16 citations · 17 across the 11 of their papers we have counts for
12 papers
Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
Begoña Ispizua, Serio Gil-López, Leire Arrizabalaga +1
With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degrada…
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
Alain Andres, Aitor Martinez-Seras, Ibai Laña +1
In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstandi…
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
Jesus L. Lobo, Ibai Laña, Eneko Osaba +1
AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Informatio…
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
Ibai Laña, Ignacio, Olabarrieta +1
The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years. Uncertainty estimation provides the user w…
A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles
Eric L. Manibardo, Ibai Laña, Esther Villar +1
Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leadin…
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification
Alejandro Barredo Arrieta, Sergio Gil-Lopez, Ibai Laña +2
Since their inception, learning techniques under the Reservoir Computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads requi…
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Eric L. Manibardo, Ibai Laña, Javier Del Ser
Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas su…
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo +2
Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking enviro…
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
Eric L. Manibardo, Ibai Laña, Javier Del Ser
This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-qualit…
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Javier Del Ser, Ibai Lana, Eric L. Manibardo +5
In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activit…
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
Eric L. Manibardo, Ibai Laña, Jesus L. Lobo +1
This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predict…
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
Ibai Lana, Javier J. Sanchez-Medina, Eleni I. Vlahogianni +1
Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven. Nowadays, In…