Antonio Morán Álvarez

@inproceedings{Ossorio2024Deep,

title = {Deep Echo State Networks for Modelling of Industrial Systems},

author = {José Ramón Rodríguez-Ossorio and Claudio Gallicchio and Antonio Morán and Ignacio Díaz and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-031-62495-7_9},

isbn = {978-3-031-62495-7},

year  = {2024},

date = {2024-06-22},

urldate = {2024-06-22},

booktitle = {Engineering Applications of Neural Networks},

pages = {106–119},

publisher = {Springer Nature Switzerland},

address = {Cham},

keywords = {AUTOMATIZACIÓN CONTROL Y SUPERVISIÓN INDUSTRIAL, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Moran2024Machine,

title = {Machine Learning Modeling in Industrial Processes for Visual Analysis},

author = {Antonio Morán and Serafín Alonso and Juan J Fuertes and Miguel A Prada and Lidia Roca and Manuel Domínguez},

doi = {10.1007/978-3-031-62495-7_29},

isbn = {978-3-031-62495-7},

year  = {2024},

date = {2024-06-22},

urldate = {2024-06-22},

booktitle = {Engineering Applications of Neural Networks},

pages = {379–391},

publisher = {Springer Nature Switzerland},

address = {Cham},

keywords = {AUTOMATIZACIÓN CONTROL Y SUPERVISIÓN INDUSTRIAL, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{RodriguezOssorio2023Adaptive,

title = {Adaptive Model for Industrial Systems Using Echo State Networks},

author = {José Ramón Rodríguez-Ossorio and Antonio Morán and Juan J Fuertes and Miguel A Prada and Ignacio Díaz and Manuel Domínguez},

doi = {10.1007/978-3-031-34204-2_31},

isbn = {978-3-031-34204-2},

year  = {2023},

date = {2023-06-07},

urldate = {2023-06-07},

booktitle = {Engineering Applications of Neural Networks},

pages = {367--378},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {When a model of an industrial system is developed, it is expected that this model performs consistently when applied to other identically designed systems. However, different operating hours, degradation or maintenance, among other circumstances, cause a change in the dynamics of the system and result in the model not performing as expected. For this reason, it is necessary to build a model that continuously adapts to changes in the dynamics of the system, in order to handle such deviations and thus reduce the estimation error.},

keywords = {AUTOMATIZACIÓN CONTROL Y SUPERVISIÓN INDUSTRIAL, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GonzalezHerbon2023Virtual,

title = {Virtual Flow Meter for an Industrial Process},

author = {Raúl González-Herbón and Guzmán González-Mateos and Serafín Alonso and Miguel A Prada and Juan J Fuertes and Antonio Morán and Manuel Domínguez},

doi = {10.1007/978-3-031-34204-2_36},

isbn = {978-3-031-34203-5},

year  = {2023},

date = {2023-06-07},

urldate = {2023-06-07},

booktitle = {Engineering Applications of Neural Networks},

pages = {433--444},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {The digitalization process has emerged strongly in the industry, causing an increase of connected sensors and IIoT devices, which produce a great amount of varied data. However, some industrial variables are hard to measure because of its high cost, complex installation mechanisms or non-stop production requirements. These variables could be indirectly estimated based on other related variables available in the process. Data-driven methods would be appropriate for this purpose, modelling real and potentially complex industrial processes. In this paper, a methodology to develop a virtual flow meter for industrial processes is presented. It assumes the impossibility of installing a flow meter in the process, so a non-invasive flow meter is used punctually to measure and capture data for training data-driven methods. Three different methods have been trained to obtain the model function: multiple linear regression (MLR), multilayer perceptron (MLP) and long-short term memory (LSTM). The developed virtual flow meter has been tested on a pilot plant built with real industrial equipment. LSTM method yields the best performance in the flow estimation, providing the lowest MAE and RMSE errors. It is able to consider temporal dependencies, besides modelling the nonlinear nature of industrial processes.},

keywords = {AUTOMATIZACIÓN CONTROL Y SUPERVISIÓN INDUSTRIAL, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-34204-2_21,

title = {Hierarchical Prediction in Incomplete Submetering Systems Using a CNN},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-031-34204-2_21},

