PROJECTS
INTELLIGENT DATA-BASED MODELING APPLIED TO INDUSTRIAL FACILITIES FOR THE IMPROVEMENT OF ENERGY EFFICIENCY (PID2020-117890RB-I00).
Funding entity: Spanish Ministry of Science and Innovation
Start and end date: 01/09/2021 – 31/08/2025
EXPERIMENTAL STUDY FOR CYBERSECURITY DIAGNOSTICS ON CONTROL & MONITORING SYSTEMS OF THE CRITICAL INFRASTRUCTURES (LE045P17)
Funding entity: Junta de Castilla y León.
Name of the program: Research projects of Junta de Castilla y León, co-financed by European Regional development funds
Entities involved: University of León.
Start and end date: 2017 to 2019.
ERASMUS + STUDENT PROFILE FOR ENHANCING ENGINEERING TUTORING. 016-1-ES01-KA203-025452.
Funding entity: Spanish Ministry of Education, Culture and Sports.
Entities involved: Instituto Politécnico de Bragança; Politechnika Opolska; Politecnico de Milano;
University of León; Universitat Autònoma de Barcelona; Universitatea Dunarea de Jos Din Galati .
Start and end date: 2016 to 2019.
TECHNIQUES OF MODELING, PREDICTION AND INTERACTIVE VISUALIZATION OF DATA TO IMPROVE ENERGY EFFICIENCY IN BUILDINGS AND INDUSTRIAL FACILITIES (DPI2015-69891-C2-1-R)
Funding entity / s: Ministry of Economy and Competitiveness.
Name of the program: State Program of R & D Oriented towards the Challenges of the Society
Dates: 01/01/2016 – 12/31/2018
Entity / is participant / s: University of León; Oviedo University; Aalto University; Université Catholique de Louvain.
Duration: 3 years.
OBTAINING DYNAMIC MODELS USING PROJECTION TECHNIQUES TO IMPROVE ENERGY EFFICIENCY IN BUILDINGS OF THE TERTIARY SECTOR (ULE2009-4)
Funding entity: Universidad de León.
Start date: 02/23/2010
Duration: 1 year.
DEVELOPMENT OF A METHODOLOGY FOR SUPERVISION AND IMPROVEMENT OF PROCESSES BASED ON THE VISUAL REPRESENTATION OF DYNAMIC BEHAVIOR. DPI2009-13398-C02-02
Funding entity: Ministry of Science and Technology.
Duration, granted July 2009 (4 years).
MODELING THE DYNAMICS OF INDUSTRIAL PROCESSES USING SELF-ORGANIZED TOPOLOGICAL MAPS
Funding entity: JUNTA DE CASTILLA Y LEON, EDUCATION COUNSELING. Order EDU / 1160/2008.
Entity of realization: University of León
Duration 2008-2009 (2 years).
ADVANCED EXTRACTION AND REPRESENTATION TECHNIQUES KNOWLEDGE IN REMOTE SUPERVISION OF COMPLEX INDUSTRIAL PROCESSES VIA INTERNET (DPI2004-07662)
Funding entity: Ministry of Science and Technology.
Duration, from: December 12, 2004 to December 12, 2005.
REMOTE SUPERVISION OF PROCESSES DISTRIBUTED VIA INTERNET
Funding entity: (ACSUCYL) AGENCY FOR THE QUALITY OF THE UNIVERSITY SYSTEM OF CASTILLA Y LEÓN. CASTILLA AND LEÓN REGIONAL GOVERNMENT. EDU / 1225/2006.
Duration, granted July 2006 (2 years).
ADVANCED SUPERVISION OF PASTEURIZATION PROCESSES.
Research contract: GEA PROCESS ENGINEERING, S.A.
Advanced monitoring of pasteurization processes.
Duration: from June 1, 2015 to May 31, 2016.
PROVISION OF THE TECHNICAL SUPPORT SERVICE FOR THE MANAGEMENT AND MAINTENANCE OF THE CONTROL SYSTEMS OF THE COOLED WATER PRODUCTION FACILITIES.
Agreement: University Assistance Complex of León (Hospital de León).
Provision of technical support service for management and maintenance of the control systems of the chilled water production facilities of the University Assistance Complex of León (Hospital de León).
Duration: February 16, 2015 to December 31, 2015.
PROVISION OF DATA COLLECTION, STUDY AND ANALYSIS OF ELECTRICAL CONSUMPTION PROFILES.
Agreement: University Assistance Complex of León (Hospital de León).
Provision of data collection, study and analysis of electrical consumption profiles of the University Care Complex of León (Hospital de León).
Duration: March 1, 2014 to October 31, 2014.
TECHNICAL SUPPORT SERVICE FOR MANAGEMENT AND MAINTENANCE OF CONTROL SYSTEMS OF COOLED WATER PRODUCTION FACILITIES.
Agreement: University Assistance Complex of León (Hospital de León).
Technical support service for management and maintenance of the control systems of the chilled water production facilities.
Duration: July 16, 2013 to July 15, 2014.
