I'm David Mercier

I'm professor at Université d'Artois, France, doing my research in the LGI2A laboratory and my teaching at IUT de Béthune in the R&T Department.

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About Me

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I graduated in 2003 with an artificial intelligence DEA (equivalent of a M.S.) from the University Paul Sabatier (UPS) of Toulouse, France, and earned a PhD from the University of Technology of Compiègne (UTC), France in 2006, my PhD being realized in the French company Solystic, a world leader in providing solutions in sorting mails. I was an Assistant Professor (French ATER) at the UTC from November 2006 to August 2007, and at the Université d'Artois, France from September 2007 to August 2008.

Since September 2008, I am a member of the Université d'Artois, France first as an associate professor, and since September 2019 as a professor. I do my research in the LGI2A laboratory and my teaching at IUT de Béthune in the R&T Department. Since September 2012, I am a board member of the Belief Functions and Applications Society (Member since 2010). I obtained my French Research Habilitation (HDR) from the University of Artois in 2015. My research interests include information fusion and reasoning with uncertainty in particular with Belief Functions

Teaching   Publications

Teaching

I currently teach networks, web and programming at the IUT of Béthune in the department of networks and telecommunications.

Publications

International Journal Articles

  1. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Stray Flux Multi-Sensor for Stator Fault Detection in Synchronous Machines, Electronics, Vol. 10, Issue 18, article 2313, 2021. [pdf]
  2. P. Minary, F. Pichon, D. Mercier, É. Lefèvre, B. Droit. Evidential joint calibration of binary SVM classifiers, Soft Computing, Vol.23, pp 4655–4671, July 2019. [pdf]
  3. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Detection of the stator winding inter-turn faults in asynchronous and synchronous machines through the correlation between harmonics of the voltage of two magnetic flux sensors, IEEE Transaction on Industry Applications, IEEE TIA, Vol. 55, Issue 3, pp. 2682-2689, May-June 2019. [pdf]
  4. N. Helal, F. Pichon, D. Porumbel, D. Mercier, É. Lefèvre, The capacitated vehicle routing problem with evidential demands, International Journal of Approximate Reasoning, Vol. 95, pp 124-151, April 2018. [pdf]
  5. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary, C. Demian. Information fusion with belief functions for detection of inter-turn short circuit faults in electrical machines using external flux sensors, IEEE Transactions on Industrial Electronics, Vol. 65, Issue 3, pp. 2642-2652, March 2018. [pdf]
  6. P. Minary, F. Pichon, D. Mercier, É. Lefèvre, B. Droit. Face pixel detection using evidential calibration and fusion, International Journal of Approximate Reasoning, Vol. 91, pp. 202-215, December 2017. [pdf]
  7. S. Hachour, F. Delmotte, D. Mercier. A robust credal assignment solution based on the generalized Bayes’ theorem, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.25, Issue 6, pp 947–971, December 2017.
  8. M. Bou Farah, D. Mercier, É. Lefèvre, F. Delmotte, Methods using belief functions to manage imperfect information concerning events on the road in VANETs, Transportation Research Part C: Emerging Technologies, Vol. 67, pp. 299-320, June 2016. [pdf]
  9. F. Pichon, D. Mercier, É. Lefèvre, F. Delmotte, Proposition and learning of some belief function contextual correction mechanisms, International Journal of Approximate Reasoning, Vol. 72, pp 4-42, May 2016. [pdf]
  10. D. Mercier, F. Pichon, É. Lefèvre Corrigendum to “Belief functions contextual discounting and canonical decompositions” [International Journal of Approximate Reasoning 53 (2012) 146–158], International Journal of Approximate Reasoning, Vol. 70, pp. 137-139, March 2016. [pdf]
  11. S. Hachour, F. Delmotte, D. Mercier, É. Lefèvre, Object tracking and credal classification with kinematic data in a multi-target context, Information Fusion, Vol. 69, pp. 174-188, November 2014 [pdf]
  12. M. Bou Farah, D. Mercier, É. Lefèvre, F. Delmotte, A high-level application using belief functions for exchanging and managing uncertain events on the road in vehicular ad-hoc networks, Annals of telecommunications: special issue on belief functions and uncertainty management in networks and telecommunication, Vol. 69, Issue 3-4, pp. 185-199, April 2014. [pdf]
  13. D. Mercier, É. Lefèvre, F. Delmotte, Belief functions contextual discounting and canonical decompositions, International Journal of Approximate Reasoning, Vol. 53, Issue 2, pp. 146-158, February 2012. [pdf]
  14. D. Mercier, É. Lefèvre, D. Jolly, Object association with belief functions, an application with vehicles, Information Sciences, Vol. 181, Issue 24, pp. 5485-5500, December 2011. [pdf]
  15. F. Périsse, D. Mercier, É. Lefèvre, D. Roger, Robust diagnostics of stator insulation based on high frequency resonances measurements, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 16, Issue 5, pp. 1496-1502, October 2009. (In the electronics specialists commmunity, a collaboration with the LSEE laboratory). [pdf]
  16. D. Mercier, G. Cron, T. Denœux, M.-H. Masson, Decision fusion for postal address recognition using belief functions, Expert Systems with Applications, Vol. 36, Issue 3, pp. 5643-5653, April 2009. [pdf]
  17. D. Mercier, B. Quost, T. Denœux, Refined modeling of sensor reliability in the belief function framework using contextual discounting, Information Fusion, Vol. 9, Issue 2, pp 246-258, April 2008. [pdf]

