1,721,026 research outputs found
Dynamic factor mixture of experts for functional time series modeling
No full textIEEE ICMLA'16 - IEEE International Conference on Machine Learning and Applications, Anaheim, ETATS-UNIS, 18-/12/2016 - 20/12/2016A new approach is introduced in this paper for dynamic modeling and dimensionality reduction from time series of curves. For this purpose, a dynamic mixture of expertsmodel whose regression coefficients evolve from curve to curve according to a Gaussian random walk over low dimensional factors, is proposed. The resulting model is neither else than a particular state-space model involving discrete and continuous latent variables, whose parameters are learned across a sequence of curves through a dedicated variational Expectation-Maximization algorithm. The experimental study conducted on simulated sequences of curves has shown the strong potential of the proposed approach
Segmental dynamic factor analysis for time series of curves
No full textInternational audienceA new approach is introduced in this article for describing and visualizing time series of curves, where each curve has the particularity of being subject to changes in regime. For this purpose, the curves are represented by a regression model including a latent segmentation, and their temporal evolution is modeled through a Gaussian random walk over low dimensional factors of the regression coefficients. The resulting model is neither else than a particular state-space model involving discrete and continuous latent variables, whose parameters are estimated across a sequence of curves through a dedicated variational Expectation-Maximization algorithm. The experimental study conducted on simulated data and real time series of curves has shown encouraging results in terms of visualization of their temporal evolution and forecasting
Dynamic factor analysis and predictive diagnosis of critical railway components
No full textWCCM 2017, First World Congress on Condition Monitoring, LONDRES, ROYAUME-UNI, 13-/06/2017 - 16/06/2017International audienceThe predictive analysis of the operating state of complex dynamic systems remains a challenge in numerous applications, particularly in the transport field. Its main objective is the estimation and prediction of the state of health of these systems from data usually available in the form of multivariate time series and emanating from multiple sensors. A classic approach to tackle this problem consists in assuming that the state of health switches between a finite set of operating states. In this case, supervised and unsupervised classification methods can be exploited, as well as finite state space dynamic models (eg. hidden Markov models). This contribution addresses the same issue, but from a different point of view: the unknown dynamics of the state of health is searched into a continuous low-dimensional space. The resulting model is a dynamic factor analytic model which can be seen as a specific state-space models. It can also be exploited for visualizing the evolution of the system's operating state over time. This article will describe the implementation of this model for the estimation, forecasting and visualization of the state of health of a specific railway component: the switch mechanism
Sequential variational learning of dynamic factor mixtures
No full textICDMW - 2018, IEEE International Conference on Data Mining Workshops, Singapour, SINGAPOUR, 17-/11/2018 - 20/11/2018The clustering of panel data remains a challenging problem, considering their dynamic and potentially massive nature. The massive aspect of panel data can be related to their number of observations and/or their high dimensionality. In this article, a new model and its estimation method are initiated to tackle these problems. The proposed model is a mixture distribution whose components are dynamic factor analyzers. The model inference, which cannot be performed exactly by classical methods, is realized in the sequential variational framework. In particular, it is established that the proposed algorithm converges in the sense of stochastic gradient algorithms toward an average lower variational bound. Experiments conducted on simulated data illustrate the good practical behavior of the method
Dynamic factor analysis and predictive diagnosis of critical railway components
No full textWCCM 2017, First World Congress on Condition Monitoring, LONDRES, ROYAUME-UNI, 13-/06/2017 - 16/06/2017International audienceThe predictive analysis of the operating state of complex dynamic systems remains a challenge in numerous applications, particularly in the transport field. Its main objective is the estimation and prediction of the state of health of these systems from data usually available in the form of multivariate time series and emanating from multiple sensors. A classic approach to tackle this problem consists in assuming that the state of health switches between a finite set of operating states. In this case, supervised and unsupervised classification methods can be exploited, as well as finite state space dynamic models (eg. hidden Markov models). This contribution addresses the same issue, but from a different point of view: the unknown dynamics of the state of health is searched into a continuous low-dimensional space. The resulting model is a dynamic factor analytic model which can be seen as a specific state-space models. It can also be exploited for visualizing the evolution of the system's operating state over time. This article will describe the implementation of this model for the estimation, forecasting and visualization of the state of health of a specific railway component: the switch mechanism
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A state-space approach to modeling functional time series: Application to rail supervision
No full textThis article introduces a state-space model for the dynamic modeling of curve sequences within the framework of railway switches online monitoring. In this context, each curve has the peculiarity of being subject to multiple changes in regime. The proposed model consists of a specific latent variable regression model whose coefficients are supposed to evolve dynamically in the course of time. Its parameters are recursively estimated across a sequence of curves through an online Expectation-Maximization (EM) algorithm. The experimental study conducted on two real power consumption curve sequences from the French high speed network has shown encouraging results
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Modèles de mélange et classification de données acoustiques en temps réel
No full textCette thèse, menée en collaboration avec le Centre Technique des Industries Mécaniques (CETIM), s'inscrit dans le cadre de la classification automatique pour le contrôle en temps réel par émission acoustique des équipements sous pression (citernes GPL .. .). Le travail effectué vise à améliorer un logiciel temps réel (LOTERE) d'aide à la décision dans le contrôle des équipements sous pression, jugé lent quand le nombre des émissions acoustiques à traiter devient très grand. Deux approches classificatoires basées sur le modèle de mélange de lois, capables de prendre en compte les contraintes de temps d'exécution, ont été développées. La première approche consiste à classifier les 'bins' résultant de la conversion des données initiales en un histogramme et la seconde consiste à classifier les données de façon séquentielle par mise à jour récurrente de la classification. Une étude expérimentale sur des données simulées et des données réelles a permis de mettre en évidence l'efficacité des approches proposées.The motivation for this Phd Thesis was a real-time flaw diagnosis application for pressurized containers using acoustic emissions. It has been carried out in collaboration with the Centre Technique des Industries Mécaniques (CETIM). The aim was to improve LOTERE, a real-time computer-aided-decision software, which has been found to be too slow when the number of acoustic emissions becomes large. Two mixture model-based clustering approaches, taking into account time constraints, have been proposed. The first one consists in clustering 'bins' resulting from the conversion of original observations into an histogram. The second one is an on-line approach updating recursively the classification. An experimental study using both simulated and real data has shown that the proposed methods are very efficient.COMPIEGNE-BU (601592101) / SudocSudocFranceF
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