STUDY AND OPTIMIZATION OF SUPERVISORY AND CONTROL SYSTEMS TO IMPROVE ENERGY EFFICIENCY.
Agreement: University of León
Study and optimization of supervision and control systems to improve energy efficiency in the facilities of the University of León. Technical support for the telemanagement system of energy consumption in the facilities of the University of León.
From: December 1, 2010 to December 31, 2013.
REMOTE SUPERVISION OF INDUSTRIAL PROCESSES DISTRIBUTED VIA INTERNET (LE015B06).
Funding entity / s: Junta de Castilla y León.
Start date: July 26, 2006.
PUBLICATIONS
2024
Diego García; Daniel Pérez; Panagiotis Papapetrou; Ignacio Díaz; Abel A Cuadrado; José María Enguita; Manuel Domínguez
Conditioned fully convolutional denoising autoencoder for multi-target NILM Journal Article Forthcoming
In: Neural Computing and Applications, Forthcoming, ISSN: 1433-3058.
@article{garcia_conditioned_2024,
title = {Conditioned fully convolutional denoising autoencoder for multi-target NILM},
author = {Diego García and Daniel Pérez and Panagiotis Papapetrou and Ignacio Díaz and Abel A Cuadrado and José María Enguita and Manuel Domínguez},
url = {https://doi.org/10.1007/s00521-024-10552-0},
doi = {10.1007/s00521-024-10552-0},
issn = {1433-3058},
year = {2024},
date = {2024-12-05},
urldate = {2024-12-01},
journal = {Neural Computing and Applications},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
Raúl González-Herbón; Guzmán González-Mateos; José Ramón Rodríguez-Ossorio; Miguel A Prada; Antonio Morán; Serafín Alonso; Juan J Fuertes; Manuel Domínguez
Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop Journal Article Forthcoming
In: Neural Computing and Applications, Forthcoming.
@article{González-Herbón2024-Assessment,
title = {Assessment and deployment of a LSTM-based virtual sensor in an industrial process control loop},
author = {Raúl González-Herbón and Guzmán González-Mateos and José Ramón Rodríguez-Ossorio and Miguel A Prada and Antonio Morán and Serafín Alonso and Juan J Fuertes and Manuel Domínguez},
url = {https://rdcu.be/d2eZP},
doi = {10.1007/s00521-024-10560-0},
year = {2024},
date = {2024-12-02},
urldate = {2024-12-02},
journal = {Neural Computing and Applications},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
José María Enguita; Diego García; Abel A Cuadrado; Daniel García-Peña; José Ramón Rodríguez-Ossorio; Ignacio Díaz
Trustworthiness Score for Echo State Networks by Analysis of the Reservoir Dynamics Proceedings Article
In: ESANN 2024 proceesdings, pp. 455–460, Ciaco - i6doc.com, Bruges (Belgium) and online, 2024, ISBN: 978-2-87587-090-2.
@inproceedings{enguita_trustworthiness_2024,
title = {Trustworthiness Score for Echo State Networks by Analysis of the Reservoir Dynamics},
author = {José María Enguita and Diego García and Abel A Cuadrado and Daniel García-Peña and José Ramón Rodríguez-Ossorio and Ignacio Díaz},
url = {https://www.esann.org/sites/default/files/proceedings/2024/ES2024-38.pdf},
doi = {10.14428/esann/2024.ES2024-38},
isbn = {978-2-87587-090-2},
year = {2024},
date = {2024-10-09},
urldate = {2024-01-01},
booktitle = {ESANN 2024 proceesdings},
pages = {455–460},
publisher = {Ciaco - i6doc.com},
address = {Bruges (Belgium) and online},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
José Ramón Rodríguez-Ossorio; Claudio Gallicchio; Antonio Morán; Ignacio Díaz; Juan J Fuertes; Manuel Domínguez
Deep Echo State Networks for Modelling of Industrial Systems Proceedings Article
In: Engineering Applications of Neural Networks, pp. 106–119, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-62495-7.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Antonio Morán; Serafín Alonso; Juan J Fuertes; Miguel A Prada; Lidia Roca; Manuel Domínguez
Machine Learning Modeling in Industrial Processes for Visual Analysis Proceedings Article
In: Engineering Applications of Neural Networks, pp. 379–391, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-62495-7.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder Journal Article
In: Integrated Computer-Aided Engineering, vol. 31, iss. 2, pp. 157-172, 2024, ISBN: 1875-8835.
@article{alonso23,
title = {Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder},
author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},
doi = {10.3233/ICA-230728},
isbn = {1875-8835},
year = {2024},
date = {2024-01-30},
urldate = {2024-00-00},
journal = {Integrated Computer-Aided Engineering},
volume = {31},
issue = {2},
pages = {157-172},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
José Ramón Rodríguez-Ossorio; Antonio Morán; Serafín Alonso; Daniel Pérez; Ignacio Díaz; Manuel Domínguez
Echo State Networks for anomaly detection in industrial systems Journal Article
In: IFAC-PapersOnLine, vol. 56, no. 2, pp. 1472–1477, 2023, ISSN: 2405-8963.
