Publications
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Des Jumeaux Numériques d’Enseignement : entre instrument et finalité de l’acte éducatif
Dans la continuité des Jumeaux Numériques (JN) industriels, la formation est devenue un champ d’application visé par le développement de Jumeaux Numériques d’Enseignement (JNE). À partir d’une étude sur l’introduction d’un JNE dans une formation d’ingénieurs, cet article se propose de comprendre dans quelle mesure le JNE peut être instrumenté comme objet pédagogique dans une […]
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Effects of Thermal Activation on Mechanical Performance and Sustainability of Slag-Based Geopolymers
Ground granulated blast furnace slag (GBFS)-based geopolymers represent a viable binder system that combines mechanical efficiency with a significantly lower carbon footprint when compared to conventional Portland cement. This work examines how thermal curing between 20 ◦C and 80 ◦C affects setting time, mechanical performance, shrinkage, and porosity of GBFS-based geopolymers. Curing at 40 ◦C […]
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Dynamic mechanical analysis of shape memory polymers: thermomechanical behavior and influence of thermal stimuli
This study explores the thermomechanical properties of polymethacrylate-based shape memory polymers (SMPs), focusing on hot water as a thermal stimulus for shape recovery. Using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), the research evaluates thermal transitions, viscoelastic behavior, and energy dissipation. DSC identified a glass transition temperature (Tg) of 67 °C, critical for […]
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AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognition
Context-aware emotion recognition (CAER) enhances affective computing in real-world scenarios, but traditional methods often suffer from context bias-spurious correlation between background context and emotion labels (e.g. associating “garden” with “happy”). In this paper, we propose AGCD-Net, an Attention Guided Context Debiasing model that introduces Hybrid ConvNeXt, a novel convolutional encoder that extends the ConvNeXt backbone […]
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MANAGING READINESS FOR CHANGE TO UNLOCK PROJECT PERFORMANCE -A quantitative study at the cross-section of change- and project management practices-
This study explores the influence of perceptions on individual readiness for change as an explanatory factor in the field of organizational behavior and renewed human resource practices. Hereby, this study offers a fresh perspective on the comparative significance of culture versus nationality as explanatory constructs to further explore the realm of diversity-, equity- and inclusion […]
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Smart Fleet Management for Shared Micro-mobility: Balanced demand, Redistribution and Charging via Deep Reinforcement Learning
Shared electric micro-mobility, as an emerging mode of urban transportation, has been booming worldwide in recent years. Al though it provides sustainable, eco-friendly, and cost-effective mobility, it also faces several challenges, particularly due to existing inefficient fleet management strategies. These typically rely on fixed redistribution schedules that fail to adapt to highly dynamic user demand […]
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Implementation of an AI-driven dynamic control system to optimize excess photovoltaic energy management in grid-connected sustainable BIPV
This study proposes an integrated approach for optimizing grid-connected photovoltaic (PV) systems through AI-based forecasting and a Dynamic Automatic Control System (DACS). Using Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN-LSTM model, we predict PV production and building energy demand. The CNN-LSTM model achieved the best performance for PV forecasting […]
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An enhanced genetic algorithm for optimized task allocation and planning in heterogeneous multi-robot systems
Efficient task allocation and path planning in heterogeneous multi-robot systems (MRS) remains a significant challenge in industrial inspection contexts, particularly when robots exhibit diverse sensing capabilities and must operate across spatially distributed sites. To address the limitations of exact methods and conventional heuristics, we propose a novel two-phase enhanced genetic algorithm (EGA) tailored for capability-constrained […]
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Automatic learning of physics-constrained space heterogeneous PDEs for defect identification in slender mechanical structures
This contribution explores the opportunities offered by physics-constrained automatic PDE learning, see for example [1], for identifying defects in mechanical structures. In this framework, defects are seen as limited zones in a domain where the constitutive law is different from that of the healthy material. One challenge in defect localization is that the impact of […]
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Multi-LOD generative approach for multi-objective sustainability optimization from the early stages of building design
Given the urgency of reducing the buildings’ environmental impact, this article focuses on optimizing sustainability from the earliest design phases, when decisions have the greatest influence. To address the challenges posed by the coarse granularity of digital models during the sketching phase and the often-conflicting nature of sustainability criteria, a generative workflow is proposed. This […]
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Channel Estimation for OFDM Systems Over Doubly Selective Channels Based on CEHNet
In dynamic scenarios, time-frequency doubly selective channels challenge accurate estimation. Deep learning based method emerges as a promising way by leveraging temporal correlation and local time-frequency features characterized by wireless channels. To enhance adaptability in dynamic channels with fewer pilots, this letter proposes a novel channel estimation algorithm based on a channel-enhanced deep Horblock network […]
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A Divisive Unsupervised Feature Selection Approach for Explainable Remaining Useful Life Prediction
Predicting the Remaining Useful Life (RUL) in maintenance often encounters challenges such as high dimensionality, feature redundancy, and limited explainability. This paper presents a novel approach that combines Interpretable Divisive Feature Clustering (IDFC) with Long Short-Term Memory (LSTM) networks. The IDFC algorithm leverages the strengths of variable clustering methods (VARCLUS) and the Clustering of Variables […]