2354.00061","volume":"41","author":"IM Semenick Alam","year":"2000","unstructured":"Semenick Alam, I.M., Sickles, R.C.: Time series analysis of deregulatory dynamics and technical efficiency: the case of the us airline industry. Int. Econ. Rev. 41(1), 203\u2013218 (2000)","journal-title":"Int. Econ. Rev."},{"key":"10_CR32","first-page":"11936","volume":"33","author":"A Sivaraman","year":"2020","unstructured":"Sivaraman, A., Farnadi, G., Millstein, T., Van den Broeck, G.: Counterexample-guided learning of monotonic neural networks. Adv. Neural. Inf. Process. Syst. 33, 11936\u201311948 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"10_CR33","volume":"9","author":"K Soomro","year":"2019","unstructured":"Soomro, K., Bhutta, M.N.M., Khan, Z., Tahir, M.A.: Smart city big data analytics: an advanced review. Wiley Interdisc. Rev.: Data Min. Knowl. Disc. 9(5), e1319 (2019)","journal-title":"Wiley Interdisc. Rev.: Data Min. Knowl. Disc."},{"issue":"20","key":"10_CR34","doi-asserted-by":"publisher","first-page":"8460","DOI":"10.3390\/su12208460","volume":"12","author":"J St\u00fcbinger","year":"2020","unstructured":"St\u00fcbinger, J., Schneider, L.: Understanding smart city-a data-driven literature review. Sustainability 12(20), 8460 (2020)","journal-title":"Sustainability"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Susto, G.A., Beghi, A.: Dealing with time-series data in predictive maintenance problems. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1\u20134. IEEE (2016)","DOI":"10.1109\/ETFA.2016.7733659"},{"key":"10_CR36","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"issue":"2","key":"10_CR37","first-page":"101","volume":"15","author":"G Touloumi","year":"2004","unstructured":"Touloumi, G., et al.: Analysis of health outcome time series data in epidemiological studies. Environ.: Official J. Int. Environ. Soc. 15(2), 101\u2013117 (2004)","journal-title":"Environ.: Official J. Int. Environ. Soc."},{"key":"10_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-53288-8_2","volume-title":"Computer Aided Verification","author":"H-D Tran","year":"2020","unstructured":"Tran, H.-D., Bak, S., Xiang, W., Johnson, T.T.: Verification of deep convolutional neural networks using ImageStars. In: Lahiri, S.K., Wang, C. (eds.) CAV 2020. LNCS, vol. 12224, pp. 18\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53288-8_2"},{"key":"10_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/978-3-030-30942-8_39","volume-title":"Formal Methods \u2013 The Next 30 Years","author":"H-D Tran","year":"2019","unstructured":"Tran, H.-D., et al.: Star-based reachability analysis of deep neural networks. In: ter Beek, M.H., McIver, A., Oliveira, J.N. (eds.) FM 2019. Star-based reachability analysis of deep neural networks., vol. 11800, pp. 670\u2013686. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30942-8_39"},{"key":"10_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/978-3-030-30942-8_39","volume-title":"Formal Methods \u2013 The Next 30 Years","author":"H-D Tran","year":"2019","unstructured":"Tran, H.-D., et al.: Star-based reachability analysis of deep neural networks. In: ter Beek, M.H., McIver, A., Oliveira, J.N. (eds.) FM 2019. LNCS, vol. 11800, pp. 670\u2013686. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30942-8_39"},{"key":"10_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-81685-8_12","volume-title":"Computer Aided Verification","author":"H-D Tran","year":"2021","unstructured":"Tran, H.-D., et al.: Robustness verification of semantic segmentation neural networks using relaxed reachability. In: Silva, A., Leino, K.R.M. (eds.) CAV 2021. LNCS, vol. 12759, pp. 263\u2013286. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81685-8_12"},{"key":"10_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-53288-8_1","volume-title":"Computer Aided Verification","author":"H-D Tran","year":"2020","unstructured":"Tran, H.-D., et al.: NNV: the neural network verification tool for deep neural networks and learning-enabled cyber-physical systems. In: Lahiri, S.K., Wang, C. (eds.) CAV 2020. LNCS, vol. 12224, pp. 3\u201317. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53288-8_1"},{"key":"10_CR43","unstructured":"Truax, B.: Handbook for acoustic ecology. Cambridge Street Records (1999)"},{"key":"10_CR44","unstructured":"Wang, Z., Wang, Y., Fu, F., Jiao, R., Huang, C., Li, W., Zhu, Q.: A tool for neural network global robustness certification and training. arXiv preprint arXiv:2208.07289 (2022)"},{"key":"10_CR45","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1146\/annurev.publhealth.26.021304.144517","volume":"27","author":"SL Zeger","year":"2006","unstructured":"Zeger, S.L., Irizarry, R., Peng, R.D.: On time series analysis of public health and biomedical data. Annu. Rev. Public Health 27, 57\u201379 (2006)","journal-title":"Annu. Rev. Public Health"},{"key":"10_CR46","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jprocont.2021.03.004","volume":"102","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Lai, X., Wu, M., Chen, L., Lu, C., Du, S.: Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process. J. Process Control 102, 24\u201333 (2021)","journal-title":"J. Process Control"}],"container-title":["Lecture Notes in Computer Science","Formal Methods for Industrial Critical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43681-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T09:05:56Z","timestamp":1710839156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43681-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031436802","9783031436819"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43681-9_10","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":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FMICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Formal Methods for Industrial Critical Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Antwerp","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","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":"20 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fmics2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.uantwerpen.be\/en\/conferences\/confest-2023\/fmics\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"24","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":"14","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":"58% - 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","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":"2,5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}