Emmanuel Ramasso
Centre national de la recherche scientifique
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Featured researches published by Emmanuel Ramasso.
IEEE Transactions on Knowledge and Data Engineering | 2012
Costas Panagiotakis; Nikos Pelekis; Ioannis Kopanakis; Emmanuel Ramasso; Yannis Theodoridis
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
IEEE Transactions on Systems, Man, and Cybernetics | 2013
Emmanuel Ramasso; Michèle Rombaut; Noureddine Zerhouni
Forecasting the future states of a complex system is a complicated challenge that is encountered in many industrial applications covered in the community of prognostics and health management. Practically, states can be either continuous or discrete: Continuous states generally represent the value of a signal while discrete states generally depict functioning modes reflecting the current degradation. For each case, specific techniques exist. In this paper, we propose an approach based on case-based reasoning that jointly estimates the future values of the continuous signal and the future discrete modes. The main characteristics of the proposed approach are the following: 1) It relies on the K-nearest neighbor algorithm based on belief function theory; 2) belief functions allow the user to represent his/her partial knowledge concerning the possible states in the training data set, particularly concerning transitions between functioning modes which are imprecisely known; and 3) two distinct strategies are proposed for state prediction, and the fusion of both strategies is also considered. Two real data sets were used in order to assess the performance in estimating future breakdown of a real system.
IEEE Transactions on Fuzzy Systems | 2014
Emmanuel Ramasso; Thierry Denoeux
This paper addresses the problem of parameter estimation and state prediction in hidden Markov models (HMMs) based on observed outputs and partial knowledge of hidden states expressed in the belief function framework. The usual HMM model is recovered when the belief functions are vacuous. Parameters are learned using the evidential expectation-maximization algorithm, a recently introduced variant of the expectation-maximization algorithm for maximum likelihood estimation based on uncertain data. The inference problem, i.e., finding the most probable sequence of states based on observed outputs and partial knowledge of states, is also addressed. Experimental results demonstrate that partial information about hidden states, when available, may substantially improve the estimation and prediction performances.
prognostics and system health management conference | 2010
Emmanuel Ramasso; Rafael Gouriveau
Condition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations.
international workshop on machine learning for signal processing | 2009
Emmanuel Ramasso
Evidence-theoretic propagations of temporal belief functions are proposed to deal with possibly dependent observations and for partially supervised learning of HMM. Solutions are formulated in Transferable Belief Model framework and experiments concern a diagnosis problem.
IEEE Transactions on Circuits and Systems for Video Technology | 2007
Emmanuel Ramasso; Michèle Rombaut; Denis Pellerin
In this paper, we propose a tool called temporal credal filter with conflict-based model change (TCF-CMC) to smooth belief functions online in transferable belief model (TBM) framework. The TCF-CMC takes temporal aspects of belief functions into account and relies on conflict information explicitly modelled in TBM when combining beliefs. TBM fusion, in addition to uncertainty, takes into account imprecision and conflict inherent to features. The TCF-CMC takes part in a wider system for human action recognition in videos. The whole system is tested on 62 videos (11000 images) with moving camera and real conditions where the TCF-CMC improves running, jumping, falling and standing-up actions recognition in high jump, pole vault, long jump and triple jump activities. The TCF-CMC is also compared to hidden Markov models. Lastly, a TBM rules-based modelling is compared to Gaussian mixture.
IEEE Transactions on Reliability | 2014
Emmanuel Ramasso; Rafael Gouriveau
Various approaches for prognostics have been developed, and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets to build a model of the degradation signal, and estimate the limit under which the degradation signal should stay. Applicability and accuracy of these methods are thereby closely related to the amount of available data, and even sometimes requires the user to make assumptions on the dynamics of health states evolution. Following that, the aim of this paper is to propose a method for prognostics and remaining useful life estimation that starts from scratch, without any prior knowledge. Assuming that remaining useful life can be seen as the time between the current time and the instant where the degradation is above an acceptable limit, the proposition is based on a classification of prediction strategy (CPS) that relies on two factors. First, it relies on the use of an evolving real-time neuro-fuzzy system that forecasts observations in time. Secondly, it relies on the use of an evidential Markovian classifier based on Dempster-Shafer theory that enables classifying observations into the possible functioning modes. This approach has the advantage to cope with a lack of data using an evolving system, and theory of belief functions. Also, one of the main assets is the possibility to train the prognostic system without setting any threshold. The whole proposition is illustrated and assessed by using the CMAPPS turbofan dataset. RUL estimates are shown to be very close to actual values, and the approach appears to accurately estimate the failure instants, even with few learning data.
international conference on acoustics, speech, and signal processing | 2006
Emmanuel Ramasso; Michèle Rombaut; Denis Pellerin
In the context of human action recognition in video sequences, a temporal belief filter based on the transferable belief model is proposed. It ensures a consistency in the temporal belief evolution. The filter is useful to cope with varying video quality and experiment conditions by smoothing belief on actions and solving conflict due to contradictory parameters. The proposed approach is validated on real video sequences with moving camera under several view angles
articulated motion and deformable objects | 2006
Costas Panagiotakis; Emmanuel Ramasso; Georgios Tziritas; Michèle Rombaut; Denis Pellerin
An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transferable Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump
ieee conference on prognostics and health management | 2011
Lisa Serir; Emmanuel Ramasso; Noureddine Zerhouni
Diagnostics and prognostics of health states are important activities in the maintenance process strategy of dynamical systems. Many approaches have been developed for this purpose and we particularly focus on data-driven methods which are increasingly applied due to the availability of various cheap sensors. Most data-driven methods proposed in the literature rely on probability density estimation. However, when the training data are limited, the estimated parameters are no longer reliable. This is particularly true for data in faulty states which are generally expensive and difficult to obtain. In order to solve this problem, we propose to use the theory of belief functions as described by Dempster, Shafer (Theory of Evidence) and Smets (Transferable Belief Model). A few methods based on belief functions have been proposed for diagnostics and prognostics of dynamical systems. Among these methods, Evidential Hidden Markov Models (EvHMM) seems promising and extends usual HMM to belief functions. Inference tools in EvHMM have already been developed, but parameter training has not fully been considered until now or only with strong assumptions. In this paper, we propose to complete the generalization of HMM to belief functions with a method for automatic parameter training. The generalization of this training procedure to more general Time-Sliced Temporal Evidential Network (TSTEN) is discussed paving the way for a further generalization of Dynamic Bayesian Network to belief functions with potential applications to diagnostics and prognostics. An application to time series classification is proposed.