Publications
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Multi-LOD generative approach for multi-objective sustainability optimization from the early stages of building design
Given the urgency of reducing the buildings’ environmental impact, this article focuses on optimizing sustainability from the earliest design phases, when decisions have the greatest influence. To address the challenges posed by the coarse granularity of digital models during the sketching phase and the often-conflicting nature of sustainability criteria, a generative workflow is proposed. This […]
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A Divisive Unsupervised Feature Selection Approach for Explainable Remaining Useful Life Prediction
Predicting the Remaining Useful Life (RUL) in maintenance often encounters challenges such as high dimensionality, feature redundancy, and limited explainability. This paper presents a novel approach that combines Interpretable Divisive Feature Clustering (IDFC) with Long Short-Term Memory (LSTM) networks. The IDFC algorithm leverages the strengths of variable clustering methods (VARCLUS) and the Clustering of Variables […]
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Investigating the sustainable design of a shipping container building using advanced building energy modeling and Bayesian inference
Modular buildings demonstrate environmental benefits in raw material usage but vary in energy performance by climate. Our research evaluates the energy performance of a modular educational building by calibrating a Building Energy Model (BEM) with operational data and Bayesian inference. As expected, this case study reveals that energy model calibration is not required when sufficient […]
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Structured pruning for efficient systolic array accelerated cascade Speech-to-Text Translation
We present in this paper a simple method for pruning tiles of weights in sparse matrices, that do not require fine-tuning or retraining. This method is applied here to the feed-forward layers of transformers. We assess in a first experiment the impact of such pruning on the performances of speech recognition, machine translation, and the […]
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Collaborative Semantic Mapping for Updating the Digital Twin in controlled Indoor Environment
Efficient management of indoor spaces is increasingly critical for applications such as security, evacuation planning, and roboticdeployment. Digital twin technology has emerged as a transformative solution, providing a real-time link between the physicalenvironment and its virtual counterpart to enable monitoring, simulation, analysis, and performance optimization. This paperintroduces a novel collaborative approach to semantic mapping that […]
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Towards a dynamic model of collective intelligence: Theoretical integration, nonverbal interaction and temporality
Most existing research on Collective Intelligence (CI) tends to emphasize final performance indicators or sums of individual cognitive traits, giving insufficient attention to how teams dynamically construct their collective capacity through ongoing interactions. In this paper, we propose an integrative perspective that draws on multiple existing approaches, ranging from conceptual frameworks (IMOI, TSM-CI) to measurement-oriented […]
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Augmented Perception: a real-time digital twin based approach to enhance robotic perception.
This paper introduces an Augmented Perception (AP) framework to enhance robotic perception in resilient manufacturing systems (MS) by integrating Digital Twin (DT) data directly at the sensor level in real time. Inspired by augmented reality, this approach enables robots to perceive both physical and virtual entities within a unified representation. To ensure real-time performance, we […]
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Multi-agent reinforcement learning approach for predictive maintenance of a Smart Building lighting system
This paper presents a predictive maintenance methodology for Smart Building systems using fault tree models and Weibull distributions to estimate component failure probabilities. We introduce connection events to reduce the complexity of the fault tree architecture. These new events allow us to capture system interactions and identify critical components. Reinforcement learning-based algorithms are employed to […]
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Exploring the Explanatory Constructs Influencing Employees’ Perceptions and Attitudes Toward Change
This study explores the influence of perceptions on individual readiness for change, offering a fresh perspective on the comparative significance of culture versus nationality as explanatory constructs. Based on a quantitative survey of 310 executives from Anglo-Saxon, North and Latin European, Arabian, and Far East Asian countries, the study employed a macro process regression procedure. […]
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Organizing Open Social Innovation for Grand Challenges: A Case Study in The Context of Disability
Open Social Innovation (OSI) is gaining increasing attention in discourses among scholars and practitioners as a new approach to addressing grand challenges. However, little is known about how to organize such OSI efforts. To address this gap, this study explores the role of open spaces, especially fabrication laboratories (fab labs), in orchestrating and scaling OSI […]
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Les déterminants dispositionnels dans lechoix de la mobilité écologique
Dans le respect des limites planétaires, la transition des mobilités (d’une mobilité carbonnée et individuelle vers une mobilité décarbonnée et raisonnée) est un enjeu central. Si les facteurs externes (infrastructures, coûts) influencent les choix de transport, ils n’expliquent pas à eux seuls la complexité des comportements. Les recherches en psychologie sociale ont largement exploré ce […]
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Graph-based framework for temporal human action recognition and segmentation in industrial context
Industry 5.0 places human operators at the center of industrial processes. In this context, analyzing human movements has become crucial for ensuring operator safety and improving productivity. More specifically, an accurate system for action recognition and segmentation is essential to identify and break down each action an operator performs. These systems enable a range of […]
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