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Eva Portillo
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2020 – today
- 2024
- [j23]Sergio Lucas
, Eva Portillo
Methodology based on spiking neural networks for univariate time-series forecasting. Neural Networks 173: 106171 (2024) - 2023
- [j22]Adriana Navajas-Guerrero
PLAHS: A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process. Appl. Soft Comput. 147: 110718 (2023) - [j21]Janire Otamendi
Machine learning-based gait anomaly detection using a sensorized tip: an individualized approach. Neural Comput. Appl. 35(24): 17443-17459 (2023) - 2022
- [j20]Adriana Navajas-Guerrero
A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0. Comput. Ind. Eng. 171: 108381 (2022) - [j19]Iratxe Niño-Adan
Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0. Eng. Appl. Artif. Intell. 111: 104807 (2022) - 2021
- [j18]Iratxe Niño-Adan
Normalization Influence on ANN-Based Models Performance: A New Proposal for Features' Contribution Analysis. IEEE Access 9: 125462-125477 (2021) - [j17]Iratxe Niño-Adan
Soft-sensor design for vacuum distillation bottom product penetration classification. Appl. Soft Comput. 102: 107072 (2021) - [j16]Urtats Etxegarai
A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners. Eur. J. Oper. Res. 291(2): 427-437 (2021) - [j15]Iratxe Niño-Adan
Feature weighting methods: A review. Expert Syst. Appl. 184: 115424 (2021) - [j14]Iratxe Niño-Adan
Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column. Sensors 21(12): 3991 (2021) - 2020
- [j13]Asier Brull Mesanza
A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip. IEEE Access 8: 210023-210034 (2020) - [j12]Ander Arriandiaga
Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data. IEEE Trans. Neural Networks Learn. Syst. 31(10): 3920-3931 (2020)
2010 – 2019
- 2019
- [j11]Iñigo Sesar Gil
Instrumented Crutch Tip for Monitoring Force and Crutch Pitch Angle. Sensors 19(13): 2944 (2019) - [c10]Iratxe Niño-Adan
Analysis and Application of Normalization Methods with Supervised Feature Weighting to Improve K-means Accuracy. SOCO 2019: 14-24 - [c9]Adriana Navajas-Guerrero
A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data. SOCO 2019: 179-189 - [i1]Urtats Etxegarai, Eva Portillo, Jon Irazusta, Lucien Koefoed, Nikola K. Kasabov:
A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners. CoRR abs/1903.02318 (2019) - 2018
- [j10]Urtats Etxegarai
Estimation of lactate threshold with machine learning techniques in recreational runners. Appl. Soft Comput. 63: 181-196 (2018) - [j9]Asier Zubizarreta
Real time direct kinematic problem computation of the 3PRS robot using neural networks. Neurocomputing 271: 104-114 (2018) - [j8]Aitziber Mancisidor
Virtual Sensors for Advanced Controllers in Rehabilitation Robotics. Sensors 18(3): 785 (2018) - 2017
- [j7]Ander Arriandiaga
Downsizing training data with weighted FCM for predicting the evolution of specific grinding energy with RNNs. Appl. Soft Comput. 61: 211-221 (2017) - [j6]Ander Arriandiaga
Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding. Neural Comput. Appl. 28(6): 1293-1307 (2017) - 2016
- [j5]Ander Arriandiaga
A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks. Neural Comput. Appl. 27(6): 1577-1592 (2016) - 2015
- [c8]Ander Arriandiaga
On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks. EANN 2015: 24-34 - [c7]Pablo Bengoa
A stable model-based control scheme for parallel robots using additional sensors. IROS 2015: 3170-3175 - 2014
- [j4]Ander Arriandiaga
Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process. Sensors 14(5): 8756-8778 (2014) - 2012
- [j3]Eva Portillo
A computer assistant for monitoring tool performance during the drilling process. Int. J. Comput. Integr. Manuf. 25(9): 829-838 (2012) - [c6]Eva Portillo
A Case Study of a Flexible Software Tool in the Drilling Process. CESCIT 2012: 333-338 - [c5]Isidro Calvo
Towards an Infrastructure Model for Composing and Reconfiguring Cyber-Physical Systems. UCAmI 2012: 282-289 - 2010
- [c4]Asier Zubizarreta
Extended CTC control for parallel robots. ETFA 2010: 1-8
2000 – 2009
- 2009
- [j2]Eva Portillo
Recurrent ANN for monitoring degraded behaviours in a range of workpiece thicknesses. Eng. Appl. Artif. Intell. 22(8): 1270-1283 (2009) - [c3]Asier Zubizarreta, Itziar Cabanes
Redundant dynamic modelling of the 3RRR parallel robot for control error reduction. ECC 2009: 2205-2210 - 2008
- [c2]Eva Portillo
Artificial Neural Networks for detecting instability trends in different workpiece thicknesses in a machining process. ACC 2008: 1064-1069 - 2007
- [j1]Eva Portillo
Design of a Virtual-Instrumentation System for a Machining Process. IEEE Trans. Instrum. Meas. 56(6): 2616-2622 (2007) - [c1]Eva Portillo
On the application of recurrent neural network techniques for detecting instability trends in an industrial process. ETFA 2007: 242-248
Coauthor Index
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