@article{Rodríguez-Ossorio2023,
title = {Echo State Networks for anomaly detection in industrial systems},
author = {José Ramón Rodríguez-Ossorio and Antonio Morán and Serafín Alonso and Daniel Pérez and Ignacio Díaz and Manuel Domínguez},
doi = {10.1016/j.ifacol.2023.10.1836},
issn = {2405-8963},
year = {2023},
date = {2023-11-22},
urldate = {2023-11-22},
journal = {IFAC-PapersOnLine},
volume = {56},
number = {2},
pages = {1472--1477},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
José Ramón Rodríguez-Ossorio; Antonio Morán; Juan J Fuertes; Miguel A Prada; Ignacio Díaz; Manuel Domínguez
Adaptive Model for Industrial Systems Using Echo State Networks Proceedings Article
In: Engineering Applications of Neural Networks, pp. 367–378, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-34204-2.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Raúl González-Herbón; Guzmán González-Mateos; Serafín Alonso; Miguel A Prada; Juan J Fuertes; Antonio Morán; Manuel Domínguez
Virtual Flow Meter for an Industrial Process Proceedings Article
In: Engineering Applications of Neural Networks, pp. 433–444, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-34203-5.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Hierarchical Prediction in Incomplete Submetering Systems Using a CNN Proceedings Article
In: Engineering Applications of Neural Networks, pp. 239–250, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-34204-2.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Diego García; Daniel Pérez; Panagiotis Papapetrou; Ignacio Díaz; Abel A Cuadrado; José María Enguita; Ana González; Manuel Domínguez
Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation Proceedings Article
In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 421–433, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-34204-2.
@inproceedings{Garcia2023conditioned,
title = {Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation},
author = {Diego García and Daniel Pérez and Panagiotis Papapetrou and Ignacio Díaz and Abel A Cuadrado and José María Enguita and Ana González and Manuel Domínguez},
doi = {10.1007/978-3-031-34171-7_34},
isbn = {978-3-031-34204-2},
year = {2023},
date = {2023-06-07},
urldate = {2023-06-07},
booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations},
pages = {421–433},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
José María Enguita; Ignacio Díaz; Diego García; Abel A Cuadrado; José Ramón Rodríguez-Ossorio
Principal Component Modes of Reservoir Dynamics in Reservoir Computing Proceedings Article
In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 434–445, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-34204-2.
@inproceedings{Enguita2023principal,
title = {Principal Component Modes of Reservoir Dynamics in Reservoir Computing},
author = {José María Enguita and Ignacio Díaz and Diego García and Abel A Cuadrado and José Ramón Rodríguez-Ossorio},
doi = {10.1007/978-3-031-34171-7_35},
isbn = {978-3-031-34204-2},
year = {2023},
date = {2023-06-07},
urldate = {2023-06-07},
booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations},
pages = {434–445},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Raúl González-Herbón; José Ramón Rodríguez-Ossorio; Guzmán González-Mateos; Antonio Morán; Serafín Alonso; Juan J Fuertes
Control de caudal con un sensor virtual basado en técnicas de deep learning Proceedings Article
In: XLIII Jornadas de Automática, pp. 368–375, Universidade da Coruña. Servizo de Publicacións 2022.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ana González; Ignacio Díaz; Abel A Cuadrado; Diego García; Daniel Pérez
Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders Journal Article
In: Computers and Electrical Engineering, vol. 101, pp. 108065, 2022, ISSN: 0045-7906.
@article{Gonzalez2022TwoStep,
title = {Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders},
author = {Ana González and Ignacio Díaz and Abel A Cuadrado and Diego García and Daniel Pérez},
doi = {https://doi.org/10.1016/j.compeleceng.2022.108065},
issn = {0045-7906},
year = {2022},
date = {2022-07-01},
urldate = {2022-01-01},
journal = {Computers and Electrical Engineering},
volume = {101},
pages = {108065},
abstract = {Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system’s healthy behaviour; and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system’s healthy behaviour; and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.
José Ramón Rodríguez-Ossorio; Raúl González-Herbón; Guzmán González-Mateos; Antonio Morán; Miguel A Prada; Ignacio Díaz; Manuel Domínguez
Sensor virtual de caudal basado en técnicas de deep learning Proceedings Article
In: XVII Simposio CEA de Control Inteligente, pp. 81-86, 2022.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Reconstructing Electricity Profiles in Submetering Systems Using a GRU-AE Network Proceedings Article
In: Communications in Computer and Information Science, pp. 247-259, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 1865-0937.
@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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Virtual Sensor for Probabilistic Estimation of the Evaporation in Cooling Towers Journal Article
In: Integrated Computer-Aided Engineering, vol. 28, no. 4, pp. 369-381, 2021.
@article{alonso21,
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Luis Francisco Martín Liras; Adolfo Rodríguez de Soto; Miguel A Prada
Feature analysis for data-driven APT-related malware discrimination Journal Article
In: Computers & Security, pp. 102202, 2021.
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Diego García; Daniel Pérez; Ignacio Díaz; Ana González; Manuel Domínguez; Abel A Cuadrado
Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings Journal Article
In: IEEE Transactions on Smart Grid, vol. 12, no. 3, pp. 2722 – 2731, 2021, (Cited by: 38).
