Publications
-
Optimizing Shapley Value for Client Valuation in Federated Learning through Enhanced GTG-Shapley
In the ever-evolving realm of federated learning (FL), the question of data worth resonates with newfound urgency across organizations and individuals. In the dynamic FL ecosystem, where data resides across distributed nodes, evaluating the value of each client’s data is paramount. The evaluation mechanism helps to understand individual contributions to the overall process and incentivizes […]
-
Enhancing Explainability in Predictive Maintenance: Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness
In predictive maintenance, the complexity of the data often requires the use of Deep Learning models. These models, called ”black boxes”, have proved their worth in predicting the Remaining Useful Life (RUL) of industrial machines . However, the inherent opacity of these models requires the incorporation of post-hoc explanation methods to enhance transparency. The quality […]
-
GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks
The increasing growth of the Internet of Things (IoT) with the diverse and dynamic nature of devices made detecting and preventing network intrusions more important and challenging. As new and sophisticated cyber-attacks are being used, there is an increasing need for advanced intrusion detection systems that can adapt to emerging threats. The majority of existing […]
-
An intelligent mechanism for energy consumption scheduling in smart buildings
In recent years, the incorporation of sensing technology into residential buildings has given rise to the concept of ”smart buildings”, aimed at enhancing resident comfort. These buildings are typically part of interconnected neighborhoods sharing common energy sources, which makes the energy consumption a critical consideration in decision-making processes. Consequently, optimizing energy usage in smart buildings […]
-
Multi-Task Learning for PBFT Optimisation in permissioned Blockchains
Finance, supply chain, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT) which tolerates up […]
-
Analyse de sensibilité du modèle énergétique d’un bâtiment pédagogique constitué de conteneurs maritimes
Les modèles énergétiques, bien que très utiles pour prédire le comportement physique du bâtiment, comportent des incertitudes (paramètres inconnus, approximations…). Ces incertitudes ont un impact sur la fiabilité des prédictions, qu’il est possible de limiter via la calibration du modèle. Nous présentons dans cet article une modélisation énergétique d’un bâtiment pédagogique construit à partir de […]
-
Maximizing the number of satisfied charging demands of electric vehicles on identical chargers
This paper addresses the electric vehicle charging scheduling problem in a charging station with a limited overall power capacity and a limited number of chargers. Electric vehicle drivers submit their charging demands. Given the limited resources, these charging demands are either accepted or rejected and accepted demands must be satisfied. The objective of the scheduler […]
-
Bus routing optimisation: a case study for the Toulouse metropolitan area
This paper investigates the efficiency evaluation of a public transport using an analysis of Origin-Destination matrices (mOD). The use of a trip-chaining method on the automatically collected transport data provides a realistic and accurate representation of traffic flows characterized by mOD. The introduction of a critical walking distance and an user flow at bus stop […]
-
Simultaneous scheduling of production and maintenance in a flexible job-shop workshop
This paper proposes an integer linear program for joint and simultaneous scheduling of production and predictive maintenance tasks based on degradation prediction. The objective is to find a scheduling scheme that minimizes the total execution time while guaranteeing that the state of the machines is maintained below a predefined degradation threshold. Experiments are performed on […]
-
Exploring the Capacitated Vehicle Routing Problem using the power of Machine Learning : A Literature Review
The Capacitated Vehicle Routing Problem is a classic logistics optimization challenge for which numerous approaches are employed to address it. A prevalent strategy involves employing machine learning for predicting demand, leveraging historical data to accurately forecast customer needs. Furthermore, reinforcement learning techniques are employed to optimize vehicle routes, adapting them dynamically in response to changing […]
-
Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward
Within urban mobility ecosystem, Mobility-as-a-Service (MaaS) has come up as a promising approach to promote sustainable modes of transport and increase the attractiveness of public and shared multimodal mobility. It aims to become a viable alternative to personal cars for door-to-door trips. The long-term objective of MaaS is to change the people’s travel behaviour by […]
-
Flexible job-shop problem with predictive maintenance planning using genetic algorithm
The most common disruptions and emerging challenges that manufacturing systems frequently encounter include the arrival of new orders, last-minute order cancellations, unforeseen machine breakdowns, and alterations in due dates. To effectively respond to these challenges, production schedules are continually adjusted by implementing real-time rescheduling mechanisms that rely on up-to-the-minute data from the shop floor. This […]
Loading…
Erreur : tout le contenu a été chargé.