Philippe Thomas
Publications
Publications
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MLP model for prediction of pellet combustion: how to deal with small datasets17th International Conference on Neural Computation Theory and Application, NCTA 2025, insticc, Oct 2025, Marbella, Spain |
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An object-oriented architecture to couple simulators and their machine learning surrogates models in the context of digital shadows22nd IFAC World Congress, IFAC 2023, Jul 2023, Yokohama, Japan. ⟨10.1016/j.ifacol.2023.10.1051⟩ |
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CoreSelect: a new approach to select landmarks for dissimilarity space embedding15th International Joint Conference on Computational Intelligence, IJCCI 2023, International Conference on Neural Computation Theory and Applications, NCTA 2023, Nov 2023, Rome, Italy |
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A MLP for dryer energy consumption prediction in wood panel industry14th International Conference on Neural Computation Theory and Applications, NCTA 2022, Oct 2022, Valetta, Malta. ⟨10.5220/0011541900003332⟩ |
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A comparison of wood log dissimilarities to predict sawmill output with k-Nearest Neighbor algorithms4th International Conference on Advances in Signal Processing and Artificial Intelligence, ASPAI' 2022, Oct 2022, Corfu, Greece |
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Toward a sawmill digital shadow based on coupled simulation and supervised learning models12th International Workshop on Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA’22, Sep 2022, Bucharest, Romania |
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Toward a self-adaptive digital twin based active learning method: an application to the lumber industry14th IFAC Workshop on Intelligent Manufacturins Systems, IMS 2022, Mar 2022, Tel Aviv, Israel |
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A kNN approach based on ICP metrics for 3D scans matching: an application to the sawing process17th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2021, Jun 2021, Budapest (virtual), Hungary |
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Dissimilarity to class medoids as features for 3D point cloud classificationIFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.573-581, ⟨10.1007/978-3-030-85906-0_62⟩ |
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Neural network architectures and feature extraction for lumber production predictionThe 34th Canadian Conference on Artificial Intelligence, May 2021, Vancouver, Canada. ⟨10.21428/594757db.89eadeff⟩ |
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Medoid-based MLP: an application to wood sawing simulator metamodeling13th International Conference on Neural Computation Theory and Applications, NCTA 2021, Oct 2021, Online streaming, Portugal |
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Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithmInternational Joint Conference on Neural Networks, IJCNN 2019, Jul 2019, Budapest, Hungary |
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Demand forecasting using artificial neuronal networks and time series: application to a French furniture manufacturer case study11th International Joint Conference on Computational Intelligence, IJCCI’19 (11th International Conference on Neural Computation Theory and Applications, NCTA), Sep 2019, Vienne, Austria |
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Toward a sustainable new product development approach based on industry 4.0 assets8th Workshop on Service Orientation in Holonic and Multi-agent Manufacturing, SOHOMA'18, Jun 2018, Bergamo, Italy |
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Using AHP process for scheduling problem based on smart lots and their quality prediction capability8th Workshop on Service Orientation in Holonic and Multi-agent Manufacturing, SOHOMA'18, Jun 2018, Bergamo, Italy |
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Neural network inverse model for quality monitoring: Application to a high quality lackering process9th International Joint Conference on Computational Intelligence, IJCCI’17, Nov 2017, Funchal, Portugal |
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A case study of an Intelligent Manufacturing Control based on multi-agents' system to deal with batching and sequencing on rework context7th Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA’17, Oct 2017, Nantes, France |
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Impact of hidden weights choice on accuracy of MLP with randomly fixed hidden neurons for regression problems9th International Joint Conference on Computational Intelligence, IJCCI’17, Nov 2017, Funchal, Portugal |
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A batching and scheduling optimisation for a cutting work-center: Acta-Mobilier case study7th International Conference on Industrial Engineering and Systems Management, IESM'17, Oct 2017, Saarbrücken, Germany |
