Eric Lefèvre

International Journal of Approximate Reasoning, 2012

In this article, the contextual discounting of a belief function, a classical discounting general... more In this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family allowing one to strengthen a belief function.

Discountings of a belief function using a confusion matrix

by Eric Lefèvre, Zied Elouedi, and David Mercier

In this paper, we present an analysis of different approaches relative to the correction of belie... more

Diagnostic Based on High Frequency Resonances and Information Fusion

Methods Handling Accident and Traffic Jam Information with Belief Functions in VANETs

by Eric Lefèvre, David Mercier, and Sylvain Lagrue

Lecture Notes in Computer Science, 2014

An improvement of a diagnosis procedure for AC machines using two external flux sensors based on a fusion process with belief functions

by Eric Lefèvre, David Mercier, and Rémus Pusca

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012

ABSTRACT In this paper, a method for diagnosis of AC machines using the spectrum of the near magn... more ABSTRACT In this paper, a method for diagnosis of AC machines using the spectrum of the near magnetic field is presented. The method is associated to a fusion process based on belief functions which analyze the measurements. In previous works, it has been shown that it is possible to detect the inter-turns short circuit in the stator windings of electrical machines using a noninvasive method. It is based on the analysis of the variation of sensitive harmonics when the load varies, and eliminates the main drawback presented by other diagnostic methods which use the comparison with a healthy state assumed known. Several measurements around the machine are necessary to increase the probability of the fault detection because the fault position relatively to the sensor can strongly influence the results. So in this paper it is proposed to exploit conjointly the whole measurements in order to obtain a more robust and reliable diagnostic and to increase the probability of detecting the fault. The merging of the different estimations being realized through the belief functions framework, this approach is tested on real measurements. Experimental tests are performed on a special rewound induction machine in order to validate the theoretical approach.

Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, 2008

The stator insulation breakdown is a major cause of AC machine failures. Ground insulation defaul... more The stator insulation breakdown is a major cause of AC machine failures. Ground insulation defaults are easily detected by classical systems based on leakage current measurements, however the turn-to-turn insulation degradations are more difficult to detect. For large machines, on-line methods, based on partial discharge detection and analysis, give good results but they cannot be used for low-voltage machines fed by adjustable speed drives (ASD). A new monitoring system able to detect slight variations of high frequency resonances in the winding of a working machine fed by an industrial inverter was presented in [1]. Several measurements can be used in order to estimate the aging of an AC machine winding (HF measurements of current or magnetic field) [2]. When a measure, also called a piece of information, is precise and certain, no other measure is necessary. However, such a measure is rarely obtained in real world application. Information fusion consists then in merging, or exploiting conjointly, several imperfect sources of information to make proper decision. Various frameworks can be used to model the fusion, e.g., probability theory, possibility theory, belief functions [3,4]. In this paper, different measurements of the aging of an AC machine winding are combined in the latter frame. This approach is tested on real measurements.

Robust Diagnostic Based on High Frequency Resonance Measurements

Fusion d’Informations pour la Classification Multi-capteurs, Multi-cibles Information Fusion for a Multi-sensor, Multi-target Classification

by David Mercier, Samir Hachour, and Eric Lefèvre

Towards an alarm for opposition conflict in a conjunctive combination of belief functions

by David Mercier, Eric Lefèvre, and Zied Elouedi

ABSTRACT In the framework of belief functions, information fusion is based on the construction of... more ABSTRACT In the framework of belief functions, information fusion is based on the construction of a unique belief function resulting from the combination of available belief functions induced from several information sources. When sources are reliable and distinct, Smets’ conjunctive rule, which is equivalent to Dempster’s rule of combination without the normalization process, can be considered. This rule offers interesting properties, but in return the empty set is an absorbing element: a series of conjunctive combinations tends to bring a mass equal to 1 to the empty set, making impossible the distinction between a real problem and an effect due to this absorbing effect of the empty set. Then a formalism allowing the preservation of the conflict which reflects the opposition between sources, is introduced in this paper. Based on the normalization process and on distance measures between belief functions, it is tested and compared with classic conjunctive operators on synthetic belief functions.

Object association in the TBM framework, application to vehicle driving aid

Tracking and identification of multiple targets

by Samir Hachour, David Mercier, and Eric Lefèvre

Truthfulness in contextual information correction

Application du modèle des croyances transférables dans le cadre d’expertises en entomologie médico-légale

by Eric Lefèvre, David Mercier, Gildas Morvan, and Alexandre Veremme

ABSTRACT Résumé. La représentation et la gestion d&#39;informations dans le cadre des sys-tèm... more ABSTRACT Résumé. La représentation et la gestion d&#39;informations dans le cadre des sys-tèmes complexes, où les flux de données sont importants, posent bien souvent problème, en particulier lorsque des prises de décisions sont souhaitées. Cela est d&#39;autant plus difficile lorsque ces informations sont imprécises et incertaines. En Entomologie Médico-Légale, un expert, déterminant la date de la mort d&#39;une victime retrouvée dans un écosystème complexe, doit ainsi traiter ce type d&#39;in-formations imparfaites tout en restant objectif et prudent. Pour résoudre ces pro-blématiques, un système informatique d&#39;aide à la décision a été mis en oeuvre. Dans ce système, afin d&#39;obtenir une décision de meilleure qualité, une étape de fusion est intégrée. Celle-ci permet de compiler les résultats du système et repose sur le Modèle des Croyances Tranférables (MCT).