isbn = {978-3-031-34204-2},

year  = {2023},

date = {2023-06-07},

urldate = {2023-06-07},

booktitle = {Engineering Applications of Neural Networks},

pages = {239--250},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Energy market liberalization brings new opportunities, since large consumers have direct access to energy trading to buy energy for the next day. However, that requires a good estimation of the expected amount of energy and its hourly distribution in advance. On the other hand, smart energy meters are being installed in many facilities with the aim of achieving holistic submetering systems. These systems consist of a set of meters structured in several levels, so that there are hierarchical relations among upstream and downstream meters. This information could be exploited for achieving accurate one-day-ahead energy predictions. However, submetering systems might be incomplete due to unavailable meters or lost energy. In this paper, we propose a hierarchical prediction method for incomplete submetering systems that is based on 2D convolutional neural network (2D CNN) and is able to perform day ahead prediction of power consumption. This method exploits the hierarchical relations among meters and considers periodicity in order to forecast the power consumption for the next day. The proposed hierarchical method has proved to be more accurate and fast to forecast power consumption in incomplete submetering systems than using an individual predictions.},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gonzalez2022control,

title = {Control de caudal con un sensor virtual basado en técnicas de deep learning},

author = { Raúl González-Herbón and José Ramón Rodríguez-Ossorio and Guzmán González-Mateos and Antonio Morán and Serafín Alonso and Juan J Fuertes},

doi = {10.17979/spudc.9788497498418.0368},

year  = {2022},

date = {2022-09-01},

urldate = {2022-01-01},

booktitle = {XLIII Jornadas de Automática},

pages = {368--375},

organization = {Universidade da Coruña. Servizo de Publicacións},

keywords = {AUTOMATIZACIÓN CONTROL Y SUPERVISIÓN INDUSTRIAL, INDUSTRIA 4.0, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Sensor virtual de caudal basado en técnicas de deep learning},

author = { José Ramón Rodríguez-Ossorio and Raúl González-Herbón and Guzmán González-Mateos and Antonio Morán and Miguel A Prada and Ignacio Díaz and Manuel Domínguez},

doi = {10.18002/simceaci},

year  = {2022},

date = {2022-06-29},

urldate = {2022-07-01},

booktitle = {XVII Simposio CEA de Control Inteligente},

journal = {XVII Simposio CEA de Control Inteligente},

pages = {81-86},

keywords = {INDUSTRIA 4.0, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alonso2022Reconstructing,

title = {Reconstructing Electricity Profiles in Submetering Systems Using a GRU-AE Network},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-031-08223-8_21},

issn = {1865-0937},

year  = {2022},

date = {2022-06-10},

urldate = {2022-01-01},

booktitle = {Communications in Computer and Information Science},

volume = {1600 CCIS},

pages = {247-259},

publisher = {Springer Science and Business Media Deutschland GmbH},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alonso2020probabilistic,

title = {Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network},

author = { Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-030-48791-1_11},

year  = {2020},

date = {2020-01-01},

booktitle = {International Conference on Engineering Applications of Neural Networks},

pages = {155--166},

organization = {Springer, Cham},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Spagnolini2019,

title = {Data mining tool for academic data exploitation: Webtool description and usage},

author = {Umberto Spagnolini and Luca Fontana and Anna Paganoni and Aldo Torrebruno and Miguel A Prada and Manuel Domínguez and Antonio Morán and Ramón Vilanova and J. L. Vicario and Maria J Varanda and Paulo Alves and Michal Podpora and Marian Barbu},

isbn = {978-989-20-9286-7},

year  = {2019},

date = {2019-09-01},

booktitle = {Webtool description and usage Erasmus+ KA2 /KA203 project SPEET- Student Profiel for Enhancing Engineering Tutoring},

keywords = {MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pérez2019,

title = {Comparison of Network Intrusion Detection Performance Using Feature Representation},

author = {Daniel Pérez and Serafín Alonso and Antonio Morán and Miguel A Prada and Juan J Fuertes and Manuel Domínguez },

doi = {10.1007/978-3-030-20257-6_40},

isbn = {978-3-030-20257-6},

year  = {2019},

date = {2019-05-15},

booktitle = {Communications in Computer and Information Science, International Conference on Engineering Applications of Neural Networks},