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A hybrid manufacturing control based on smart lots in a disrupted industrial context20th IFAC World Congress, IFAC 2017, Jul 2017, Toulouse, France |
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An iterative closest point method for measuring the level of similarity of 3D log scans in wood industry7th Workshop on Service Orientation in Holonic and Multi Agent Manufacturing, SOHOMA'17, Oct 2017, Nantes, France |
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K-means clustering based method for production flow management facing rework disturbancesInternational Conference on Information Systems, Logistics and Supply Chain, ILS’16, Jun 2016, Bordeaux, France |
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Réordonnancement dynamique rapide dans un contexte de système contrôlé par le produit sur un poste de travail soumis à un fort taux de reprisesJournées Produits Intelligents, JPI 2015, Mar 2015, Valenciennes, France |
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Reduced simulation model for flow analysis in a sawmill internal supply chainInternational Conference on Industrial Engineering and Systems Management, IESM'15, Oct 2015, Seville, Spain |
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Perceptron learning for classification problems7th International Conference on Neural Computation Theory and Applications, NCTA 2015, (part of the 7th International Joint Conference on Computational Intelligence, IJCCI'15), Nov 2015, Lisbonne, Portugal |
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Impact de la qualité sur la gestion des flux de production dans les ateliers à forts taux de reprises Application au cas d'un fabricant de mobiliers laqués de très haute qualité11ème Congrès International Pluridisciplinaire en Qualité, Sûreté de Fonctionnement et Développement Durable, QUALITA 2015, Mar 2015, Nancy, France |
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Impact de la qualité sur la gestion des flux de production dans les ateliers à forts taux de reprises11ème Congrès International Pluridisciplinaire en Qualité, Sûreté de Fonctionnement et Développement Durable, QUALITA'2015, Mar 2015, Nancy, France |
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Un ensemble classificateur pour la classification de données dynamiques. Application à un problème de qualité d'air intérieur10ème Conférence Francophone de Modélisation, Optimisation et Simulation, MOSIM'14, Nov 2014, Nancy, France |
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Réduction et génération automatique de modèle Arena© en exploitant des arbres de régression : cas d'une scierie10ème Conférence Francophone de Modélisation, Optimisation et Simulation, MOSIM'14, Nov 2014, Nancy, France |
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Combinaison d'indicateurs pour évaluer la perturbation des flux dans les ateliers à forts taux de reprises10ème Conférence Francophone de Modélisation, Optimisation et Simulation, MOSIM'14, Nov 2014, Nancy, France |
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CART for supply chain simulation models reductionIFIP International Conference on Advances in Production Management Systems, APMS 2014, Sep 2014, Ajaccio, France. pp.530-537, ⟨10.1007/978-3-662-44733-8_66⟩ |
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Flow disturbance analysis in workshops with high reworks rateIFIP International Conference on Advances in Production Management Systems, APMS'14, Sep 2014, Ajaccio, France. pp.280-287, ⟨10.1007/978-3-662-44733-8_35⟩ |
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CART for supply chain simulation models reduction: application to a sawmill internal supply chainIFIP International Conference on Advances in Production Management Systems, APMS'14, Sep 2014, Ajaccio, France. ⟨10.1007/978-3-662-44733-8_66⟩ |
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Prediction model adaptation thanks to control chart monitoring application to pollutants prediction6th International Joint Conference on Computational Intelligence, IJCCI'14, Oct 2014, Rome, Italy |
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Improving production process performance thanks to neuronal analysis11th IFAC Workshop on Intelligent Manufacturing System, IMS'2013, May 2013, Sao Paulo, Brazil. pp.CDROM |
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Implantation of an on-line quality process monitoringInternational Conference on Industrial Engineering and Systems Management, IESM'13, Oct 2013, Rabat, Morocco. pp.CDROM |
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Neural Networks ensemble for quality monitoring5th International Conference on Neural Computation Theory and Application, NCTA'13, Sep 2013, Vilamoura, Portugal. pp.CDROM |
