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Fouille de séries temporelles pour l'explicabilité de la dégradation de l'état de charge des batteries Lithium-ions.

, , , , and . EGC, volume E-39 of RNTI, page 305-312. Editions RNTI, (2023)

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Context Modeling for Industry 4.0: an Ontology-Based Proposal., , and . KES, volume 126 of Procedia Computer Science, page 675-684. Elsevier, (2018)Towards the use of Situation Hierarchies for supporting Decision Making: A Formal Lattice-Based Approach., , and . RuleML+RR (Supplement), volume 2644 of CEUR Workshop Proceedings, page 73-86. CEUR-WS.org, (2020)Abnormal Situations Interpretation in Industry 4.0 using Stream Reasoning., , and . KES, volume 159 of Procedia Computer Science, page 620-629. Elsevier, (2019)STEaMINg: semantic time evolving models for industry 4.0. (STEaMINg: modèles sémantiques qui évoluent dans le temps pour l'industrie 4.0).. Normandy University, Caen, France, (2020)Fouille de séries temporelles pour l'explicabilité de la dégradation de l'état de charge des batteries Lithium-ions., , , , and . EGC, volume E-39 of RNTI, page 305-312. Editions RNTI, (2023)Towards the use of post-hoc explainable methods to define and detect semantic situations of importance in medical data., , , and . KES, volume 225 of Procedia Computer Science, page 2312-2321. Elsevier, (2023)OntoSoC: An ontology-based approach to battery pack SoC estimation., , , , , and . KES, volume 225 of Procedia Computer Science, page 2254-2263. Elsevier, (2023)Detecting Situations with Stream Reasoning on Health Data Obtained with IoT., , and . KES, volume 192 of Procedia Computer Science, page 507-516. Elsevier, (2021)Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach., , , , and . Cybern. Syst., 50 (2): 82-96 (2019)