Book Chapters

  1. R. Pusca, É. Lefèvre, D. Mercier, R. Romary, M. Irhoumah, Diagnosis of electrical machines by external field measurement, Electrical Systems 2: From Diagnosis to Prognosis, pp 1-35, A. Soauhli and H. Razik, Wiley Online Library, avril 2020
  2. S. Hachour, F. Delmotte, D. Mercier, Belief function based multisensor multitarget classification solution, Multisensor data fusion, From algorithms and architectural design to applications, H. Fourati (Ed.), CRC Press, pp. 331-348, August 2015.
  3. D. Mercier, T. Denœux, M.-H. Masson, Belief function correction mechanisms, Studies in Fuzziness and Soft Computing, B. Bouchon-Meunier et al. (Eds.), Vol. 249, pp. 203-222, January 2010. [pdf]

Editorials

  1. S. Destercke, D. Mercier, F. Pichon, Special issue from the 5th International Conference on Belief Functions (BELIEF 2018), International Journal of Approximate Reasoning, pp 50-51, Vol. 117, February 2020.

French-speaking Journal Articles

  1. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Diagnostic de machines électriques utilisant six capteurs de champ extérieur, 3EI, Vol. 90, pp. 30-36, Octobre 2017.
  2. D. Mercier, É. Lefèvre, D. Jolly, Association pour le suivi d'objets dans le cadre des fonctions de croyance, appliquée aux véhicules intelligents, Revue des Nouvelles Technologies de l'Information, Vol. E-18, pp. 25-50, Février 2010. [pdf]
  3. D. Mercier, G. Cron, T. Denœux, M.-H. Masson, Fusion de décisions postales dans le cadre du Modèle des Croyances Transférables, Traitement du Signal, vol. 24, num. 2, pp. 133-151, 2007. [pdf]

