Zerrendatu honen arabera: egilea "5017eceeae33d018ddf8bb5c340a47ab"
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Data-Driven Industrial Human-Machine Interface Temporal Adaptation for Process Optimization
Reguera-Bakhache, Daniel; Garitano, Iñaki; Uribeetxeberria, Roberto; Cernuda, Carlos; Zurutuza, Urko (IEEE, 2020)The application of Artificial Intelligence (AI) into Industrial Human-Machine Interfaces (HMIs) moved old systems with physical buttons and analogue actuators into adaptive interaction models and context-based self adjusted ... -
Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery
Serradilla, Oscar; Zugasti, Ekhi; Cernuda, Carlos; Zurutuza, Urko (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 ... -
A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images
Intxausti Arbaiza, Eneko; Cernuda, Carlos; Zugasti, Ekhi (MDPI, 2024)In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need ... -
Prediction of long-term creep modulus of thermoplastics using brief tests and interpretable machine learning
Cernuda, Carlos (Elsevier, 2024)The prediction of creep behavior plays a critical role in the design of thermoplastic materials intended for prolonged use. The creep modulus, which describes the relationship between stress and strain that a material ... -
Towards robust defect detection in casting using contrastive learning
Intxausti Arbaiza, Eneko; Zugasti, Ekhi; Cernuda, Carlos (Springer, 2023)Defect detection plays a vital role in ensuring product quality and safety within industrial casting processes. In these dynamic environments, the occasional emergence of new defects in the production line poses a significant ... -
Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data
Cernuda, Carlos; Ezpeleta, Enaitz; Alberdi Aramendi, Ane (IOS Press, 2020)Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately ...