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2020
Daniel Pérez; Serafín Alonso; Antonio Morán; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Evaluation of feature learning for anomaly detection in network traffic Journal Article
In: Evolving Systems, 2020.
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Miguel A Prada; Manuel Domínguez; Jose Lopez Vicario; Paulo Alexandre Vara Alves; Marian Barbu; Michal Podpora; Umberto Spagnolini; Maria J Varanda Pereira; Ramon Vilanova
Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees Journal Article
In: IEEE Access, vol. 8, pp. 212818–212836, 2020.
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Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Ignacio Díaz; Manuel Domínguez
Estimating cooling production and monitoring efficiency in chillers using a soft sensor Journal Article
In: Neural Computing and Applications, vol. 32, no. 23, pp. 17291–17308, 2020.
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Antonio Morán; Serafín Alonso; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Feature Extraction from Building Submetering Networks Using Deep Learning Journal Article
In: Sensors, vol. 20, no. 13, pp. 3665, 2020.
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Serafín Alonso; Antonio Morán; Daniel Pérez; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network Proceedings Article
In: International Conference on Engineering Applications of Neural Networks, pp. 155–166, Springer, Cham 2020.
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2019
Serafín Alonso; Antonio Morán; Miguel A Prada; Perfecto Reguera; Juan J Fuertes; Manuel Domínguez
A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants Journal Article
In: Energies, vol. 12, no. 5, pp. 827-827, 2019, ISSN: 1996-1073.
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Serafín Alonso; Daniel Pérez; Juan J Fuertes; Ignacio Díaz; Manuel Domínguez
A deep learning approach for fusing sensor data from screw compressors Journal Article
In: Sensors, vol. 19, no. 13, 2019, ISSN: 1424-8220.
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Umberto Spagnolini; Luca Fontana; Anna Paganoni; Aldo Torrebruno; Miguel A Prada; Manuel Domínguez; Antonio Morán; Ramón Vilanova; J. L. Vicario; Maria J Varanda; Paulo Alves; Michal Podpora; Marian Barbu
Data mining tool for academic data exploitation: Webtool description and usage Proceedings Article
In: Webtool description and usage Erasmus+ KA2 /KA203 project SPEET- Student Profiel for Enhancing Engineering Tutoring, 2019, ISBN: 978-989-20-9286-7.
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Marian Barbu; Ramón Vilanova; J. L. Vicario; Maria J Varanda; Paulo Alves; Michal Podpora; A. Kawala-Janik; Miguel A Prada; Manuel Domínguez; Umberto Spagnolini; Luca Fontana
Data mining tool for academic data exploitation: Publication Report on Engineering Students Profiles Proceedings Article
In: Publication Report on Engineering Students Porfiles Erasmus+ KA2 / KA203 project SPEET -Student Profile for Enhancing Engineering Tutoring, 2019, ISBN: 978-989-20-9286-7.
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Ramón Vilanova; Manuel Domínguez; J. L. Vicario; Miguel A Prada; Marian Barbu; Maria J Varanda; Paulo Alves; Michal Podpora; Umberto Spagnolini; Anna Paganoni
Data-driven tool for monitoring of students performance Journal Article
In: IFAC-PapersOnLine, vol. 52, no. 9, pp. 190-195, 2019.
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Manuel Domínguez; Ramón Vilanova; Miguel A Prada; J. L. Vicario; Marian Barbu; Maria J Varanda; Michal Podpora; Umberto Spagnolini; Paulo Alves; Anna Paganoni
Visual data analysis of engineering students performance from academic data Proceedings
no. 50-61, 2019.
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Daniel Pérez; Serafín Alonso; Antonio Morán; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Comparison of Network Intrusion Detection Performance Using Feature Representation Proceedings Article
In: Communications in Computer and Information Science, International Conference on Engineering Applications of Neural Networks, pp. 463-475, EANN2019 Springer, 2019, ISBN: 978-3-030-20257-6.
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Serafín Alonso; Antonio Morán; Daniel Pérez; Perfecto Reguera; Ignacio Díaz; Manuel Domínguez
Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers Proceedings Article
In: Communications in Computer and Information Science, International Conference on Engineering Applications of Neural Networks, pp. 307-319, EANN2019 Springer, 2019, ISBN: 978-3-030-20257-6.
@inproceedings{Alonso2019,
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Gonzalo Vergara; Juan J Carrasco; Jesus Martínez-Gómez; Manuel Domínguez; José A Gámez; Emilio Soria-Olivas
Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings Proceedings Article
In: Architecture and Design: Breakthroughs in Research and Practice , pp. 506-536, IGI Global, 2019.
@inproceedings{Vergara2019,
title = {Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings},
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2018
Diego García; Ignacio Díaz; Daniel Pérez; Abel A Cuadrado; Manuel Domínguez; Antonio Morán
Interactive visualization for NILM in large buildings using non-negative matrix factorization Journal Article
In: Energy and Buildings, vol. 176, pp. 95 - 108, 2018, ISSN: 0378-7788.