International Conference Papers

  1. S. Mutmainah, S. Hachour, F. Pichon, D. Mercier. On Improving a Group of Evidential Sources with Different Contextual Corrections, 7th International Conference on Belief Functions, BELIEF 2022, S. Le Hégarat-Mascle, I. Bloch, and E. Aldea (Eds.), pp 109-118, Paris, France, October 26-28, 2022. [pdf]
  2. Y.-L. Huang, G. Morvan, F. Pichon, D. Mercier. SPSC: an efficient, general-purpose execution policy for stochastic simulations, The 2021 Winter Simulation Conference, WSC 2021, S. Kim, B. Feng, K. Smith, S. Masoud, Z. Zheng, C. Szabo, M. Loper, (Eds.), Phoenix, USA, December 13-16, 2021. [pdf]
  3. S. Mutmainah, S. Hachour, F. Pichon, D. Mercier. Improving an Evidential Source of Information Using Contextual Corrections Depending on Partial Decisions, 6th International Conference on Belief Functions, BELIEF 2021, T. Denœux, É. Lefèvre, Z. Liu and F. Pichon (Eds.), pp 247-256, Shanghai, China, October 15-19, 2021. [pdf]
  4. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Adapted coil sensors for measuring the external magnetic field of electrical machines, 6th International conference on engineering & MIS, ICEMIS'2020, article n°88, pp 1-7, September 2020.
  5. S. Mutmainah, S. Hachour, F. Pichon, D. Mercier. On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions, 13th International Conference on Scalable Uncertainty Management, SUM 2019, N. Ben Amor, B. Quost and M. Theobald (Eds.), Springer, Volume 11940 of Lecture Notes in Computer Science, pp 405–411, Compiègne, France, December 16-18, 2019. [pdf]
  6. Y.-L. Huang, G. Morvan, F. Pichon, D. Mercier. SPSC: A New Execution Policy for Exploring Discrete-Time Stochastic Simulations, 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019, M. Baldoni, M. Dastani, B. Liao, Y. Sakurai and R. Zalila Wenkstern (Eds.), Springer, Volume 11873 of Lecture Notes in Computer Science, pp 568-575, Torino, Italy, October 28-31, 2019. [pdf]
  7. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Information fusion of external flux sensors for detection of inter-turn short circuit faults in induction machines, 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Beijing, China, October 29 - November 1, 2017. [pdf]
  8. P. Minary, F. Pichon, D. Mercier, É. Lefèvre, B. Droit. Evidential joint calibration of binary SVM classifiers using logistic regression, 11th International Conference on Scalable Uncertainty Management, SUM 2017, S. Moral, O. Pivert, D. Sánchez and N. Marín (Eds.), Springer, Volume 10564 of Lecture Notes in Computer Science, pp 405–411, Granada, Spain, October 4-6, 2017. [pdf]
  9. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Diagnosis of induction machines using external magnetic field and correlation coefficient, 11th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2017, pp 531-536, Tinos, Greece, August 29 - September 1, 2017. [pdf]
  10. N. Helal, F. Pichon, D. Porumbel, D. Mercier, É. Lefèvre. A Recourse Approach for the Capacitated Vehicle Routing Problem with Evidential Demands, 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017, A. Antonucci, L. Cholvy and O. Papini (Eds), LNCS 10369, pp. 190-200, Lugano, Switzerland, July 10-14, 2017. [pdf]
