Publications
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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 […]
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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 […]
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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 […]
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Online human motion analysis in industrial context: A review
Human motion analysis plays a crucial role in industry 4.0 and, more recently, in industry 5.0 where humancentered applications are becoming increasingly important, demonstrating its potential for enhancing safety, ergonomics and productivity. Considering this opportunity, an increasing number of studies are proposing works on the analysis of human motion in an industrial context, taking advantage […]
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Classification of whispering gallery modes for cladded systems
A classification of whispering gallery modes has been proposed between three types : core modes, cladding modes and composed modes. While core modes or cladding modes are interesting to generate with a sensor purpose, composed modes propagate in both core and coating and therefore should be avoided. In this paper, a theoretical and numerical study […]
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Determinant Factors of Teaching Performance in COVID-19 Context
COVID-19 pandemic still impact higher education system, stakeholders and environment all around the world. Students, teachers, academic institutions and education decision makers were shocked by an atypical new context they promptly put in face, asking drastic change in behavior and procedures at individual, familial and institutional levels. Full lockdown and closing campuses enforced students and […]
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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 […]
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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 […]
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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 […]
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RPA-Code for Secure Binary Sequence Generation from Graph-Based Scanning
This article introduces a novel method for generating random bi- nary sequences from Random Polar Angles (RPA). These sequences can be derived from an image, akin to QR-Codes, making them suitable for cryptographic applications and information coding sys- tems. The proposed method allows the generation of multiple codes using the same image. It is based […]
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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 […]
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Deep Learning Based on EfficientNet for Multiorgan Segmentation of Thoracic Structures on a 0.35 T MR-Linac Radiation Therapy System
The advent of the 0.35 T MR-Linac (MRIdian, ViewRay) system in radiation therapy allows precise tumor targeting for moving lesions. However, the lack of an automatic volume segmentation function in the MR-Linac’s treatment planning system poses a challenge. In this paper, we propose a deep-learning-based multiorgan segmentation approach for the thoracic region, using EfficientNet as […]
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