pages = {463-475},

publisher = {Springer},

organization = {EANN2019},

keywords = {CIBERSEGURIDAD EN INFRAESTRUCTURAS CRÍTICAS, MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alonso2019,

title = {Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Perfecto Reguera and Ignacio Díaz and Manuel Domínguez},

doi = {10.1007/978-3-030-20257-6_26},

isbn = {978-3-030-20257-6},

year  = {2019},

date = {2019-05-15},

booktitle = {Communications in Computer and Information Science, International Conference on Engineering Applications of Neural Networks},

pages = {307-319},

publisher = {Springer},

organization = {EANN2019},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moran2017,

title = {Analysis of Parallel Process in HVAC Systems Using Deep Autoencoders},

author = {Antonio Morán and Serafín Alonso and Miguel A Prada and Juan J Fuertes and Ignacio Díaz and Manuel Domínguez},

editor = {Giacomo Boracchi and Lazaros Iliadis and Chrisina Jayne and Aristidis Likas},

doi = {10.1007/978-3-319-65172-9_2},

isbn = {978-3-319-65172-9},

year  = {2017},

date = {2017-01-01},

booktitle = {Engineering Applications of Neural Networks},

pages = {15--26},

publisher = {Springer International Publishing},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{barrientos13,

title = {Analysis of Heating Systems in Buildings Using Self-Organizing Maps},

author = {Pablo Barrientos and Carlos Del J Canto and Antonio Morán and Serafín Alonso and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

editor = {Lazaros Iliadis and Harris Papadopoulos and Chrisina Jayne},

doi = {10.1007/978-3-642-41013-0_38},

isbn = {978-3-642-41012-3},

year  = {2013},

date = {2013-01-01},

booktitle = {Engineering Applications of Neural Networks},

volume = {383},

pages = {370--379},

publisher = {Springer},

address = {Heidelberg},

series = {Communications in Computer and Information Science},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moran12b,

title = {Análisis de perfiles de consumo energético en edificios de uso docente utilizando técnicas de reducción de la dimensión.},

author = {Antonio Morán and Juan J Fuertes and Abel A Cuadrado and Miguel A Prada and Serafín Alonso and Manuel Domínguez},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the VIII Simposio CEA de Control Inteligente},

address = {Baiona, España},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moran12c,

title = {Visual continuous map for electrical bill comparisons},

author = {Antonio Morán and Juan J Fuertes and Manuel Domínguez and Miguel A Prada and Serafín Alonso and Pablo Barrientos},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 10th Portuguese Conference on Automatic Control},

address = {Funchal, Portugal},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moran11,

title = {Time series for fault detection in an industrial pilot plant},

author = {Antonio Morán and Serafín Alonso and Miguel A Prada and Roberto García and Juan J Fuertes},

year  = {2011},

date = {2011-09-01},

booktitle = {Physcon 2011, 5th International Scientific Conference on Physics and Control},

keywords = {MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{prada11b,

title = {Dimensionality reduction for damage detection in engineering structures.},

author = {Miguel A Prada and Antonio Morán and Serafín Alonso and Juan J Fuertes},

year  = {2011},

date = {2011-09-01},

booktitle = {Physcon 2011, 5th International Scientific Conference on Physics and Control},

keywords = {MACHINE LEARNING},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dominguez11c,

title = {Analysis of Electric Power Consumption Using Self-Organizing Maps},

author = {Manuel Domínguez and Juan J Fuertes and Ignacio Díaz and Abel A Cuadrado and Serafín Alonso and Antonio Morán},

doi = {10.3182/20110828-6-IT-1002.02092},

year  = {2011},

date = {2011-08-01},

booktitle = {Proceedings of the 8th World Congress of the International Federation of Automatic Control (IFAC 2011)},

address = {Milán, Italia},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alonso11b,

title = {A variant of self-organizing map (SOM) for monitoring power consumption.},

author = {Serafín Alonso and Antonio Morán and Miguel A Prada and Pablo Barrientos and Manuel Domínguez},

year  = {2011},

date = {2011-01-01},

booktitle = {Physcon 2011, 5th International Scientific Conference on Physics and Control},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}