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Miguel A Prada; Manuel Domínguez; Antonio Morán; Ramón Vilanova; J. L. Vicario; Maria J Varanda; Paulo Alves; Michal Podpora; Marian Barbu; Aldo Torrebruno; Umberto Spagnolini
Data Mining tool for Academic Data Exploitation. Graphical Data Analysis and Visualization. Technical Report
2018, ISBN: 978-989-20-8739-9.
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J. L. Vicario; Ramón Vilanova; M. Bazzarelli; A. Paganoni; Umberto Spagnolini; Aldo Torrebruno; Miguel A Prada; Antonio Morán; Manuel Domínguez; Maria J Varanda; Michal Podpora; Marian Barbu
Data Mining tool for Academic Data Exploitation. Selection of Most Suitable Algorithms. Technical Report
2018, ISBN: 978-989-20-8738-2.
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Ignacio Díaz; Daniel Pérez; Abel A Cuadrado; Diego García; Manuel Domínguez
Interactive dimensionality reduction of large datasets using interpolation Proceedings Article
In: Proceedings of the ESANN 2018 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 117-122, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2018.
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2017
Ramón Vilanova; José Lopez; Miguel A Prada; Marian Barbu; Manuel Dominguez; Maria J Varanda; Michal Podpora; Umberto Spagnolini; Paulo Alves; Anna Paganoni
SPEET: An International Collaborative Experience in Data Mining For Education Proceedings Article
In: ICERI2017 Proceedings, pp. 1648-1656, IATED, Seville, Spain, 2017, ISSN: 2340-1095.
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Diego García; Ignacio Díaz; Daniel Pérez; Abel A Cuadrado; Manuel Domínguez
Latent variable analysis in hospital electric power demand using non-negative matrix factorization Proceedings Article
In: Proceedings of ESANN 2017, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 507-512, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2017.
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Ignacio Díaz; Abel A Cuadrado; Daniel Pérez; Manuel Domínguez; Serafín Alonso; Miguel A Prada
Energy analytics in public buildings using interactive histograms Journal Article
In: Energy and Buildings, vol. 134, pp. 94–104, 2017, ISSN: 0378-7788.
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Marian Barbu; Ramón Vilanova; José Lopéz; Maria J Varanda; Paulo Alves; Michal Podpora; Miguel A Prada; Antonio Morán; Aldo Torrebruno; Rodica Tocu Simona Marin
Data Mining Tool for Academic Data Exploitation. Literature review and first architecture proposal Technical Report
ERASMUS + KA2 / KA203 2017, ISBN: 978-972-745-228-6.
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Antonio Morán; Serafín Alonso; Miguel A Prada; Juan J Fuertes; Ignacio Díaz; Manuel Domínguez
Analysis of Parallel Process in HVAC Systems Using Deep Autoencoders Proceedings Article
In: Giacomo Boracchi; Lazaros Iliadis; Chrisina Jayne; Aristidis Likas (Ed.): Engineering Applications of Neural Networks, pp. 15–26, Springer International Publishing, 2017, ISBN: 978-3-319-65172-9.
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2016
Gonzalo Vergara; Juan I Alonso; Emilio Soria; José A Gámez; Manuel Domínguez
Random extreme learning machines to predict electric load in buildings Journal Article
In: Progress in Artificial Intelligence, pp. 1–7, 2016, ISSN: 2192-6360.
@article{Vergara2016,
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2015
Daniel Pérez
Supervisión y análisis de procesos con dinámica mediante principios y técnicas de visualización PhD Thesis
Universidad de Oviedo, 2015.
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Serafín Alonso; Miguel A Prada; Juan J Fuertes; Ignacio Díaz; Manuel Domínguez
Analysis of Electricity Bill Data Using Interactive Dimensionality Reduction Proceedings Article
In: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS), pp. 3:1–3:7, ACM, Rhodes, Greece, 2015, ISBN: 978-1-4503-3580-5.
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Manuel Domínguez; Serafín Alonso; Antonio Morán; Miguel A Prada; Juan J Fuertes
Dimensionality reduction techniques to analyze heating systems in buildings Journal Article
In: Information Sciences, vol. 294, pp. 553–564, 2015.
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2014
Daniel Pérez; Ignacio Díaz; Abel A Cuadrado; Francisco J García; Alberto B Díez; Manuel Domínguez
Power-Consumption Analysis through Web-Based Visual Data Exploration Proceedings Article
In: World Congress, pp. 11257–11262, 2014.
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2013
Pablo Barrientos; Carlos Del J Canto; Antonio Morán; Serafín Alonso; Miguel A Prada; Juan J Fuertes; Manuel Domínguez
Analysis of Heating Systems in Buildings Using Self-Organizing Maps Proceedings Article
In: Lazaros Iliadis; Harris Papadopoulos; Chrisina Jayne (Ed.): Engineering Applications of Neural Networks, pp. 370–379, Springer, Heidelberg, 2013, ISBN: 978-3-642-41012-3.
