Publications
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Natural Fiber Composites for Sustainable Model Rocketry: Bamboo and Jute as Alternatives to Fiberglass
The search for sustainable alternatives to synthetic composites has become increasingly relevant in aerospace engineering education and student rocketry. Fiberglass is widely used for rocket fuselages due to its favorable balance of performance and cost, but it is energy-intensive, non-biodegradable, and environmentally burdensome. This study provides the first demonstration of natural fiber composites applied to […]
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Failure analysis of putty‑composite patch repair system for wall loss damaged pipelines
Wrapping glass fiber-reinforced polymer (GFRP) composites around damaged pipes are effective method and accepted in practices. The main aim of this paper is to propose new composite repair geometry to optimize the wrap repair thickness for cost-effective repair system using finite using finite element analysis (FEA). Two different repair geometries were considered: one where the […]
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Graph-based federated learning approach for intrusion detection in IoT networks
Internet of Things (IoT) networks face increasing cyber threats that require collaborative intrusion detection across distributed environments. Existing federated learning approaches for intrusion detection have critical architectural and methodological limitations: traditional federated approaches using deep learning methods like LSTM and CNN cannot capture structural patterns as these architectures are not designed to model network graph […]
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Reconstruction-Based Methods for Multivariate Time Series Anomaly Detection : A Review and Taxonomy
Reconstruction-based methods have become a central paradigm for unsupervised multivariate time series anomaly detection (MTSAD), especially in the context of cyber-physical and industrial control systems. Their ability to learn normal patterns and detect deviations without supervision makes them highly suitable for safety-critical environments. In this work, we conduct a structured review of recent deep learning […]
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The CG-MER dyadicmultimodal dataset for spontaneous french conversations: annotation, analysis and assessment benchmark
Emotion recognition is crucial for enhancing human-computer interaction systems. However, the development of robust methodologies for French emotion recognition is hindered by the scarcity of labeled, interactive multimodal datasets. In this work, we outline the acquisition and annotation procedures and provide an evaluation benchmark for the Card Game-based Multimodal EmotionRecognition (CG-MER)dataset thatwedesigned to capture spontaneous […]
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MHeedra: Putting Duplication-Enabled Task Scheduling Within Heterogeneous Multi-User Edge-Cloud Platforms to Work
Meeting task performance requirements within edge-cloud computing platforms is difficult due to heterogeneous processing, transmission capabilities, and the multiplicity of optimization opportunities. Edge-cloud platforms fill the computing continuum gap and alleviate data-locality performance issues from offloading tasks. Indeed, additional computing resources disseminated across the network but close to the data source help decouple the inherent […]
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Optimization of Maintenance, Production Planning, and Quality Control in Lithium-ion Battery Manufacturing Line
In the context of environmental challenges related to global warming, lithium-ion batteries have emerged as a strate- gic solution to replace thermal energy sources in vehicles, while also serving as an efficient means of energy storage. However, this transition towards electromobility presents significant challenges for the battery industry, particularly with regard to industrialization and the […]
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Human-Humanoid collaboration in manufacturing opportunities and challenges in the context of industry 5.0
The introduction of humanoid robots in industry represents an important aspect of the industry 5.0 enhance resilience of productin and represents a real change in the manner of dealing with the technology assistance for human operators, where the smart system will be able to execute similar tasks as the humans. This paper presents a concise […]
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Towards Eco-Efficient AI: Hybrid Data Strategies for BIPV Energy Prediction
Accurate prediction of energy production from building-integrated photovoltaic (BIPV) systems is essential for optimizing building energy use and supporting decarbonization strategies. Synthetic data provides a valuable foundation for model development, particularly when real measurements are limited, but validation on operational systems remains critical for reliable deployment. In this study, we propose a hybrid data approach, […]
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BI2PV Simulator for Optimized BIPV Design in Industrial Buildings
The integration of photovoltaics (PV) into industrial buildings remains inhibited by the lack of unified tools that reconcile architectural constraints, energy performance, and economic viability. We present Industrial Building Integrated PV (BI²PV) simulator, an interactive simulator developed with MATLAB App Designer, encompassing the entire workflow: import and interpolation of NSRDB (National Solar Radiation Database) data […]
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Sparse space-heterogeneous, physics-constrained PDE reconstruction for defect identification through low-frequency mechanics
This contribution explores an application of automatic PDE identification for nondestructive testing. Defect localization is traditionally performed via the identification of the spatial distribution of a well-chosen physical property, which is assumed to be impacted by the defect in question. Defects whose impact on physical properties is unclear may thus cause the identification approach to […]
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Potential of Generative Artificial Intelligence in Knowledge-Based Predictive Maintenance for Aircraft Engines
Predictive maintenance based on remaining useful life (RUL) estimation is widely recognized as a promising strategy for monitoring the health of critical systems such as aircraft engines, anticipating failures, and optimizing maintenance planning. A variety of approaches have been proposed in the literature, including data-driven, physics-based, and knowledgebased methods. Among them, deep learning-based methods have […]
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