Publications
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Hygroscopic stresses development in epoxy-metal bonded assemblies under hydrothermal conditions
Epoxy-metal bonded assemblies are widely used in various industrial applications due to their mechanical efficiency and stress distribution capabilities. However, the durability of these assemblies in humid environments remains the subject of extensive research. This study focuses on the development of a numerical hygroelastic model to investigate hygroscopic stresses in a single-lap epoxy-metal bonded assembly […]
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Enhancing Fuzzy Forests with Consensus Clustering for Unbiased and Robust Feature Selection
This study presents the Fuzzy Forests algorithm, which uses consensus clustering to improve feature selection in high-dimension data and address multicollinearity issues. While Fuzzy Forests mitigates feature selection biases, its effectiveness relies on the clustering method used. Our proposed consensus clustering framework enhances robustness and reduces variability in results, demonstrating better feature independence through extensive […]
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Multimodal Route Planning Integrating Soft Mobility: A Real-World Case Study for Student Mobility
Soft and active mobility (SAM) integration into multimodal route planning is a critical innovation for advanc ing sustainable transportation. This study explores the inclusion of shared (SSAM) and personal (PSAM) soft and active mobility modes within public transport systems. Leveraging a time-expanded model, the proposed approach optimizes route planning by introducing reliability as a novel […]
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Data generation and deep neural network predictions for aged mechanical properties
The aim of this work is the data generation of aged mechanical properties following the Arrhenius equation and large deformation theory for a transversely isotropic bio-based polyurethane foam, and the application of this dataset in the training process of different deep neural network architectures to evaluate their capacity to predict the full stress–strain behavior of […]
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A memetic method for solving portfolio optimization problem under cardinality, quantity, and pre-assignment constraints
In industrial finance, portfolio selection has emerged as a critical challenge that has received considerable attention over the past few decades. The standard approach to this problem is the Markowitz mean–variance model, which seeks to balance two inherently conflicting objectives: maximizing returns and minimizing risk. This study investigates portfolio optimization under realistic constraints, including cardinality, […]
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NETLOGOPY: UNLOCKING ADVANCED SIMULATION AND INTEGRATION FOR NETLOGO USING PYTHON
NetLogo is widely recognized as one of the most popular software tools for agent-based simulation. However, it has notable limitations, particularly the lack of advanced libraries in specialized areas such as optimization, artificial intelligence (AI), and mechanical or electrical modeling. On the other hand, Python is a feature-rich programming language that is increasingly used in […]
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Deep Reinforcement Learning of Simulated Students Multimodal Mobility Behavior: Application to the City of Toulouse
This study presents a Deep Reinforcement Learning (DRL) approach to address the multimodal mobility behavior of daily commuters, focusing specifically on students’ home-university multimodal trips. The proposed mesoscopic model addresses key limitations of recent macro and microscopic models by balancing individual mobility preferences with significant group-level student factors. At its core, the model employs a […]
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Integrating Centrality Measures in Federated Learning-Based Intrusion Detection Systems
Network Intrusion Detection Systems (NIDS) are mechanisms designed to improve security by monitoring networks for signs of potential intrusions. While data-driven deep learningbased NIDSs have been popular for their superior performance, they are limited by their reliance on large amounts of data, often processed in a centralized manner. Federated Learning (FL) has thus emerged as […]
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A Novel Federated Learning Client Selection With Anomaly Detection Approach for IoT Systems
Federated Learning (FL) is emerging as a crucial approach to enhance data privacy and security, particularly in smart buildings and Internet of Things (IoT) ecosystems. By distributing learning across multiple clients, FL minimizes the need for centralized data transfers. This decentralized approach allows clients to collaboratively improve machine learning models without sharing raw data, and […]
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A Secure Architecture for Digital Twins in Resource-Constrained Industrial Systems
Digital Twins (DT) are transforming the Industrial Internet of Things (IIoT) by providing virtual replicas of physical assets, enabling enhanced predictive analytics and data-driven decision-making. However, as Industry 5.0 integrates DT with generative AI technologies via APIs and tokens, security risks increase. Outdated modeling tools, coupled with vulnerabilities in resource-constrained environments, such as limited computational […]
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Improving predictive maintenance: Evaluating the impact of preprocessing andmodel complexity on the effectiveness of eXplainable Artificial Intelligence methods
Due to their performance in this field, Long-Short-Term Memory Neural Network (LSTM) approaches are often used to predict the remaining useful life (RUL). However, their complexity limits the interpretability of their results. So, eXplainable Artificial Intelligence (XAI) methods are used to understand the relationship between the input data and the predicted RUL. Modeling involves making […]
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A Continual Learning Approach for Failure Prediction under Non-Stationary Conditions: Application to Condition Monitoring Data Streams
Accurate forecasting of Remaining Useful Life (RUL) is crucial for predictive maintenance (PdM), permitting prompt actions that decrease downtime and maintenance expenses. Yet, conventional RUL estimation techniques often struggle to adjust to changing operational conditions and data drift, restricting their use in dynamic industrial settings. This research presents a continual learning framework designed for these […]
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