Publications
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Forecasting short-term availability of bikes at sharing stations : a deep-learning approach
The growing popularity of bike-sharing systems represents a significant urban mobility trend, offering substantial economic, environmental, territorial and social benefits. The achievement of these advantages is contingent on the operational efficiency of the service. In this paper, the short-term bike availability forecasting at the station level is addressed using real-time open data from public bike […]
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Hybrid flax/carbon bonded composite patches for strengthening of steel plates: layup and adhesive effect
Adhesive bonding of Fiber-Reinforced Polymer (FRP) patches is increasingly used to strengthen steel structures. Considering that carbon FRP (CFRP) and epoxy adhesives are the primary materials in industrial applications, this study explores the feasibility of hybridizing CFRPs with Flax FRPs (FFRPs) and the effects of using different adhesives on the mechanical performance of reinforced steel […]
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Data-driven prediction and surrogate-based parameter selection for density and surface roughness in SLM of 316L SS under data scarcity
Selective laser melting (SLM) of 316L stainless steel involves strongly nonlinear interactions between processing parameters and resulting part properties, which renders conventional trial-and-error optimization strategies inefficient. This study proposes a data-centric framework that combines machine learning with evolutionary optimization to accurately predict and computationally select candidate processing parameters for surface roughness Ra and relative density. […]
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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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Dynamic Integrated Production and Delivery Scheduling with Electric Vehicles: A Hybrid Ant Colony System Approach
This paper addresses a dynamic Integrated Production and Delivery Problem (IPDP), where customer orders arrive in real time and production and transportation decisions must be continuously updated. To manage this dynamic envi- ronment, we develop D-HACS, a Dynamic-Hybrid Ant Colony System that adapts integrated schedules as new information becomes available. Computational experiments show that D- […]
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Energy-Aware Flexible Flow Shop Scheduling with Solar PV, Battery Storage, and Demand Response: A Genetic Algorithm Approach
Green manufacturing increasingly requires pro- duction systems to balance operational performance with en- ergy efficiency and cost-effectiveness. This paper addresses an energy-aware flexible flow shop scheduling problem in which a factory operates under a time-of-use electricity tariff, participates in a demand response reward program, exploits on- site photovoltaic generation, battery energy storage, and applies an […]
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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 […]