  11. M. Irhoumah, R. Pusca, É. Lefèvre, D. Mercier, R. Romary. Detection of stator fault in synchronous generator without the knowledge of the healthy state, 12th issue of the international conference on theory and application of modeling and simulation for analysis, control, power management and design in electrical power engineering, Electrimacs 2017, Paper 137, Toulouse, France, July 4-6, 2017. [pdf]
  12. P. Minary, F. Pichon, D. Mercier, É. Lefèvre, B. Droit. An Evidential Pixel-Based Face Blurring Approach, 4th International Conference on Belief Functions, BELIEF 2016, J. Vejnarová and V. Kratochvil (Eds.), pp 222-230, Prague, Czech Republic, September 21-23, 2016. (co-best student paper award) [pdf]
  13. N. Helal, F. Pichon, D. Porumbel, D. Mercier, É. Lefèvre. The Capacitated Vehicle Routing Problem with Evidential Demands: A Belief-Constrained Programming Approach, 4th International Conference on Belief Functions, BELIEF 2016, J. Vejnarová and V. Kratochvil (Eds.), pp 212-221, Prague, Czech Republic, September 21-23, 2016. [pdf]
  14. P. Minary, B. Droit, F. Pichon, D. Mercier, É. Lefèvre. A fusion method for blurring faces on platforms using belief functions, 11th World Congress on Railway Research, Milan, Italy, May 29-June 2, 2016. [pdf]
  15. D. Mercier, F. Pichon, É. Lefèvre, F. Delmotte, Learning contextual discounting and contextual reinforcement from labelled data, 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015, S. Destercke and T. Denœux (Eds), LNAI 9161, pp. 472-481, Compiègne, France, July 15-17, 2015. [pdf]
  16. F. Pichon, D. Mercier, F. Delmotte, É. Lefèvre, Truthfulness in contextual information correction, 3rd International Conference on Belief Functions, BELIEF 2014, F. Cuzzolin (Ed.), pp. 11-20, Oxford, United Kingdom, September 26-28, 2014. [pdf]
  17. M. Bou Farah, D. Mercier, F. Delmotte, É. Lefèvre, S. Lagrue, Methods handling accident and traffic jam information with belief functions in VANETs, 3rd International Conference on Belief Functions, BELIEF 2014, F. Cuzzolin (Ed.), pp. 124-133, Oxford, United Kingdom, September 26-28, 2014. [pdf]
  18. S. Hachour, F. Delmotte, D. Mercier, A new parameterless credal method to track-to-track assignment problem, 3rd International Conference on Belief Functions, BELIEF 2014, F. Cuzzolin (Ed.), pp. 403-411, Oxford, United Kingdom, September 26-28, 2014. [pdf]
  19. F. Delmotte, D. Mercier, F. Pichon, A first inquiry into Simpson's paradox with belief functions, 15th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference, IPMU 2014, pp. 190-199, Montpellier, France, July 15-19, 2014. [pdf]
  20. S. Hachour, F. Delmotte, D. Mercier, Comparison of credal assignment algorithms in kinematic data tracking context, 15th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference, IPMU 2014, pp. 200-211, Montpellier, France, July 15-19, 2014. [pdf]
  21. S. Hachour, F. Delmotte, D. Mercier, A distributed solution for multi-object tracking and classification, 17th International Conference on Information Fusion, FUSION 2014, paper 323, Salamanca, Spain, July 7-10, 2014. [pdf]
  22. S. Hachour, F. Delmotte, D. Mercier, É. Lefèvre, Multi-sensor multi-target tracking with robust kinematic data based credal classification, 8th Workshop, Sensor Data Fusion: Trends, Solutions, Application, SDF 2013, paper Thu1130, Bonn, Germany, October 9-11, 2013. [pdf]
  23. M. Bou Farah, D. Mercier, É. Lefèvre, F. Delmotte, Exchanging dynamic and imprecise information in V2V networks with belief functions, 16th International IEEE Conference on Intelligent Transport Systems, ITSC 2013, pp. 967-972, The Hague, The Netherlands, October 6-9, 2013 [pdf] [pres]
