Publications
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MHeedra: Putting Duplication-Enabled Task Scheduling Within Heterogeneous Multi-User Edge-Cloud Platforms to Work
Meeting task performance requirements within edge-cloud computing platforms is difficult due to heterogeneous processing, transmission capabilities, and the multiplicity of optimization opportunities. Edge-cloud platforms fill the computing continuum gap and alleviate data-locality performance issues from offloading tasks. Indeed, additional computing resources disseminated across the network but close to the data source help decouple the inherent […]
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Optimization of Maintenance, Production Planning, and Quality Control in Lithium-ion Battery Manufacturing Line
In the context of environmental challenges related to global warming, lithium-ion batteries have emerged as a strate- gic solution to replace thermal energy sources in vehicles, while also serving as an efficient means of energy storage. However, this transition towards electromobility presents significant challenges for the battery industry, particularly with regard to industrialization and the […]
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Human-Humanoid collaboration in manufacturing opportunities and challenges in the context of industry 5.0
The introduction of humanoid robots in industry represents an important aspect of the industry 5.0 enhance resilience of productin and represents a real change in the manner of dealing with the technology assistance for human operators, where the smart system will be able to execute similar tasks as the humans. This paper presents a concise […]
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Potential of Generative Artificial Intelligence in Knowledge-Based Predictive Maintenance for Aircraft Engines
Predictive maintenance based on remaining useful life (RUL) estimation is widely recognized as a promising strategy for monitoring the health of critical systems such as aircraft engines, anticipating failures, and optimizing maintenance planning. A variety of approaches have been proposed in the literature, including data-driven, physics-based, and knowledgebased methods. Among them, deep learning-based methods have […]
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The CG-MER Dyadic Multimodal Dataset for Spontaneous French Conversations: Annotation, Analysis and Assessment Benchmark
Emotion recognition is crucial for enhancing human-computer interaction systems. However, the development of robust methodologies for French emotion recognition is hindered by the scarcity of labeled, interactive multimodal datasets. In this work, we outline the acquisition and annotation procedures and provide an evaluation benchmark for the Card Game-based Multimodal Emotion Recognition (CG-MER) dataset that we […]
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Combining Client-Based Anomaly Detection and Federated Learning for Energy Forecasting in Smart Buildings
In today’s interconnected world, energy consumption forecasting faces challenges due to client-side anomalies in time-series data. Federated Learning (FL) offers a decentralized solution by forecasting without directly accessing user data. However, the effectiveness of the global model can decline if local anomalies are not properly managed. We propose our lightweight framework EIF-FL: Elliptic envelope and […]
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A Stochastic Model for the Bike-sharing
Bike Sharing Systems (BSS) offer a sustainable and flexible solution to urban mobility, but their rapid growth as a viable and popular transportation alternative has exposed major challenges. Due to asymmetric user flows throughout the day they suffer from chronic imbalances in bike distribution, badly impacting both the system reliability and user satisfaction. In this […]
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Improving Image-Based Tool Detection in Industrial Workstations using Data Augmentation
Within the framework of Industry 5.0, affordances enable intuitive and adaptive interactions between operators and their industrial work environments. Accurately perceiving these affordances enhances overall production performance, safety, and operator effectiveness. This paper focuses on the initial step of a larger affordance characterization pipeline: detecting tools used by operators during manual assembly tasks. To address […]
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Synthetic Data-Driven Augmentation for Precise 6-DoF Pose Estimation of Building Components in Automated Facility Inspections
This paper tackles the challenge of automating facility inspections by detecting building components, estimating their six-degree-of-freedom (6-DoF) poses (position and orienta tion), and comparing these estimations to Building Information Modeling (BIM) ground truth data. Vision based Deep learn ing methods offer promising results in pose estimation. They rely heavily on large annotated image datasets for […]
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Evaluating Robustness of 3D Gaussian Splatting–Based 6D Camera Pose Refinement Under Degraded Conditions for Lightly Textured Industrial Synthetic Objects
In this paper, 6D camera pose refinement is explored using 3D Gaussian Splatting (3DGS) on lightly textured industrial object datasets. The study employs datasets generated with Unity 3D rendering software, featuring objects such as a bicycle, MiR robot, Tiago robot, and UR robotic arm, each captured with ground-truth intrinsic and extrinsic camera parameters. A 3DGS […]
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Leveraging digital twin and dynamic scheduling for enhanced human-robot collaboration
Industry 5.0 represents a paradigm shift toward human-centric, resilient, and sustainable production systems. At the core of this transformation lies digital twins, which enable predictive and prescriptive analytics in real time, improving decision-making capabilities such as visibility, transparency, and collaboration. By integrating advanced AI algorithms for data interpretation and facilitating seamless human-machine interactions, digital twins […]
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L’innovation juridique à la croisée du droit et de l’innovation : réflexions sur un champ émergent
L’innovation et le droit sont deux domaines étroitement liés, s’influençant mutuellement dès lors qu’il s’agit de réguler, protéger et encourager la création de nouvelles idées, normes juridiques, produits, services ou technologies. S’inscrivant dans le prolongement de l’approche Law & Management, l’innovation juridique constitue un champ de recherche émergent qui tente d’appréhender les influences réciproques entre […]
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