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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Contribution to the distribution supply chain’s performance through the use of digital technologies Case study: cold logistics chain
The development of the Internet of Things (IOT) has made it easier to obtain real-time data related to the supply chain for food distribution’s traceability management. In this study, we suggest digitally transforming the supply chain for seafood distribution. In order to provide better visibility of both traceability data and the parameters that need to […]
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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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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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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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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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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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Multi-Agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles
Autonomous and Guided Vehicles (AGVs) have long been employed in material handling but necessitate significant investments, such as designating specific movement areas. As an alternative, Autonomous and Intelligent Vehicles (AIVs) have gained traction due to their adaptability, intelligence, and capability to handle unexpected obstacles. Yet, challenges like optimizing scheduling and path planning, and managing routing […]
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