  24. R. Pusca, C. Demian, D. Mercier, É. Lefèvre, R. Romary, An improvement of a diagnosis procedure for AC machines using two external flux sensors based on a fusion process with belief functions, 38th Annual Conference of the IEEE Industrial Electronics Society, IECON 2012, pp. 5078-5083, Montréal, Québec, Canada, October 25-28, 2012. [pdf]
  25. É. Lefèvre, Z. Elouedi, D. Mercier, Towards an alarm for opposition conflict in a conjunctive combination of belief functions, 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2011, LNAI 6717, pp. 314-325, Belfast, Northern Ireland, UK, June 29 - July 1, 2011. [pdf]
  26. M. Bou Farah, D. Mercier, É. Lefèvre, F. Delmotte, Towards a robust exchange of imperfect information in inter-vehicle ad-hoc networks using belief functions, IEEE Intelligent Vehicles Symposium, IV 2011, pp. 436-441, Baden-Baden, Germany, June 5-9, 2011. [pdf] [poster]
  27. Z. Elouedi, É. Lefèvre, D. Mercier, Discountings of a belief function using a confusion matrix, 22th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2010, Vol. 1, pp. 287-294, Arras, France, October 27-29, 2010. [pdf]
  28. D. Mercier, Extending the contextual discounting of a belief function thanks to its canonical disjunctive decomposition, 1st Workshop on Belief Functions, BELIEF 2010, paper 61, Brest, France, April 1-2, 2010. [pdf]
  29. D. Mercier, É. Lefèvre, D. Jolly, Object association in the TBM framework, application to vehicle driving aid, 6th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2009, pp. 317-326, Durham, United Kingdom, July 14-18, 2009. [pdf]
  30. A. Veremme, É. Lefèvre, D. Dupont, D. Mercier, Belief assignment on compound hypotheses within the framework of the Transferable Belief Model, 12th International Conference on Information Fusion, FUSION 2009, paper 0203, Seattle, USA, July 6-9, 2009. [pdf]
  31. D. Mercier, T. Denœux, M.-H. Masson, A parametrized family of belief functions correction mechanisms, 12th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference, IPMU 2008, pp. 306-313, Malaga, Spain, June 22-27, 2008. [pdf]
  32. F. Périsse, É. Lefèvre, D. Mercier, D. Roger, E. Matéo, Diagnostic based on high frequency resonances and information fusion, 17th IEEE International Symposium on Electrical Insulation, ISEI 2008, pp. 628-631, Vancouver, Canada, June 8-11, 2008. [pdf]
  33. D. Mercier, T. Denœux, M.-H. Masson, General correction mechanisms for weakening or reinforcing belief functions, 9th International Conference on Information Fusion, FUSION 2006, article 146, Florence, Italy, July 10-13, 2006. [pdf]
  34. D. Mercier, T. Denœux, M.-H. Masson, Refined sensor tuning in the belief function framework using contextual discounting, 11th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference, IPMU 2006, pp. 1443-1450, Paris, France, July 2-7, 2006. [pdf]
  35. D. Mercier, G. Cron, T. Denœux, M.-H. Masson, Fusion of multi-level decision systems using the Transferable Belief Model, 8th International Conference on Information Fusion, article C8-2, FUSION 2005, Philadelphia, USA, July 25-29, 2005. [pdf]
  36. D. Mercier, B. Quost, T. Denœux, Contextual discounting of belief functions, 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2005, pp. 552-562, Barcelona, Spain, July 6-8, 2005. [pdf]

