Publications
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Cross-Graph Relational Knowledge Distillation with Lightweight ST-GCN for Gait Disorder Recognition
Gait recognition is essential for early diagnosis of movement disorders. The integration of new technologies can enhance the early identification of these conditions. Many current studies use Spatio-Temporal Graph Convolutional Networks (ST-GCN) that depend on skeletal data, however, these models often require substantial memory, which limits their use in clinical settings. This work introduces an […]
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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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nfluence de la culture organisationnelle sur la mobilité durable des étudiant.e.s
La transition vers des modes de vie plus durables ne peut se limiter à des changements individuels. Elle doit être pensée de manière systémique en intégrant l’influence de l’environnements organisationnels. Cette étude explore le lien entre la culture organisationnelle verte des établissements d’enseignement supérieur – comprenant leurs normes, valeurs et pratiques – et le choix […]
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Life Cycle Assessment of Vernacular Construction Techniques: Comparative Analysis of Tilestone Roofing vs. Conventional Systems
Traditional tilestone roofing, commonly used in UNESCO-listed French regions such as the Causses and Ce vennes, may provide notable sustainability benefits, yet comparative studies on roofing remain scarce. Previous research has highlighted the advantages of traditional construction, especially in wall systems: As reported by [1], up to a 91% reduction in embodied emissions with natural […]
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Impact of Successive Layer Spraying of Polymer Binder on Hygroscopic Performance of Earth Walls in Humid Tropical Environments
The use of earth in tropical building is gaining increasing attention due to its natural ability to regulate humidity, which is essential for thermal comfort. This study evaluates the hygroscopic performance of an earth wall stabilized with hemp fibers and a polymer binder, applied through successive layer spraying via gravity-driven impregnation—a technique borrowed from the […]
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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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Automatisation d’un robot pédagogique sous IEC 61499
Cet article explore l’automatisation d’un robot pédagogique Niryo Ned2 en utilisant la norme IEC 61499 et le logiciel EcoStruxure Automation Expert (EAE) de Schneider Electric. L’idée est de développer une plateforme de formation pour les étudiants et les techniciens, adaptée aux exigences de programmation d’automatisme et de robotique de l’Industrie du futur. La plateforme comprend […]