Zerrendatu honen arabera: egilea "5017eceeae33d018ddf8bb5c340a47ab"
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An Adaptive Industrial Human-Machine Interface to Optimise Operators Working Performance
Reguera-Bakhache, Daniel; Garitano, Iñaki; Cernuda, Carlos; Uribeetxeberria, Roberto; Zurutuza, Urko; Lasa, Ganix (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 ... -
Critical Analysis of the Suitability of Surrogate Models for Finite Element Method Application in Catalog-Based Suspension Bushing Design
Cernuda, Carlos; Llavori, Inigo; Zavoianu, Alexandru-Ciprian; Aguirre, Aitor; Zabala, Alaitz; Plaza, Jon (IEEE, 2020)This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the ... -
Data-driven glass viscosity soft sensor development and validation in a glass container manufacturing line
Peña Mangas, David; Cernuda, Carlos; Reguera-Bakhache, Daniel (Elsevier, 2025)Viscosity plays a key role in glass container manufacturing, directly impacting product quality and consistency. To date, online measuring of this property during the glass manufacturing process has been both difficult and ... -
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 ... -
Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data
Mar Medina, Javier; Gorostiza, Ania; Arrospide, Arantzazu; Larrañaga Uribeetxeberria, Igor; Alberdi Aramendi, Ane; Cernuda, Carlos; Iruin, Álvaro; Tainta, Mikel; Mar Barrutia, Lorea; Ibarrondo Olagüenaga, Oliver (Elsevier, 2022)Introduction Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate ... -
Generalized SMOTE: A universal generation oversampling technique for all data types in imbalanced learning
Cernuda, Carlos; Reguera-Bakhache, Daniel; Aguirre, Aitor; Iturbe, Mikel; Garitano, Iñaki; Zurutuza, Urko (CAEPIA, 2021)A common problem that arises when facing classification tasks is the class imbalance problem, which happens when one or more classes are heavily underrepresented compared to the rest, being usually those minority classes ... -
An Industrial HMI Temporal Adaptation based on Operator-Machine Interaction Sequence Similarity
Reguera-Bakhache, Daniel; Garitano, Iñaki; Uribeetxeberria, Roberto; Cernuda, Carlos (IEEE, 2021)The incorporation of Artificial Intelligence (AI) into Industrial Environments has brought about a Smart Industry revolution, improving efficiency and simplifying complex industrial processes. However, these technological ... -
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 ...
