A5018743316- Digital Transformation in Industry
- Bayesian Modeling and Causal Inference
- IoT and Edge/Fog Computing
- Data Stream Mining Techniques
- Industrial Vision Systems and Defect Detection
- Time Series Analysis and Forecasting
- Machine Learning and Data Classification
- Robotics and Automated Systems
- Neural Networks and Applications
Universidad de Deusto
2021-2025
Fractional-order systems capture complex dynamic behaviors more accurately than integer-order models, yet their real-time identification remains challenging, particularly in resource-constrained environments. This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. First, PSO optimizes model parameters by minimizing the discrepancy between high-order...
This paper discusses the increasing amount of data handled by companies and need to use Big Data Analytics extract value from this data.However, due large collected, challenges related computational capacity machines often arise when performing analysis acquire relevant information for organization, especially we are using edge computing.The aims train machine learning models compressed data, with two compression techniques applied original data.The results show that trained achieved similar...
The industrial sector has undergone significant digital transformation, driven by advancements in technology and the Internet of Things (IoT). These developments have facilitated collection vast quantities data, which, turn, pose challenges for real-time data processing. This study seeks to validate efficacy accuracy edge computing models designed represent subprocesses within environments compare their performance with that traditional cloud models. By processing locally at point...
Currently most of the data collected in companies and industrial manufacturing environments through IoT devices is processed cloud [1]. Given large volume that each company manages due to emergence IoT, computing not best option for certain sectors such as automotive [3]. Within this sector, quality perceived by end customer closely linked assembly line. These lines collect a high number variables (temperatures, pressures, pumps, etc.) real-time prediction means small digital twins process...