Publications
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RUL Prediction with Encoding and Spatial-Temporal Deep Neural Networks
The objective of this paper is to design and develop an approach to estimate the Remaining Useful Life (RUL) of an industrial equipment evolving in a Cyber-Physical System (CPS). To do so, this work aims to predict failures and malfunctions of an industrial equipment, as well as evaluating all the main underlying causes. The system […]
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Réseau antagoniste génératif pour la fusion spatio-temporelle d’images satellitaires multi-spectrales
Résumé – Dans cet article, nous étudions la fusion spatio-temporelle d’une série temporelle d’images multi-spectrales avec une série temporelle d’images hyper-spectrales. Nous proposons pour cela une nouvelle approche fondée sur un réseau antagoniste génératif (GAN). Notre contribution principale réside dans le fait que le GAN prend en entrée des images satellitaires plutôt que du bruit. […]
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Solution de génération de formes 2D d’objets basée sur des connaissances préalables pour la cartographie sémantique
Dans ce travail, nous proposons une solution de cartographie sémantique en temps réel basée sur les données RGBD. Nous nous focalisons sur la proposition d’une approche d’association pour générer les formes 2D des objets sémantiques en utilisant des connaissances préalables. Notre approche est évaluée dans un environnements bureautique à l’aide du robot mobile MIR. Les […]
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Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of parameters that significantly contribute to the model’s memory footprint. Consequently, it becomes […]
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A systematic review of federated learning: Challenges, aggregation methods, and development tools
Since its inception in 2016, federated learning has evolved into a highly promising decentral-ized machine learning approach, facilitating collaborative model training across numerous devices while ensuring data privacy. This survey paper offers an exhaustive and systematic review of federated learning, emphasizing its categories, challenges, aggregation techniques, and associated development tools. To start, we outline our […]
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Energy Management, Control, and Operations in Smart Grids: Leveraging Blockchain Technology for Enhanced Solutions
As smart grids advance rapidly, they are evolving along two primary trajectories: (1) digitalization through the incorporation of Internet of Things (IoT) technology and intelligent control, and (2) decentralization by leveraging small-scale distributed energy sources for control. However, these developments also introduce complexities in the functioning, management, and control of smart grids. Consequently, there is […]
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Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro
The cement industry is a major energy consumer, with most of its costs associated with fuel and energy requirements. While traditional thermal power plants generate electricity, they are both harmful and inefficient. In this study, battery depth of discharge (DOD) is evaluated for four different battery technologies in the context of the cement industry. The […]
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PUF-based mutual authentication and session key establishment protocol for IoT devices
The Internet of things (IoT) is an indispensable part of our daily lives, bringing us many conveniences, including e-commerce and m-commerce services. Unfortunately, IoT networks suffer from several security issues, such as privacy, access control, and authentication. However, due to the limited computation resources, remote authentication between IoT devices and servers is vulnerable to being […]
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FedGA-Meta: Federated Learning Framework using Genetic Algorithms and Meta-Learning for Aggregation in Industrial Cyber- Physical Systems
In Industry 4.0, factories encounter significant challenges in making informed decisions to maintain or enhance their industry standing. By utilizing machine learning (ML), they can improve product quality, decrease production downtime, and boost operational efficiency. However, acquiring datasets with sufficient variation and diversity to train a robust neural network centrally is a challenge within the […]
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Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network
The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to […]
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Problem-based learning analysis using capability approach
Based on research conducted with undergraduated students in French Engineering School, we propose to highlight the conditions under which problem-based learning (PBL) can be favorable to student’s learning based on the method of analysis resulting from the capability approach (Sen, 2001). PBL appears in the literature to be an adapted way to tend to, among […]
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