Estimation de méta-connaissancesa partir de matrices de confusion pour la correction de fonctions de croyance Estimation of meta-knowledge from confusion matrices to correct belief functions

Sur l’affaiblissement d’une fonction de croyance par une matrice de confusion About the Discounting of a Belief Function using a Confusion Matrix

Etude préliminaire de l’application de la fusion d’informations pour le diagnostic de défauts de bobinages de machinesa courant alternatif Preliminary study of an information fusion application for windings faults diagnosis of AC machines

by Eric Lefèvre, David Mercier, Rémus Pusca, and Raphael Romary

Dans cet article, une premièreétude est réalisée sur des mécanismes d'affaiblissement permettant ... more

Un processus V2V d’échanges et de gestion d’informations imparfaites basé sur des fonctions de croyance A V2V process for exchanging and managing imperfect information using belief functions

Belief assignment on compound hypotheses within the framework of the Transferable Belief Model

by David Mercier, Eric Lefèvre, and Alexandre Veremme

Within the framework of evidence theory, the construction of mass functions remains an important ... more Within the framework of evidence theory, the construction of mass functions remains an important problem which can considerably influence final results. Several propositions of construction are available in literature but generally, these methods do not consider the belief masses on compound hypotheses and do not exploit all the strengths of the theory. In this article, we propose two methods of grouping hypotheses allowing to reduce the number of focal elements during the construction of mass functions. The first one is based on a well-known approach in cartography and geoinformation fields: Jenks' method. The second one is based on information criteria. We present and confront both methods. Results in a forensic entomology application are then presented.

Sur l’ajustement d’une fonction de croyance par une matrice de confusion On the Ajustment of a Belief Function with a Confusion Matrix

Belief functions contextual discounting and canonical decompositions

Classification crédale multi-cibles Multi-targets evidential classification

by David Mercier, Samir Hachour, Eric Lefèvre, and J. M. Vannobel

Robust diagnostics of stator insulation based on high frequency resonances measurements

by Eric Lefèvre, David Mercier, and D. Roger

IEEE Transactions on Dielectrics and Electrical Insulation, 2000

International Journal of Approximate Reasoning, 2012

Discountings of a belief function using a confusion matrix

by Eric Lefèvre, Zied Elouedi, and David Mercier

In this paper, we present an analysis of different approaches relative to the correction of belie... more

Diagnostic Based on High Frequency Resonances and Information Fusion

Methods Handling Accident and Traffic Jam Information with Belief Functions in VANETs

by Eric Lefèvre, David Mercier, and Sylvain Lagrue

Lecture Notes in Computer Science, 2014

An improvement of a diagnosis procedure for AC machines using two external flux sensors based on a fusion process with belief functions

by Eric Lefèvre, David Mercier, and Rémus Pusca

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012

Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, 2008

Robust Diagnostic Based on High Frequency Resonance Measurements

Fusion d’Informations pour la Classification Multi-capteurs, Multi-cibles Information Fusion for a Multi-sensor, Multi-target Classification

by David Mercier, Samir Hachour, and Eric Lefèvre

Towards an alarm for opposition conflict in a conjunctive combination of belief functions

by David Mercier, Eric Lefèvre, and Zied Elouedi

Object association in the TBM framework, application to vehicle driving aid

Tracking and identification of multiple targets

by Samir Hachour, David Mercier, and Eric Lefèvre

Truthfulness in contextual information correction

Application du modèle des croyances transférables dans le cadre d’expertises en entomologie médico-légale

by Eric Lefèvre, David Mercier, Gildas Morvan, and Alexandre Veremme

Estimation de méta-connaissancesa partir de matrices de confusion pour la correction de fonctions de croyance Estimation of meta-knowledge from confusion matrices to correct belief functions

Sur l’affaiblissement d’une fonction de croyance par une matrice de confusion About the Discounting of a Belief Function using a Confusion Matrix

by Eric Lefèvre, David Mercier, Rémus Pusca, and Raphael Romary

Dans cet article, une premièreétude est réalisée sur des mécanismes d'affaiblissement permettant ... more

Un processus V2V d’échanges et de gestion d’informations imparfaites basé sur des fonctions de croyance A V2V process for exchanging and managing imperfect information using belief functions

Belief assignment on compound hypotheses within the framework of the Transferable Belief Model

by David Mercier, Eric Lefèvre, and Alexandre Veremme

Sur l’ajustement d’une fonction de croyance par une matrice de confusion On the Ajustment of a Belief Function with a Confusion Matrix