Publications
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Double-Layer Soft Data Fusion for Indoor Robot WiFi-Visual Localization
This paper presents a novel WiFi-Visual data fusion method for indoor robot (TIAGO++) localization. Long-term follow-up experiments show that this method can use 10 WiFi samples and 4 low-resolution images ($58 times 58$ in pixels) to localize an indoor robot with an average error distance of about 1.32 meters 3 months (or 1.7 meters 7 […]
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Chemical servoing with hexapod robot for gas source localization and YOLO‑based visual defect detection
Robotic systems have the ability to perform industrial inspection tasks through the integration of visual and chemical servoing functions. A commonly used system is the image-based servoing system. This study presents an innovative approach that merges chemical servoing with visual defect detection in industrial settings using a hexapod robot. The main goal is to develop […]
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Etude de cas : L’optimisation d’une unité industrielle d’osmose inverse (OI) via l’intégration d’un jumeau numérique (JN) et de la réalité augmentée (RA).
L’osmose inverse (OI) est un procédé essentiel pour la production d’eau ultrapure dans divers secteurs industriels, mais son optimisation se heurte à des défis significatifs : complexité des paramètres physico-chimiques, maintenance prédictive des membranes, exigences de traçabilité et risques d’erreurs humaines [1-5]. L’émergence des technologies de l’Industrie 4.0, notamment le jumeau numérique (JN) [6, 8] […]
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Role tension and perceived leadership influence on middle managers’ transformational leadership
Objectives & Literature Middle managers in the nuclear sector, as in other organizations, are key players in implementing policies and strategy. They are frequently exposed to paradoxical leadership styles and demands (Berti et Simpson, 2021). It gives rise to role tensions (i.e., role conflict, organizational cynicism, or cognitive dissonance) for middle managers (Razouk et Quéméner, […]
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Advanced Multi-Model Prediction of Aircraft Engine Remaining Useful Life with Random Sampling-Based Class Balancing and Voting-Based Features Selection
In Industry 4.0, predictive maintenance for critical systems like aircraft engines relies on accurate Remaining Useful Life (RUL) estimation to prevent unexpected failures and optimize maintenance schedules. However, existing models face several limitations that hinder their effectiveness in real-world applications. Common challenges include data imbalance, which can lead to biased predictions; suboptimal feature selection, which […]
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The Augmented Perception: an emerging approach toward resilient manufacturing systems involving robotic agents and digital twin.
This paper addresses the resilience of Industry 5.0 Manufacturing Systems (MS) with mobile robotic agents, focusing on robustness (handling disruptions while maintaining production) and flexibility (adapting to reconfigurations). We propose Augmented Perception (AP), a Digital Twin-based approach that enhances robot perception by integrating virtual elements in the map of the robot. Three use cases, in […]
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Chemical Servoing with Hexapod Robot for Gas Source Localization and YOLO-based Visual Defect Detection
Robotic systems have the ability to perform industrial inspection tasks through the integration of visual and chemical servoing functions. A commonly used system is the image-based servoing system. This study presents an innovative approach that merges chemical servoing with visual defect detection in industrial settings using a hexapod robot. The main goal is to develop […]
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Plastic optical fiber sensors for mooring lines monitoring in floating wind turbines: A reliability study of OTDR measurement
This study investigates the use of POF (Plastic Optical Fibers) for mooring lines monitoring in floating wind turbines. Focusing on their mechanical adaptability and optical performance in marine environments. Optical attenuation measurement are employed to determine integrity of POFs under mechanical stresses such as tension and torsion, as well as during prolonged water immersion at […]
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Integrating BIM with Lean Principles for Enhanced Decision-making: Optimizing Insulation Material Selection in Sustainable Construction Project
This study addresses the construction sector’s growing need for improved decisionmaking and reduced carbon emissions by integrating Lean principles into Building Information Modeling (BIM). A decision-support tool was developed using Python and RStudio to enhance stakeholder efficiency, reduce errors, and streamline communication. The tool combines Set-Based Design, Choosing By Advantages, and Big Room methods with […]
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Extensive development of a Bayesian calibration approach for building energy models using an innovative case study: a shipping container building.
The building sector is responsible for almost a third of global energy consumption and a quarter of CO2 emissions. Innovative architectural designs that promote the reusability of raw materials, such as shipping container architecture, can help to reduce construction’s environmental impact. By creating physical models to analyze energy consumption, we can develop practical tools to […]
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Electrochemical and thermodynamic modeling of PEM electrolyzer performance: A comparative study with and without diffusion overpotential
In this paper, a mathematical model is developed, combining thermodynamic and electrochemical models. It was used to study the effect of operating parameters, such as membrane thickness and operating temperature on the performance of PEM electrolzer cell. The effect of ion diffusion through the membrane on the cell potential was also studied. In addition, an […]
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Predicting wind turbines faults using Multi-Objective Genetic Programming
Wind turbines are a key component of renewable energy, converting wind into electricity with minimal environmental impact. Ensuring their continuous operation is crucial for maximizing energy production and reducing costly downtimes. To extend their operational lifespan, proactive maintenance strategies that predict and address potential faults are essential. While Machine Learning (ML) and Deep Learning (DL) […]
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