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Retour d'expérience industrielle sur le choix d'une technologie d'information portée par les produits9e Conférence Internationale de Modélisation, Optimisation et Simulation, MOSIM'12, Jun 2012, Bordeaux, France. pp.CDROM |
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One approach of data-mining for Product Driven Systems14th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2012, May 2012, Bucarest, Romania. pp.CDROM |
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Petri net control of a kanban loopInternational Conference on Industrial Engineering and Systems Management, IESM 2011, May 2011, Metz, France. pp.CDROM |
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De la nécessité des bonnes informations dans les systèmes contrôlés par les produits7ème Conférence Internationale Conception et Production Intégrées, CPI'2011, Oct 2011, Oujda, Maroc. pp.CDROM |
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Proposition d'une procédure de détermination de temps unitaire de pièces découpées8ème Conférence Internationale de Modélisation et Simulation, MOSIM'10, May 2010, Hammamet, Tunisie. pp.CDROM |
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Pilotage d'une boucle KANBAN modélisée par réseau de Petri8ème Conférence Internationale de Modélisation et Simulation, MOSIM'10, May 2010, Hammamet, Tunisie. pp.CDROM |
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How deals with discrete data for the reduction of simulation models using neural network13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'2009, Jun 2009, Moscou, Russia. pp.1177-1182 |
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Reduction of Product Driven System emulation models based on neural network: impact of discrete dataInternational Conference on Industrial Engineering and Systems Management, IESM' 2009, May 2009, Montréal, Canada. pp.CD |
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Sélection de la structure d'un perceptron multicouches pour la réduction dun modèle de simulation d'une scierie.5ème Conférence Internationale Francophone d'Automatique, CIFA'2008, Sep 2008, Bucarest, Roumanie. pp.CDROM |
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Expérimentation de la réduction d'un modèle de simulation par réseau de neurones : cas d'une scierie7ème Conférence Internationale de Modélisation, Optimisation et Simulation des Systèmes, MOSIM 08, Mar 2008, Paris, France. pp.2030-2038 |
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Simulation Reduction Models Approach Using Neural Network10th International Conference on Computer Modelling and Simulation, EUROSIM'08, Apr 2008, Cambridge, United Kingdom. pp.679-684 |
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Elagage d'un perceptron multicouches : utilisation de l'analyse de la variance de la sensibilité des paramètres5ème Conférence Internationale Francophone d'Automatique, CIFA'2008, Sep 2008, Bucarest, Roumanie. pp.CDROM |
Fault detection for HDS by means of neural networks: application to two tanks hydraulic system6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS'06, Sep 2006, Beijing, China. pp.1039 - 1044 |
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Impact of RFID technology on Petri net modelisation: application to an assembly platformNov 2006, pp.CDROM |
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Modeling and Monitoring of Hybrid Dynamic Systems with Feed-Forward Neural Networks: application to two tanks hydraulic systemWorkshop on Advanced Control and Diagnosis, Nov 2005, Mulhouse, France. pp.103-109 |
Big data analytics and machine learning for industrial cyber-physical systemsOlivier Cardin, Damien Trentesaux, William Derigent. Digitalization and Control of Industrial Cyber‐Physical Systems: Concepts, Technologies and Applications, ISTE - Wiley, 149-170, Part 5, Chapter 9, 2022, 9781789450859. ⟨10.1002/9781119987420.ch9⟩ |
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An approach to data-mining for product driven systemsTheodor Borangiu, André Thomas, Damien Trentesaux. Service Orientation in Holonic and Multi-Agent Manufacturing Control, Springer-Verlag, pp.181-194, 2013, Studies in Computational Intelligence, vol. 472, 978-3-642-35851-7. ⟨10.1007/978-3-642-35852-4_12⟩ |
Semi-Supervised Learning edited by O. Chapelle, B. Schölkopf and A. Zien2009, pp.542 |
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Gradient-based controllers for continuous Petri netsUniversité de Lorraine. 2012 |
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Des carrefours « intelligents » pour une meilleure gestion du trafic urbain[Rapport Technique] Université Le Havre Normandie. 2008, 9 p |
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Contribution à l'identification de systèmes non linéaires par réseaux de neuronesSciences de l'ingénieur [physics]. Université Henri Poincaré - Nancy 1, 1997. Français. ⟨NNT : 1997NAN10030⟩ |
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Exploitation de données pour la modélisation.Réseau de neurones [cs.NE]. Université de Lorraine, 2014 |