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Manuel Domínguez; Juan J Fuertes; Serafín Alonso; Miguel A Prada; Antonio Morán; Pablo Barrientos
Power monitoring system for university buildings: Architecture and advanced analysis tools Journal Article
In: Energy and Buildings, vol. 59, no. 0, pp. 152 - 160, 2013, ISSN: 0378-7788.
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Fernando Mateo; Juan J Carrasco; Abderrahim Sellami; Mónica Millán; Manuel Domínguez; Ignacio Díaz; Emilio Soria
Machine learning methods to forecast temperature in buildings Journal Article
In: Expert Systems with Applications, vol. 40, no. 4, pp. 1061–1068, 2013.
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Antonio Morán; Juan J Fuertes; Manuel Domínguez; Miguel A Prada; Serafín Alonso; Pablo Barrientos
Analysis of electricity bills using visual continuous maps Journal Article
In: Neural Computing and Applications, pp. 1-11, 2013, ISSN: 0941-0643.
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Antonio Morán; Juan J Fuertes; Miguel A Prada; Serafín Alonso; Pablo Barrientos; Ignacio Díaz; Manuel Domínguez
Analysis of electricity consumption profiles in public buildings with dimensionality reduction techniques Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1872 - 1880, 2013, ISSN: 0952-1976.
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Daniel Pérez; Francisco J García; Ignacio Díaz; Abel A Cuadrado; Daniel G Ordóñez; Alberto B Díez; Manuel Domínguez
Visual analysis of a cold rolling process using a dimensionality reduction approach Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1865–1871, 2013.
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2012
Antonio Morán
Análisis y predicción de perfiles de consumo energético en edificios públicos mediante técnicas de minería de datos PhD Thesis
Universidad de Oviedo, 2012.
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Ignacio Díaz; Abel A Cuadrado; Alberto B Díez; Manuel Domínguez; Juan J Fuertes; Miguel A Prada
Supervision of Industrial Processes using Self Organizing Maps SOM Book Chapter
In: Intelligent Data Analysis for Real-Life Applications: Theory and Practice, IGI Global, 2012, ISBN: 978-1-4666-1806-0.
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Serafín Alonso
Supervision de la energía eléctrica en edificios públicos de uso docente basada en técnicas de minería de datos visual PhD Thesis
Universidad de Oviedo, 2012.
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Daniel G Ordóñez; Abel A Cuadrado; Ignacio Díaz; Francisco J García; Alberto B Díez; Juan J Fuertes
Visual analysis of residuals from data-based models in complex industrial processes Journal Article
In: International Journal of Modern Physics B, vol. 26, no. 25, 2012.
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Serafín Alonso; Alberto B Díez; Manuel Domínguez
Supervisión de la energía eléctrica en edificios públicos de uso docente basada en técnicas de minería de datos visual Proceedings Article
In: X Simposio de Ingeniería de Control, Barcelona, 2012.
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Serafín Alonso; Antonio Morán; Miguel A Prada; Pablo Barrientos; Manuel Domínguez
Monitoring Power Consumption using a Generalized Variant of Self-Organizing Map (SOM) Journal Article
In: International Journal of Modern Physics B, vol. 26, no. 25, 2012.
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Ignacio Díaz; Manuel Domínguez; Abel A Cuadrado; Alberto B Díez; Juan J Fuertes
MorphingProjections: Interactive Visualization of Electric Power Demand Time Series Proceedings Article
In: M Meyer; T Weinkauf (Ed.): Eurographics Conference on Visualization (EuroVis2012), 2012.
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Manuel Domínguez; Juan J Fuertes; Ignacio Díaz; Miguel A Prada; Serafín Alonso; Antonio Morán
Monitoring industrial processes with SOM-based dissimilarity maps. Journal Article
In: Expert Systems With Applications., vol. 39, no. 8, pp. 7110-7120, 2012.
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Juan J Fuertes; Manuel Domínguez; Ignacio Díaz; Miguel A Prada; Serafín Alonso; Antonio Morán
Visualization Maps based on SOM to analyze MIMO systems Journal Article
In: Neural Computing and Applications, 2012.
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Antonio Morán; Juan J Fuertes; Serafín Alonso; Carlos Del J Canto; Manuel Domínguez
Time series for fault detection in an industrial pilot plant Journal Article
In: International Journal of Modern Physics B, vol. 26, no. 25, 2012.
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Antonio Morán; Juan J Fuertes; Abel A Cuadrado; Miguel A Prada; Serafín Alonso; Manuel Domínguez
Análisis de perfiles de consumo energético en edificios de uso docente utilizando técnicas de reducción de la dimensión. Proceedings Article
In: Proceedings of the VIII Simposio CEA de Control Inteligente, Baiona, España, 2012.
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Antonio Morán; Juan J Fuertes; Manuel Domínguez; Miguel A Prada; Serafín Alonso; Pablo Barrientos
Visual continuous map for electrical bill comparisons Proceedings Article
In: Proceedings of the 10th Portuguese Conference on Automatic Control, Funchal, Portugal, 2012.
