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Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
(IEEE, 2020)
The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ...
Data-driven energy resource planning for Smart Cities
(IEEE, 2020)
Cities are growing and, therefore, the primary needs, such as the energy resources. Hence, managing them in the proper way becomes essential for a sustainable growth. This paper proposes a data-driven tool based on IoT ...
OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs
(IEEE, 2024)
The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and ...
Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery
(IEEE, 2020)
This paper presents the implementation and explanations of a remaining life estimator model based on machine learning, applied to industrial data. Concretely, the model has been applied to a bushings testbed, where fatigue ...
Analyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated Vehicles
(IEEE, 2023)
Automated Vehicles (AVs) require sensing and perception to integrate data from multiple sources, such as cameras, lidars, and radars, to operate safely and efficiently. Collaborative sensing through wireless vehicular ...
An Adaptive Industrial Human-Machine Interface to Optimise Operators Working Performance
(IEEE, 2021)
Adaptive User Interfaces (AUI) have the potential to deliver advantageous solutions for a wide range of industrial applications. Their ability to adapt to operator interaction patterns and achieve a more personalised ...
Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms
(IEEE, 2021)
The software of elevators requires maintenance over several years to deal with new functionality, correction of bugs or legislation changes. To automatically validate this software, test oracles are necessary. A typical ...
A novel methodology for the characterization of cutting conditions in turning processes using Machine Learning models and Acoustic Emission Signals
(Springer Nature, 2021)
In the last few years, the industry requires to know in real-time the condition of their assets. Acoustic Emission (AE) technique has been widely used to understand the real-time condition of manufacturing processes such ...
