Publications
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Spectral densification and macroscopic phase delay of gravitational echoes from exotic compact objects
Gravitational-wave echoes from Exotic Compact Objects (ECOs) provide an observable probe for horizon-scale physics. Standard phenomenological models for these signals typically assume a constant Free Spectral Range, relying on the geometric optics approximation. In this work, we demonstrate that wave dispersion at the photon sphere induces a systematic deviation from this assumption, manifesting instead as […]
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A resilient model-free controller for power regulation and fatigue load reduction in floating offshore wind turbines
This paper introduces a model-free control strategy for floating offshore wind turbines (FOWTs), which utilizes a double cascade, two extended state observer (dCESO)-based active disturbance rejection controller (ADRC) to regulate the collective pitch angle of the turbine. The primary objectives are stabilizing the generated power and rotor speed at their rated values while mitigating damage […]
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Stable O(N) recursive boundary mapping for robust parametric analysis of dispersion in 2D N-layered WGM resonators
A self-normalizing analytical framework is established to evaluate the exact dispersion relations of whispering gallery modes (WGMs) in 2D N-layered cylindrical micro-resonators. By mapping a continuous radial boundary function through a recursive propagator, the catastrophic numerical cancellations inherent to traditional transfer matrix methods (TMMs) are avoided, particularly at high azimuthal orders in the strict evanescent […]
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Standardization of Quantitative Variables in ClustOfVar-Based Variable Clustering for Mixed Data
In many real-world datasets, individuals are described by both quantitative and qualitative variables. Clustering variables rather than individuals can provide valuable insight into the underlying structure of such mixed datasets. ClustOfVar is a technique for clustering variables proposed by [2] and widely used for mixed data, with both hierarchical and partitioning algorithms. The method aims […]
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Interpretable Feature Clustering for Dimensionality Reduction. An application to Explainable Remaining Useful Life Prediction
In prediction, variable selection allows to handle high-dimensionality, variable redundancy, multicollinearity and instability. Classical approaches, such as penalised supervised methods, like Lasso, may become unstable in the presence of strongly correlated variables and rely on specific modeling assumptions. Unsupervised feature clustering provides an alternative strategy for dimensionality reduction that is independent of the response variable. […]
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Culture organisationnelle et mobilité durable : une approche méthodologique en contexte universitaire
Face au changement climatique, les établissements d’enseignement supérieur jouent un rôle clé dans la promotion de comportements durables. Cette étude explore l’impact de la culture organisationnelle sur les choix de transport des étudiants. Pour ce faire, une enquête a été menée auprès de 294 étudiants en mastère spécialisé au CESI, entre février et mai 2024. […]
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Multi-objective optimization of artificial neural networks using Fast NSGA-II for electricity demand forecasting
Accurate short-term electricity demand forecasting is a critical requirement for modern power systems, as forecast errors directly affect generation scheduling, market prices, and operational costs, particularly under dynamic pricing environments and increasing demand volatility. This study proposes an integrated forecasting framework combining Artificial Neural Networks (ANNs) with Multi-Objective Optimization (MOO) to jointly improve predictive accuracy […]
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BIM-LPS® Integration Case Study in the MEP Sector: Automating Phase Schedule Generation
Abstract – The Last Planner System® (LPS®) and Building Information Modeling (BIM) hold strong potential to improve construction productivity, yet their integration at the dataprocessing level remains limited. Information embedded in BIM models is often insufficiently structured and therefore not directly exploitable for LPS® implementation. This study proposes an approach that integrates LPS® with BIM […]
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Bi-Objective Electric Vehicle Charging Scheduling Under Stochastic Charging Durations
This paper addresses the electric vehicle charging scheduling problem under stochastic charging durations, where uncertainty arises from variations in actual charging times that are typically assumed deterministic in existing literature. We formulate a bi-objective optimization problem minimizing the expected values of peak load and total tardiness. We explicitly enforce non-overlapping charging sessions within the objective […]
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ResGNN: a residual GNN approach for leveraging general user preferences in session-based recommender systems
In recent years, session-based recommender systems (SBRSs) have emerged as pioneers for intelligent recommendation environments by capturing short-term user preferences without requiring direct access to user history. However, the challenge remains in effectively considering both short-term and long-term user preferences. Graph neural networks (GNNs) have shown promise in this field by leveraging the structural information […]
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Advanced PV-enabled heat generation system with precise thermal power regulation
This study presents an advanced PV-enabled heat generation system with precise thermal power regulation for resistive heating applications. Conventional solar thermal systems often rely on direct PV–resistor coupling, which leads to poor energy utilization, or MPPT-based operation, which maximizes electrical extraction but provides limited control over chamber temperature. To address these limitations, the proposed system […]
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LOOPER: A framework for synthetic dataset generation with configurable sensors and multi-view XR environments
Collecting multi-view datasets is essential for training and evaluating AI models in domains such as robotics and autonomous systems. However, generating such datasets remains challenging due to sensor synchronization issues and the labeling process, which is often timeconsuming, error-prone, and dependent on manual intervention. To address these limitations, a novel framework, LOOPER (Light Object and […]