Publications
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Contribution à la caractérisation de l’affordance d’un environnement de travail industriel : une approche basée sur l’apprentissage profond combinant données réelles et synthétiques
Ce travail s’inscrit dans le cadre du projet “École De La Batterie”, dont l’un des objectifs concerne l’optimisation de la conception des postes de travail manuel dans le but d’améliorer leur ergonomie. Nos travaux s’inscrivent dans cette démarche et visent à caractériser l’affordance des éléments de ces environnements avec lesquels les opérateurs interagissent (outils, composants, […]
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Optimizing building envelope design across various French climates: A multi-objective approach using NSGA II and RMP method
Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption. To address this, the present work presents a novel multi-objective optimization approach, aiming to determine the optimal building envelope design. The developed approach focuses on minimizing energy consumption for both heating and cooling demand. Therefore, the methodology follows three […]
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Metaheuristic and Reinforcement Learning Techniques for Solving the Vehicle Routing Problem: A Literature Review
The Vehicle Routing Problem remains a pivotal challenge in combinatorial optimization, where the objective is to determine optimal routes for a fleet of vehicles serving geographically distributed customers under specific constraints. Over decades, a diverse spectrum of solution methodologies—spanning exact algorithms, heuristics, metaheuristics, and, more recently, machine learning—has emerged. This review critically examines the intersection […]
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A Novel Approach for Optimal Power Smoothing in Floating Offshore Wind Turbine Conversion Chains
The study proposes using battery storage systems to smooth floating offshore wind turbine (FWOT) power. However, FOWT, battery, and grid interact complexly; therefore, power flow should be optimized. This research provides a power management method that manages power flow between the FOWT and battery to smooth power grid injection.
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An innovative Machine Learning model for predicting compressive strength of biobased concretes
Biobased concretes, which incorporate renewable and environmentally friendly components such as plant-based aggregates, offer a promising alternative to conventional materials. However, their widespread adoption is hindered by several challenges such as variability in raw materials, complex interactions between components, the lack of standardized methodologies, and requirement of advanced technics for characterizing and optimizing their mechanical […]
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Lightweight Deep Learning for Photovoltaic Energy Prediction: Optimizing Decarbonization in Winter Houses
This paper proposes an innovative hybrid multivariate deep learning approach to predict photovoltaic (PV) energy production in winter houses, with a focus on lightweight models with low environmental impact. A methodology is developed to assess the carbon footprint of these models, considering training energy consumption, operational CO2 emissions, and energy savings from PV production optimization. […]
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Deterministic Scheduling of Periodic Messages for Low Latency in Cloud RAN
Cloud-RAN (C-RAN) is a cellular network architecture where pro- cessing units, previously attached to antennas, are centralized in data centers. The main challenge in meeting protocol time con- straints is minimizing the latency of periodic messages exchanged between antennas and processing units. We demonstrate that sta- tistical multiplexing introduces significant logical latency due to buffering […]
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Minimizing the total completion time for a class of semi-online single machine scheduling problems
Semi-online single machine scheduling problems with information on jobs’ processing times and the objective to minimize the total completion time are considered. In these problems, a set of jobs arriving over time are to be scheduled on a single machine and their characteristics become known only upon arrival. Some of the studied problems are shown […]
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Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach
The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. In particular, the system’s human sensing capabilities allow for the perception of emotional states to provide a personalized experience. This paper outlines the development of the emotion expression recognition module of the […]
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Calibration améliorée d’un capteur profileur laser pour l’inspection et la reconstruction 3D
Au cours des dernières décennies, l’inspection et la reconstruction tridimensionnelle des canalisations de gaz et d’eau ont nécessité l’utilisation de capteurs de haute précision capables de fonctionner dans des environnements confinés et à faible texture, posant ainsi des défis aux technologies de détection conventionnelles. Cet article présente la conception, tant matérielle que logicielle, d’un capteur […]
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Occupant behavior and impact of the HVAC system on occupant comfort in two-person offices in a mediterranean climate in the south of France
The Mediterranean climate of the south of France is characterized by a long and hot summer season. Under these conditions, it is important to understand the impact of the HVAC system on the comfort of office occupants as well as their adaptive behaviors to create a comfortable indoor thermal environment. The objective of this study […]
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Une approche divisive et interprétable de réduction de dimension pour la prédiction de la RUL
En maintenance, la prédiction de la durée de vie utile restante (RUL) est entravée par la grande dimensionnalité et le manque d’explicabilité. Cette étude propose une approche combinant une méthode innovante de réduction de dimension interprétable, l’IDFC (Interpretable Divisive Feature Clustering), et un modèle LSTM (Long Short-Term Memory) à une couche. L’IDFC s’inspire des algorithmes […]
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