French-speaking Conference Papers

  1. S. Mutmainah, S. Hachour, F. Pichon, D. Mercier. Améliorer un groupe de sources crédibilistes avec des corrections contextuelles, 31e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2022, Cépaduès éditions, pp.  239-246, Toulouse, France, 20-21 Octobre, 2022. [pdf]
  2. S. Mutmainah, F. Pichon, D. Mercier. Corrections contextuelles crédibilistes en fonction de décisions partielles., 30e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2021, Cépaduès éditions, pp.  217-224, Paris, France, 21-22 Octobre, 2021. [pdf]
  3. S. Mutmainah, F. Pichon, D. Mercier. Apprentissage de corrections contextuelles crédibilistes à partir de données partiellement étiquetées en utilisant la fonction de contour, 28e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2019, Cépadues éditions, pp. 157-164, Alès, France, 14-15 Novembre, 2019. [pdf]
  4. Y.-L. Huang, G. Morvan, F. Pichon, D. Mercier. Détection d'événements rares dans les simulations multi-agents, Journées Francophones sur les Systèmes Multi-Agents, JFSMA 2018, Métabief, France, 10-12 Octobre, 2018. [pdf]
  5. N. Helal, F. Pichon, D. Porumbel, D. Mercier, É. Lefèvre, Le problème de tournées de véhicules avec des demandes évidentielles, 26e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2017, Cépadues éditions, pp. 15-21, Amiens, France, 19-20 Octobre, 2017. (co-meilleur papier doctorant) [pdf]
  6. P. Minary, F. Pichon, D. Mercier, É. Lefèvre, B. Droit, Calibration évidentielle conjointe de classifieurs SVM binaires fondée sur la régression logistique, 26e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2017, Cépadues éditions, pp. 39-46, Amiens, France, 19-20 Octobre, 2017. (co-meilleur papier doctorant) [pdf]
  7. N. Helal, F. Pichon, D. Porumbel, D. Mercier, É. Lefèvre, Optimisation discrète sous incertitudes modélisées par des fonctions de croyance, 17ème congrès ROADEF de la société Française de Recherche Opérationnelle et Aide à la Décision, Compiègne, France, 10-12 Février, 2016. [pdf]
  8. É. Lefèvre, F. Pichon, D. Mercier, E. Elouedi, B. Quost, Estimation de méta-connaissances à partir de matrices de confusion pour la correction de fonctions de croyance, 23e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2014, pp. 115-122, Cargèse, France, 22-24 Octobre, 2014. [pdf]
  9. S. Hachour, F. Delmotte, D. Mercier, É. Lefèvre, Fusion d'informations pour la classification multi-capteurs, multi-cibles, 22e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2013, pp. 111-118, Reims, France, 10-11 Octobre, 2013. [pdf]
  10. S. Hachour, F. Delmotte, É. Lefèvre, D. Mercier. Tracking and Identification of Multiple targets. 7e Workshop Interdisciplinaire sur la Sécurité Globale, WISG 2013, papier 13, Troyes, France, 22-23 Janvier, 2013. [pdf] [poster]
  11. M. Bou Farah, D. Mercier, É. Lefèvre, F. Delmotte, Un processus V2V d'échanges et de gestion d'informations imparfaites basé sur des fonctions de croyance, 21e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2012, pp. 71-78, Compiègne, France, 15-16 Novembre, 2012. [pdf]
  12. S. Hachour, F. Delmotte, É. Lefèvre, D. Mercier, J. Klein, J.M. Vannobel, Classification crédale multi-cibles, 21e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2012, pp. 201-208, Compiègne, France, 15-16 Novembre, 2012. [pdf]
  13. D. Mercier, É. Lefèvre, R. Pusca, C. Demian, R. Romary, Étude préliminaire de l'application de la fusion d'informations pour le diagnostic de défauts de bobinages de machines à courant alternatif, 21e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2012, pp. 87-94, Compiègne, France, 15-16 Novembre, 2012. [pdf]
  14. D. Mercier, Z. Elouedi, É. Lefèvre, Sur l'affaiblissement d'une fonction de croyance par une matrice de confusion, 19e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2010, pp. 277-283, Lannion, France, 18-19 Novembre, 2010. [pdf]
  15. G. Morvan, A. Veremme, D. Mercier, É. Lefèvre, Application du Modèle des Croyances Transférables dans le cadre d'expertises en Entomologie Médico-Légale, 5e atelier sur la Fouille de données complexes dans un processus d'extraction des connaissances des 8e journées francophones Extraction et Gestion des Connaissances, pp. 49-60, 29 janvier, Nice, 2008. [pdf]
  16. D. Mercier, G. Cron, T. Denœux, M.-H. Masson, Une Approche Globale de Fusion d'Adresses Postales Basée sur la Théorie des Fonctions de Croyance, 15e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2006, pp. 287-294, Toulouse, France, 19-20 Octobre, 2006. [pdf]
  17. D. Mercier, G. Cron, T. Denœux, M.-H. Masson, Vers un Modèle de Fusion de Décisions de Lecteurs d'Adresses Postales Basé sur la Théorie des Fonctions de Croyance, 9e Colloque International Francophone sur l'Écrit et le Document, CIFED 2006, pp. 79-84, Fribourg, Suisse, 18-21 Septembre, 2006. [pdf]
  18. L. Amgoud, A. Herzig, D. Mercier, Calcul des intentions d'agent à partir de ses désirs, 2e Conférence Modèles Formels d'Interaction, MFI 2003, pp 3-9, Lille, France, 20-23 mai, 2003. (Realized during my DEA, equivalent of a M.S. in the IRIT laboratory of Toulouse) [pdf]

