Publications
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Graph Transformer Mixture-of-Experts (GTMoE) for 3D Hand Gesture Recognition
Mixture-of-experts (MoE) architectures have gained popularity in achieving high performance in a wide range of challenging tasks in Large Language Modeling (LLM) and Computer Vision, especially with the rise of Mixture-of-Experts with Mixtral/Mistral-7B Transformers. In this work, we propose the Graph Transformer Mixture-of-Experts (GTMoE) deep learning architecture to enhance the ability of the Transformer model […]
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Indicators Specification for Maturity Evaluation of BIM-based VR/AR Systems Using ISO/IEC 15939 Standard
Maturity evaluation of Building Information Modeling (BIM)-based Augmented Reality (AR) and Virtual Reality (VR) systems is still in its early phase. However, assessing the maturity of these systems is crucial to ensure they meet industry standards and are effectively implemented. This study builds upon our previously published research, which introduced an innovative approach for evaluating […]
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Multi-Objective Sustainable Flexible Job Shop Scheduling Problem: Balancing Economic, Ecological, and Social Criteria
Industry 5.0 makes it imperative to reevaluate the manner of using resources in manufacturing systems to ensure sustainability. In this context, scheduling problems are encountering new environmental and humanrelated challenges, and the concept of sustainable scheduling has gained importance, aiming to balance economic, environmental, and human factors. In this paper, we propose two multi-objective mathematical […]
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Energy-Efficient Flexible Flow Shop Scheduling Under Time-Of-Use Rates with Renewable Energy Sources
In response to climate change, industries strive to curtail energy consumption while maintaining production efficiency. Focused on time-dependent electricity prices, this paper addresses the scheduling challenge in a flexible flow shop machine environment. The goal is to minimize both makespan and total electricity costs in a grid-connected manufacturing setup with battery storage, solar power, and […]
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Dynamic and Sustainable Flexible Job Shop Scheduling Problem under Worker Unavailability Risk
In the current context of Industry 5.0, sustainable scheduling has emerged as an evolution of classical scheduling, now integrating environmental and human-centric considerations. The objective is to strike a balance between economic, environmental, and societal concerns. Additionally, there is a growing need to enhance the resilience in the Industry 5.0 era, necessitating dynamic systems capable […]
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Towards Green AI : Assessing the Robustness of Conformer and Transformer Models under Compression
Today, transformer and conformer models are commonly used in end-to-end speech recognition. Generally, conformer models are more efficient than transformers, but both suffer from large sizes, and expensive computing cost making their use environmentally unfriendly. In this paper, we propose compressing these models using quantization and pruning, evaluating size and computing time improvements while monitoring […]
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EPT-MoE: Toward Efficient Parallel Transformers with Mixture-of-Experts for 3D Hand Gesture Recognition
The Mixture-of-Experts (MoE) is a widely known deep neural architecture where an ensemble of specialized sub-models (a group of experts) optimizes the overall performance with a constant computational cost. Especially with the rise of Mixture-of-Experts with Mixtral-8x7B Transformers, MoE architectures have gained popularity in Large Language Modeling (LLM) and Computer Vision. In this paper, we […]
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Adaptative Reinforcement Learning Approach for Predictive Maintenance of a Smart Building Lighting System
Due to advancements in sensing technologies, enhanced IoT architectures, and expanded connectivity options, predictive maintenance has emerged as a compelling solution within the context of Industry 4.0 for industrial systems. However, within this landscape, such as in Smart Buildings (SBs), the lack of failure data poses a significant challenge for implementing traditional data-based approaches documented […]
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Augmented Perception: Empowering Flexible Manufacturing Systems through the Digital Twin – A Novel Approach.
In the context of the Industry of the Future, manufacturing environments must be flexible and reconfigurable to continuously adapt to customers’ personalized demands and changes in the manufacturing processes. This adaptation involves the reconfiguration of the layout and the integration of new systems into the environment: production lines, manufacturing machines, robotic arms, mobile robots, etc. […]
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Additive Manufacturing Awareness For Engineering Education in France
Additive manufacturing (AM) is one of the pillars of the Industry 4.0. Compared to traditional manufacturing, AM is a layer-by-layer construction; it provides a prototype before producing in order to optimize the design and avoid the stock market and uses strictly necessary material, which can be recyclable, at the benefit of leaning towards local production, […]
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Requirements engineering and user needs analysis
This chapter aims to provide guiding concepts to understand Requirements Engineering and User Needs Analysis, including methodological insights regarding the process and the object of study: focusing on different kinds of needs, including motivational needs, stimulating innovation, and anticipating future needs at the individual and societal levels. This chapter builds on several previous publications by […]
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Incorporating Uncertain Human Behavior in Production Scheduling for Enhanced Productivity in Industry 5.0 Context
Human-centered production systems are of increasing interest to researchers, especially with the advent of the Industry 5.0 paradigm. Most research into production scheduling has long neglected human workers’ specific roles and unpredictable behavior in a production system, treating them as machines with deterministic behavior. This work studies the impact of human operational behavior on the […]
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