Publications
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Predictive maintenance approaches in industry 4.0: A systematic literature review
The emergence of Industry 4.0 has heralded notable progress in manufacturing processes, utilizing sophisticated sensing and data analytics technologies to maximize efficiency. A vital component within this model is predictive maintenance, which is instrumental in ensuring the dependability and readiness of production systems. Nonetheless, the heterogeneous characteristics of industrial data present obstacles in realizing effective […]
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DLSTM-SCM– A Dynamic LSTM-Based Framework for Smart Supply Chain Management
In the retail industry, SCM holds significant importance as it ensures the efficient movement of goods from suppliers to customers. In this intricate and fast-paced environment, the availability of accurate information and data is crucial. The purpose of this paper is to develop a framework that enhances forecasting accuracy and efficiency in supply chain operations […]
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Finite-dimensional adaptive observer design for reaction-diffusion system
A new finite dimensional adaptive observer is proposed for a class of linear parabolic systems. The observer is based on the modal decomposition approach and uses a classical persistent excitation condition to ensure exponential convergence of both states and parameter estimation errors to zero.
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From Simulation to Digital Twins, the Case of Internet of Things Research and Tools
The digitalisation of the environment surrounding human beings in their daily life is a major challenge facing today’s technological progress. Building digital replicas of humans and systems help us to understand our environment, to anticipate its variations and to better explain its behaviour. Research in digital twins is continuously developing due to the various benefits […]
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A Collaborative Real-Time Object Detection and Data Association Framework for Autonomous Robots Using Federated Graph Neural Network
Autonomous robotics require secure and decentralized decision-making systems that ensure data privacy and computational efficiency, especially in critical areas. Current centralized models or human input are associated with data breaches and security vulnerabilities. To counter these, we propose CoRODDA, a dedicated framework combining federated learning and graph neural networks. It enhances object detection and data […]
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Securing Autonomous Vehicles: Fundamentals, Challenges, and Perspectives
This paper introduces a comprehensive methodology aimed at enhancing security and immunity in automotive networks, placing a primary focus on the detection, prediction, and forecasting of errors in autonomous vehicles. Conventional approaches to vehicle cybersecurity often struggle to keep pace with evolving threats and provide effective error detection mechanisms. Our proposed methodology seeks to bridge […]
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Securing Autonomous Vehicles
This talk delves into the fundamentals of security for autonomous vehicles, exploring the challenges and existing solutions in this rapidly evolving field. It discusses the limitations of current security measures and introduces the application of formal methods to model safety and security in autonomous vehicles. The talk outlines the process of applying formal methods to […]
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Distributed Transactive Energy Management in Microgrids based on Blockchain
While the Internet of Energy (IoE) introduced advanced collaborative management methods through real-time monitoring and demand response programs, smart grids still grapple with challenges related to central governance, ineffective information aggregation, and privacy issues. These problems create significant hurdles in smart grid management, particularly with the high penetration of distributed energy resources (DERs). In this […]
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Towards Hybrid Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach
This article introduces a novel Remaining Useful Life (RUL) estimation method using Machine Learning techniques, guided by domain knowledge, and applied to a dataset of aircraft engines (C-MAPSS). Predictive maintenance, or prognostics, offers the opportunity to predict the lifespan of aircraft engines, thereby reducing costs, minimizing breakdowns, and ensuring their reliability. While existing solutions in […]
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BootBOGS: Hands-on optimizing Grid Search in hyperparameter tuning of MLP
Neural networks are widely used in the literature in a variety of fields and for a large number of applications. A major challenge in their use is the need to identify and process hyperparametric values. Grid Search is a widely used technique for meeting this task. It systematically searches for values in a predefined range […]
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Very short-term prediction of photovoltaic energy in the winter building for an automatic energy management system
In the context of the energy transition, renewable energies have an important role to play. This is particularly true of photovoltaic (PV) energy. The use of PV energy in buildings is becoming increasingly common nowadays. Buildings integrated PV (BIPV) represent a major advantage in this respect, thanks to their high PV energy harvesting capacity. However, […]
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Optimal Deployment of Fog-Based Solution for Connected Devices in Smart Factory
Internet of Things (IoT) is commonly used in Industry-4.0/5.0, but it generates an excessive amount of data that affects quality of service (QoS). To address this challenge, Fog-based architectures have emerged as an alternative solution. However, its deployment can be challenging, particularly when dealing with mobile connected devices like robots. To optimize the deployment of […]
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