Publications
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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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Travail de garde et enjeux d’apprentissage chez les bergers et éleveurs pastoraux : une approche didactique professionnelle de l’expérience pastorale
Cet article s’intéresse aux savoirs issus de l’expérience des bergers qui ont la charge de garder les troupeaux en estive. Partant du constat d’un “malaise professionnel” principalement incarné dans la relation difficile aux éleveurs qui leur confient leurs bêtes et ancré dans le manque de structuration de la profession de berger, cette contribution s’appuie sur […]
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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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EEG-based Emotion Recognition Using Modified Covariance and Ensemble Classifiers
The Electroencephalography (EEG)-based precise emotion identification is one of the most challenging tasks in pattern recognition. In this paper, an innovative EEG signal processing method is devised for an automated emotion identification. The Symlets-4 filters based « Multi Scale Principal Component Analysis » (MSPCA) is used to denoise and reduce the raw signal’s dimension. Onward, the « Modified […]
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An overview on human-centred technologies, measurements and optimisation in assembly systems.
This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly […]
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HEALTrust: Enhancing Healthcare Data Integrity and Privacy through Blockchain-Enabled Exchange Systems
Invited talk to the event: Vers une santé digitale en Algérie, Convergence des compétences informatiques et cliniques, 2023, Tlemcen, Université de Tlemcen, Algeria. The talk is about using BC in healthcare systems
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Benchmarking OpenStack for edge computing applications
In this paper, we focus on the identification and evaluation of performance factors of OpenStack-based edge computing platforms. Such infrastructure relies on the deployment of additional computing resources close to the data source, to alleviate low throughput, latencies and network congestion. While cloud data centres offer numerous compute-intensive processing units, the edge layer leverages heterogeneous, […]
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A Comparative Study of Blockchain Development Platforms
Blockchain technology is nowadays applied to several domains, essentially finance, healthcare, supply chain, and digital identity management. For enterprises to adopt a blockchain solution, many existing platforms facilitate the development process rather than implementing the system from scratch. Each platform has its own characteristics, capabilities, and limitations. Thus, several factors should be considered, including the […]
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