Publications
-
Towards a Modular Deep Reinforcement Learning Digital-Twins Framework: A Step towards optimal RMS control
This paper proposes a modular deep reinforcement learning framework integrated with digital twin technology for optimizing the control of Reconfigurable Manufacturing Systems (RMS). The framework employs hierarchical deep reinforcement learning agents for scheduling and reconfiguration decisions across decentralized digital twins of individual Reconfigurable Machine Tools (RMT). The digital twins enable real-time monitoring, simulation, and visualization […]
-
Boosting Regression Assistive Predictive Maintenance of the Aircraft Engine with Random-Sampling Based Class Balancing
This study presents the development of a data-driven predictive maintenance model in the context of industry 4.0. The solution is based on a novel hybridization of Remaining Useful Life (RUL) gener- ation, Min-Max normalization, random-sampling based class balancing, and XGBoost regressor. The applicability is tested using the NASA’s C-MAPSS dataset, which contains aircraft engine simulation […]
-
EEG-based Schizophrenia Classification using Penalized Sequential Dictionary Learning in the Context of Mobile Healthcare
Mobile healthcare is an appealing approach based on the Internet of Medical Things (IoMT) and cloud computing. It can lead to unobstructed, economical, and patient-centric healthcare solutions. The key performance indicators of such systems are dimensionality reduction, computational effective- ness, low latency, and accuracy. In this context, a novel approach is devised for EEG-based schizophrenia, […]
-
A Systematic Review of Predictive Maintenance and Production Scheduling Methodologies with PRISMA Approach
Predictive maintenance has been considered fundamental in industrial applications over the last few years. It contributes to improving reliability, availability, and maintainability of the systems and decreasing production efficiency in manufacturing plants. This article aims to explore the integration of predictive maintenance into production scheduling through a systematic review of literature. The review includes 165 […]
-
Développement d’une nouvelle méthode numérique basée sur l’arithmétique des intervalles pour la reconstruction de matrice Origine/Destination
La matrice Origine/Destination (OD) revêt une importance capitale dans le dimensionnement et la planification d’un système de transport. L’estimation des matrices OD est principalement réalisée par les gestionnaires de réseau à partir de données issues d’enquêtes. Le déploiement massif d’outils numériques permet l’acquisition automatique de données en temps réel comme la billettique. Il génère une […]
-
Resource-Constrained EXtended Reality Operated With Digital Twin in Industrial Internet of Things
EXtended Reality (XR) alongside the Digital Twin (DT) in Industrial Internet of Things (IIoT) emerges as a promising next-generation technology. Its diverse applications hod the potential to revolutionize multiple facets of Industry 4.0 and serve as a cornerstone for the rise of Industry 5.0. However, current systems are still not effective in providing a high-quality […]
-
Supply Chain 5.0: Vision, Challenges, and Perspectives
The recent technological advancements have transformed modern supply chains into complex networks. Consequently, today’s supply chain systems are facing several challenges, including limited visibility in both upstream and downstream supply chains, lack of trust among the different stakeholders, as well as transparency and traceability. The application of the Internet of Things can enable companies to […]
-
Optimal placement and sizing of distributed generation for power factor improvement
This study employs the Forward-Backward Sweep (FBS) method in conjunction with the Sea Horse Optimization (SHO) algorithm to optimize the sizing and placement of Distributed Generators (DGs) in a distribution network for the intended case study. Through the integration of MATPOWER toolbox in MATLAB and Torrit software, the network is systematically evaluated under four scenarios […]
-
Advancing Manufacturing Efficiency: Multi-Objective Optimization in the Industry 5.0 Era
This paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi- Objective Optimisation (MOO), which is […]
-
Synthetic datasets for 6D Pose Estimation of Industrial Objects: Framework, Benchmark and Guidelines
This paper falls within the industry 4.0 and tackles the challenging issue of maintaining the Digital Twin of a manufacturing warehouse up-to-date by detecting industrial objects and estimating their pose in 3D, based on the perception capabilities of the robots moving all along the physical environment. Deep learning approaches are interesting alternatives and offer relevant […]
-
Industrial Object Detection Leveraging Synthetic Data for Training Deep Learning Models
The increasing adoption of synthetic training data has emerged as a promising solution in various domains, owing to its ability to provide accurately labeled datasets at a lower cost compared to manually annotated real-world data. In this study, we explore the utilization of synthetic data for training deep learning models in the field of industrial […]
-
Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization
Industry 5.0 ushers in a new era of manufacturing, with the integration of sophisticated technology and human know-how, emphasising durable, customised and resilient industrial techniques. Multi-Objective Optimization (MOO) becomes a crucial instrument for tackling the complicated balance between efficiency, cost, quality, and sustainability. This article introduces a new method combining mathematics and swarm intelligence to […]
Chargement en cours…
Erreur : tout le contenu a été chargé.