MAADISON - Modèles par apprentissage automatisés pour la surveillance des ouvrages névralgiques | FEREC 2020
55 VI. Références VI.A. Références sur le sujet IA / outils de modélisation [1] Florent Altché et Arnaud de La Fortelle. “An LSTM network for highway trajectory prediction”. In : 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) . IEEE. 2017, p. 353-359. [2] Alexander Amini. Introduction to Deep Learning . Massachusetts Institute of Technology. 2021. [3] R Artusi, P Verderio et EJTIjobm Marubini. “ Bravais-Pearson and Spearman correlation coefficients : meaning, test of hypothesis and confidence interval”. In : The International journal of biological markers 17.2 (2002), p. 148-151. [4] A Bagnall et al. “The great time series classification bake off : An experimental evaluation of recently proposed algorithms. Extended version. arXiv 2016”. In : arXiv preprint arXiv :1602.01711 (). [5] Anthony Bagnall et al. “Time -series classification with COTE : the collective of transformationbased ensembles”. In : IEEE Transactions on Knowledge and Data Engineering 27.9 (2015), p. 2522-2535. [6] Dzmitry Bahdanau, Kyunghyun Cho et Yoshua Bengio. “Neural machine translation by jointly learning to align and translate”. In : arXiv preprint arXiv :1409.0473 (2014). [7] Yu equan Bao et al. “Computer vision and deep learning– based data anomaly detection method for structural health monitoring”. In : Structural Health Monitoring 18.2 (2019), p. 401-421. [8] Luc Baudoux. Traitement d’images avancé et télédétection : Cours de Support Vector Machine (SVM) et sélection d’attributs . ENSG-IGN. 2019. [9] Ghassane Benrhmach et al. “Nonlinear autoregressive neural network and extended Kalman filters for prediction of financial time series”. In : Journal of Applied Mathematics 2020 (2020). [10] Houtao Deng et al. “A time series forest for classification and feature extraction”. In : Information Sciences 239 (2013), p. 142-153. [11] Ndeye Diop-Mar. Développement d’indicateurs d’anormalité par appren tissage automatisé pour la surveillance d es ouvrages d’art . Universiyé Gustave Eiffel. 2021. [12] Eloi Figueiredo et al. “Machine learning algorithms for damage detection under operational and environmental variability”. In : Structural Health Monitoring 10.6 (2011), p. 559-572. [13] Rafaelle Pia zzaroli Finotti, Alexandre Abrahão Cury et Flávio de Souza Barbosa. “An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements”. In : Latin American Journal of Solids and Structures 16 (2019). [14] Vivien Sainte Fare Garnot. Deep Learning, an introduction . ENSG-IGN. 2021. [15] Mosbeh R Kaloop et Hui Li. “Multi input– single output models identification of tower bridge movements using GPS monitoring system”. In : Measurement 47 (2014), p. 531-539. [16] Jan Koutn ik et al. “A clockwork rnn”. In : International Conference on Machine Learning . PMLR. 2014, p. 1863-1871. [17] Bibin Kurian et Ranjith Liyanapathirana. “Machine learning techniques for structural health monitoring”. In : Proceedings of the 13th International Conference on Damage Assessment of Structures . Springer. 2020, p. 3-24. [18] Dominique Ladiray. Classification de séries temporelles : Applications à la prévision et la désaisonnalisation . Institut national de la statistique et des études économiques (INSEE). 2009. [19] Zachary C Lipton, John Berkowitz et Charles Elkan. “A critical review of recurrent neural networks for sequence learning”. In : arXiv preprint arXiv :1506.00019 (2015).
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