Publications
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Synthetic Data-Driven Augmentation for Precise 6-DoF Pose Estimation of Building Components in Automated Facility Inspections
This paper tackles the challenge of automating facility inspections by detecting building components, estimating their six-degree-of-freedom (6-DoF) poses (position and orienta tion), and comparing these estimations to Building Information Modeling (BIM) ground truth data. Vision based Deep learn ing methods offer promising results in pose estimation. They rely heavily on large annotated image datasets for […]
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Evaluating Robustness of 3D Gaussian Splatting–Based 6D Camera Pose Refinement Under Degraded Conditions for Lightly Textured Industrial Synthetic Objects
In this paper, 6D camera pose refinement is explored using 3D Gaussian Splatting (3DGS) on lightly textured industrial object datasets. The study employs datasets generated with Unity 3D rendering software, featuring objects such as a bicycle, MiR robot, Tiago robot, and UR robotic arm, each captured with ground-truth intrinsic and extrinsic camera parameters. A 3DGS […]
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Improving Image-Based Tool Detection in Industrial Workstations using Data Augmentation
Within the framework of Industry 5.0, affordances enable intuitive and adaptive interactions between operators and their industrial work environments. Accurately perceiving these affordances enhances overall production performance, safety, and operator effectiveness. This paper focuses on the initial step of a larger affordance characterization pipeline: detecting tools used by operators during manual assembly tasks. To address […]
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Multi-Agent System for Solving the Vehicle Routing Problem: A Hybrid Metaheuristic Approach
This paper introduces a novel Multi-Agent System (MAS) designed to solve the Vehicle Routing Problem (VRP), a well-known optimization challenge in logistics. The proposed MAS integrates seven established metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Golden Ball Algorithm (GBA), Hill Climbing (HC), Tabu Search (TS), and Simulated Annealing (SA)—using a Bi-directional […]
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Dynamic mechanical analysis of shape memory polymers: thermomechanical behavior and influence of thermal stimuli
This study explores the thermomechanical properties of polymethacrylate-based shape memory polymers (SMPs), focusing on hot water as a thermal stimulus for shape recovery. Using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), the research evaluates thermal transitions, viscoelastic behavior, and energy dissipation. DSC identified a glass transition temperature (Tg) of 67 °C, critical for […]
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AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognition
Context-aware emotion recognition (CAER) enhances affective computing in real-world scenarios, but traditional methods often suffer from context bias-spurious correlation between background context and emotion labels (e.g. associating “garden” with “happy”). In this paper, we propose AGCD-Net, an Attention Guided Context Debiasing model that introduces Hybrid ConvNeXt, a novel convolutional encoder that extends the ConvNeXt backbone […]
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Smart Fleet Management for Shared Micro-mobility: Balanced demand, Redistribution and Charging via Deep Reinforcement Learning
Shared electric micro-mobility, as an emerging mode of urban transportation, has been booming worldwide in recent years. Al though it provides sustainable, eco-friendly, and cost-effective mobility, it also faces several challenges, particularly due to existing inefficient fleet management strategies. These typically rely on fixed redistribution schedules that fail to adapt to highly dynamic user demand […]
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An enhanced genetic algorithm for optimized task allocation and planning in heterogeneous multi-robot systems
Efficient task allocation and path planning in heterogeneous multi-robot systems (MRS) remains a significant challenge in industrial inspection contexts, particularly when robots exhibit diverse sensing capabilities and must operate across spatially distributed sites. To address the limitations of exact methods and conventional heuristics, we propose a novel two-phase enhanced genetic algorithm (EGA) tailored for capability-constrained […]
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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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