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Antonio Morán; Juan J Fuertes; Miguel A Prada; Serafín Alonso; Pablo Barrientos; Manuel Domínguez; Ignacio Díaz
Analysis of electricity consumption profiles by means of dimensionality reduction techniques Book Section
In: Chrisina Jayne; Shigang Yue; Lazaros Iliadis (Ed.): Engineering Applications of Neural Networks, vol. 311, Springer Berlin / Heidelberg, 2012.
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Antonio Morán; Miguel A Prada; Serafín Alonso; Pablo Barrientos; Juan J Fuertes; Manuel Domínguez; Ignacio Díaz
Two-stage approach for electricity consumption forecasting in public buildings Book Section
In: Jaakko Hollmén; Frank Klawonn; Allan Tucker (Ed.): Advances in Intelligent Data Analysis XI, vol. 7619, pp. 219-229, Springer Berlin / Heidelberg, 2012.
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Miguel A Prada; Manuel Domínguez; Pablo Barrientos; Sergio García
Dimensionality reduction for damage detection in engineering structures. Journal Article
In: International Journal of Modern Physics B, vol. 26, no. 25, 2012.
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2011
Francisco J García; Ignacio Díaz; Ignacio Álvarez; Daniel Pérez; Daniel G González; Manuel Domínguez
Spectrogram Analysis using Manifold Learning. Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Daniel G González; Abel A Cuadrado; Ignacio Díaz; Francisco J García; Alberto B Díez; Juan J Fuertes
Visual analysis of residuals from data-based models in complex industrial processes. Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Antonio Morán; Serafín Alonso; Miguel A Prada; Roberto García; Juan J Fuertes
Time series for fault detection in an industrial pilot plant Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Daniel Pérez; Ignacio Díaz; Francisco J García; Pablo Barrientos
Visualization of Power Systems Consumptions by Manifold Learning. Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Miguel A Prada; Antonio Morán; Serafín Alonso; Juan J Fuertes
Dimensionality reduction for damage detection in engineering structures. Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Manuel Domínguez; Juan J Fuertes; Ignacio Díaz; Abel A Cuadrado; Serafín Alonso; Antonio Morán
Analysis of Electric Power Consumption Using Self-Organizing Maps Proceedings Article
In: Proceedings of the 8th World Congress of the International Federation of Automatic Control (IFAC 2011), Milán, Italia, 2011.
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Serafín Alonso; Manuel Domínguez; Miguel A Prada; Mika Sulkava; Jaakko Hollmén
Comparative analysis of power consumption in university buildings using envSOM. Book Section
In: Advances in Intelligent Data Analysis, IDA 2011, vol. 7014, pp. 10-21, Spri, Porto, 2011.
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Serafín Alonso; Antonio Morán; Miguel A Prada; Pablo Barrientos; Manuel Domínguez
A variant of self-organizing map (SOM) for monitoring power consumption. Proceedings Article
In: Physcon 2011, 5th International Scientific Conference on Physics and Control, 2011.
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Serafín Alonso; Mika Sulkava; Miguel A Prada; Manuel Domínguez; Jaakko Hollmén
EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization Book Section
In: Jorma Laaksonen; Timo Honkela (Ed.): Advances in Self-Organizing Maps, vol. 6731, pp. 61-70, Springer Berlin / Heidelberg, 2011.
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Ignacio Díaz; Abel A Cuadrado; Alberto B Díez; Manuel Domínguez
Manifold Learning for Visualization of Vibrational States of a Rotating Machine Book Section
In: Timo Honkela; Wlodzislaw Duch; Mark Girolami; Samuel Kaski (Ed.): Artificial Neural Networks and Machine Learning - ICANN 2011, vol. 6792, pp. 285-292, Springer Berlin / Heidelberg, 2011.
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Ignacio Díaz; Antonio Robles; Abel A Cuadrado; Alberto B Díez; Manuel Domínguez; Juan J Fuertes
Control Maps: Visualization of Local Robustness Properties for Control System Design Proceedings Article
In: Proceedings of the 8th World Congress of the International Federation of Automatic Control (IFAC 2011), Milán, Italia, 2011.
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Francisco J García; Ignacio Díaz; Ignacio Álvarez; Daniel Pérez; Daniel G Ordóñez; Manuel Domínguez
Time-frequency analysis of hot rolling using manifold learning Proceedings Article
In: Engineering Applications of Neural Networks, pp. 150–155, Springer, 2011.
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2010
Ignacio Díaz; Abel A Cuadrado; Alberto B Díez; Manuel Domínguez; Juan J Fuertes; Miguel A Prada
Visualization of Changes in Process Dynamics Using Self-Organizing Maps Book Section
In: Konstantinos Diamantaras; Wlodek Duch; Lazaros Iliadis (Ed.): Artificial Neural Networks - ICANN 2010, vol. 6353, pp. 343-352, Springer Berlin / Heidelberg, 2010.
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Juan J Fuertes; Manuel Domínguez; Perfecto Reguera; Miguel A Prada; Ignacio Díaz; Abel A Cuadrado
Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 23, no. 1, pp. 8 - 17, 2010, ISSN: 0952-1976.