Scientific Patents

  1. D. Mercier, G. Cron, B. Benyoub, Method of merging postal OCR using credibility functions, European Patent Application EP1835444, september 19th, 2007.
  2. D. Mercier, G. Cron, B. Benyoub, Méthode pour fusionner des OCR postaux exploitant des fonctions de croyance, Brevet n°FR 0650910, 17 mars 2006.

Mémoire scientifique HDR (In French)

  1. D. Mercier, Corrections et fusion d'informations dans le cadre des fonctions de croyance. Applications., defended on December 4th, 2015. [pdf,slides] Supervisor: É. Lefèvre.

Ph.D. Thesis (In French)

  1. D. Mercier, Fusion d'informations pour la reconnaissance automatique d'adresses postales dans le cadre de la théorie des fonctions de croyance, defended on December 6th, 2006. [pdf,slides,pres.] Supervisors: T. Denœux and M.-H. Masson.

Misc.

PhD Students

Defended

  1. S. Mutmainah. Learning to adjust an evidential source of information using partially labeled data and partial decisions, supervisor: D. Mercier (30%), co-supervisors: S. Hachour (35%) and F. Pichon (35%), defended on September 7th, 2021 [pdf, slides]
  2. Y.-L. Huang. Une nouvelle politique d’exécution de simulations stochastiques fondée sur des principes de partitionnement, de sélection et de clonage, supervisor: D. Mercier (30%), co-supervisors: G. Morvan (35%) and F. Pichon (35%), defended on July 6th, 2021 [pdf, slides, demo]
  3. P. Minary. Evidential calibration and fusion of multiple classifiers : application to face blurring, (industrial convention with the French company SNCF) supervisor: É. Lefèvre (30%), co-supervisors: F. Pichon (35%) and D. Mercier (35%), defended on December 8th, 2017 [pdf, slides, demo (video)]
  4. S. Hachour. Suivi et classification d'objets multiples : contributions avec la théorie des fonctions de croyance, supervisor: F. Delmotte (60%), co-supervisor: D. Mercier (40%), defended on June 5th, 2015 [pdf, slides]
  5. M. Bou Farah. Méthodes utilisant des fonctions de croyance pour la gestion des informations imparfaites dans les réseaux de véhicules, supervisor: F. Delmotte (30%), co-supervisor: D. Mercier (70%), defended on December 2nd, 2014 [pdf, slides]

Talks (Selection)

  1. D. Mercier and É. Lefèvre Tutoriels introduction aux fonctions de croyance et aux applications en classification, Plate-Forme Intelligence Artificielle Saint-Étienne, France, 27 Juin - 1er Juillet 2022 [ Intro BF | Intro Classif ]
  2. D. Mercier, On belief function corrections, 4th School on Belief Functions and Their Applications, Xi'an, China, July 5th to 9th, 2017 [pdf]
  3. D. Mercier, Autour des mécanismes de correction de fonctions de croyance, Réunion GDR ISIS - Avancées en Fusion de données - 11 février 2010 [pdf]
  4. D. Mercier, Fusion d'informations imparfaites, Petit Déjeuner Scientifique, IRSAA, 31 mars 2009 [(vulgarisation) pdf]
  5. D. Mercier, Le Modèle des Croyances Transférables : une interprétation de la théorie des fonctions de croyance, Séminaire LGI2A Béthune, 13 novembre 2007 [pdf]

IUT de Béthune
Département R&T
1230 rue de l'Université, CS 20819
62408 BÉTHUNE Cedex
France.

Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A)
Faculté des Sciences Appliquées
Technoparc Futura
62400 BÉTHUNE
France.

 

 

 

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