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Miguel A Prada; Manuel Domínguez; Ignacio Díaz; Juan J Fuertes; Perfecto Reguera; Antonio Morán; Serafín Alonso
Application of SOM-Based Visualization Maps for Time-Response Analysis of Industrial Processes Book Section
In: Konstantinos Diamantaras; Wlodek Duch; Lazaros Iliadis (Ed.): Artificial Neural Networks - ICANN 2010, vol. 6353, pp. 392-401, Springer Berlin / Heidelberg, 2010.
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2009
Ignacio Díaz; Abel A Cuadrado; Alberto B Díez; Juan J Fuertes; Manuel Domínguez; Miguel A Prada
Visualization of MIMO Process Dynamics Using Local Dynamic Modelling with Self Organizing Maps Proceedings Article
In: Dominic Palmer-Brown; Chrisina Draganova; Elias Pimenidis; Haris Mouratidis (Ed.): Engineering Applications of Neural Networks, pp. 119-130, Springer Berlin Heidelberg, 2009, ISBN: 978-3-642-03969-0.
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Miguel A Prada
Técnicas de extracción del conocimiento basadas en data mining visual para la supervisión de procesos industriales. Análisis de la dinámica basado en mapas auto-organizados PhD Thesis
Universidad de León, 2009.
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2008
Ignacio Díaz; Manuel Domínguez; Abel A Cuadrado; Juan J Fuertes
A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes. Journal Article
In: Expert Systems with Applications., vol. 34, no. 4, pp. 2953-2965, 2008.
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Juan J Fuertes; Miguel A Prada; Manuel Domínguez; Perfecto Reguera; Ignacio Díaz; Alberto B Díez
Visualization of dynamic parameters of a multivariable system using Self-Organizing Maps. Proceedings Article
In: 17th IFAC World Congress, Seoul, 2008.
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2007
Manuel Domínguez; Perfecto Reguera; Juan J Fuertes; Ignacio Díaz; Abel A Cuadrado
Internet-based remote Supervision of Industrial Processes using Self-organizing maps. Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 20, no. 6, pp. 757–765, 2007.
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Ignacio Díaz; Abel A Cuadrado; Alberto B Díez; Juan J Fuertes; Manuel Domínguez; Perfecto Reguera
Visualization of Dynamics Using Local Dynamic Modelling with Self Organizing Maps. Book Section
In: Marques J de Sá; W Duch; L A Alexandre (Ed.): International Conference on Artificial Neural Networks 2007, Part I, Lecture Notes in Computer Science, vol. 4668, pp. 609–617, Springer-Verlag, 2007, ISBN: 978-3-540-74689-8.
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Juan J Fuertes; Miguel A Prada; Manuel Domínguez; Perfecto Reguera; Ignacio Díaz
Remote Supervision of Industrial Processes based on PMML-defined SOM Models. Proceedings Article
In: European Control Conference 2007 (ECC07), Kos, Greece, 2007.
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Juan J Fuertes; Miguel A Prada; Manuel Domínguez; Perfecto Reguera; Ignacio Díaz; Abel A Cuadrado
Modeling of Đynamics using Process State Projection on the Self Organizing Map. Book Section
In: J Marques de Sá; W Duch; L A Alexandre (Ed.): International Conference on Artificial Neural Networks 2007, Part I, Lecture Notes in Computer Science, vol. 4668, pp. 589–598, Springer-Verlag, 2007, ISBN: 978-3-540-74689-8.
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2006
Juan J Fuertes
Modelado y Representación de la Dinámica de Sistemas Complejos mediante Técnicas de Đata Mining Visual para la Supervisión Remota de Procesos Industriales vía Internet. PhD Thesis
Universidad de Oviedo, 2006.
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2005
Manuel Domínguez; Perfecto Reguera; Juan J Fuertes; Ignacio Díaz; Abel A Cuadrado
Internet based remote Supervision of Industrial Processes using Self Organizing Maps. Proceedings Article
In: 9th International Conference on Engineering Applications of Neural Networks, Francia, 2005.
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Juan J Fuertes; Perfecto Reguera; Manuel Domínguez; Ignacio Díaz; Abel A Cuadrado
Industrial Supervision System based on Visual Đata Mining and Motion Ŧrajectory Analysis. Proceedings Article
In: International Federation of Automatic Control 16th IFAC World Congress, Prague, 2005.
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2003
Manuel Domínguez
Supervisión remota de procesos complejos vía Internet mediante Técnicas de Data Mining Visual. Aplicación a una planta piloto industrial. PhD Thesis
Universidad de Oviedo, 2003.
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2002
Ignacio Díaz; Alberto B Díez; Abel A Cuadrado; Manuel Domínguez
Prior Knowledge Integration in Self Organizing Maps for Complex Process Supervision Proceedings Article
In: 15th IFAC World Congress, Barcelona, Spain, 2002.
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Abel A Cuadrado; Ignacio Díaz; Alberto B Díez; Manuel Domínguez; Faustino Obeso
MAPREX: A SOM-based condition monitoring system Proceedings Article
In: 15TH IFAC World Congress of Automatic Control, Barcelona, 2002, ISBN: 0-08-044295-1.
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