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Selected Papers from HAIS2018
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Selected Papers from HAIS2018

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 19535

Special Issue Editors

Prof. Dr. Francisco Javier De Cos Juez

E-Mail Website
Guest Editor
Department of Mining Exploitation and Prospecting, Universidad de Oviedo, 33012 Oviedo, Spain
Interests: virtual sensors; intelligent and autonomous sensor systems; adaptive optics; artificial intelligence; machine learning; deep learning
Dr. Enrique De la Cal

E-Mail Website
Guest Editor
Department of Computer Science, Universidad de Oviedo, Oviedo, Spain
Interests: intelligent sensors; automatic diseases identification; human activity recognition; soft computing industrial problem modelling; automatic trading; soft computing
Dr. José R. Villar

E-Mail Website
Guest Editor
Computer Science Department, University of Oviedo, Spain
Interests: eHealth, ambient assisted living, computation in biology and medicine, soft computing
Prof. Dr. Emilio Corchado

E-Mail
Guest Editor
Department of Computer Science, Universidad de Salamanca, Salamanca, Spain
Interests: intelligent sensors; eHealth; automatic diseases identification; soft computing

Special Issue Information

Dear Colleagues,

A selection of the best works presented during the 13th International Conference on Hybrid Artificial Intelligent Systems 2018 (HAIS 2018) will be collected in a Special Issue of the journal Sensors, “Selected Papers from the 13th International Conference on Hybrid Artificial Intelligent Systems 2018”. The conference will be focused on hybridized intelligent systems, in terms of both techniques and applications. HAIS will be held in Oviedo (Spain), 20–22 June, 2018.

The HAIS conference is an annual meeting of researchers focused on the hybridization of AI techniques and their applications to solve complex real world problems. Typically, this conference includes theoretical topics, such as optimization, soft computing, neural networks, and classification with rule base systems, among others. HAIS also includes applying theoretical research in solving real world problems, knowledge extraction from big data problems, intelligent sensor design, IoT, and eHealth.

This Special Issue will focus on problems, such as developing or applying intelligent sensors in real scenarios, intelligent management of IoT problems, eHealth solutions based on HAIS and also includes distributed intelligent sensor networks. Furthermore, research on HAIS applied to the design and development of Cyber–Physical Systems—including, techniques such as avatar profile learning and deployment—are also are included in the focus of this Special Issue.

Topics:

We invite investigators to contribute original research articles, as well as review articles, to this Special Issue. Potential topics include, but are not limited to:

Hybrid Artificial Intelligent Systems using:

  • Fusion of soft computing and hard computing
  • Evolutionary Computation
  • Visualization Techniques
  • Optimization Techniques
  • Ensemble Techniques
  • Case base reasoning
  • Chance discovery
  • Data mining and decision support systems
  • Intelligent agent-based systems
  • Deep Learning

Applied to:

  • IoT and Intelligent Cloud/Fog Computing
  • Computation in Bio-Medicine
  • Intelligent Sensors
  • Cyber-Physical Systems
Prof. Dr. Francisco Javier De Cos Juez
Dr. Enrique de la Cal
Dr. José R. Villar
Prof. Dr. Emilio Corchado
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

19 pages, 3915 KiB  
Article
Thumbnail Tensor—A Method for Multidimensional Data Streams Clustering with an Efficient Tensor Subspace Model in the Scale-Space
by Bogusław Cyganek
Sensors 2019, 19(19), 4088; https://doi.org/10.3390/s19194088 - 21 Sep 2019
Cited by 5 | Viewed by 2480
Abstract
In this paper an efficient method for signal change detection in multidimensional data streams is proposed. A novel tensor model is suggested for input signal representation and analysis. The model is built from a part of the multidimensional stream by construction of the [...] Read more.
In this paper an efficient method for signal change detection in multidimensional data streams is proposed. A novel tensor model is suggested for input signal representation and analysis. The model is built from a part of the multidimensional stream by construction of the representing orthogonal tensor subspaces, computed with the higher-order singular value decomposition (HOSVD). Parts of the input data stream from successive time windows are then compared with the model, which is either updated or rebuilt, depending on the result of the proposed statistical inference rule. Due to processing of the input signal tensor in the scale-space, the thumbnail like output is obtained. Because of this, the method is called a thumbnail tensor. The method was experimentally verified on annotated video databases and on real underwater sequences. The results show a significant improvement over other methods both in terms of accuracy as well as in speed of operation time. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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14 pages, 1278 KiB  
Article
Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees
by Eva María Artime Ríos, Fernando Sánchez Lasheras, Ana Suárez Sánchez, Francisco J. Iglesias-Rodríguez and María del Mar Seguí Crespo
Sensors 2019, 19(12), 2800; https://doi.org/10.3390/s19122800 - 22 Jun 2019
Cited by 21 | Viewed by 3887
Abstract
One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative [...] Read more.
One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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16 pages, 884 KiB  
Article
A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques
by Héctor Aláiz-Moretón, Manuel Castejón-Limas, José-Luis Casteleiro-Roca, Esteban Jove, Laura Fernández Robles and José Luis Calvo-Rolle
Sensors 2019, 19(12), 2740; https://doi.org/10.3390/s19122740 - 18 Jun 2019
Cited by 27 | Viewed by 3292
Abstract
This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish [...] Read more.
This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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18 pages, 981 KiB  
Article
Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling
by José-Luis Casteleiro-Roca, José Francisco Gómez-González, José Luis Calvo-Rolle, Esteban Jove, Héctor Quintián, Benjamin Gonzalez Diaz and Juan Albino Mendez Perez
Sensors 2019, 19(11), 2485; https://doi.org/10.3390/s19112485 - 31 May 2019
Cited by 38 | Viewed by 4283
Abstract
The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as [...] Read more.
The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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17 pages, 19699 KiB  
Article
Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method
by Ruben Urraca, Javier Antonanzas, Andres Sanz-Garcia and Francisco Javier Martinez-de-Pison
Sensors 2019, 19(11), 2483; https://doi.org/10.3390/s19112483 - 30 May 2019
Cited by 2 | Viewed by 2522
Abstract
Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the [...] Read more.
Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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15 pages, 3815 KiB  
Article
Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations
by Sergio Luis Suárez Gómez, Carlos González-Gutiérrez, Francisco García Riesgo, Maria Luisa Sánchez Rodríguez, Francisco Javier Iglesias Rodríguez and Jesús Daniel Santos
Sensors 2019, 19(10), 2233; https://doi.org/10.3390/s19102233 - 14 May 2019
Cited by 6 | Viewed by 2615
Abstract
Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the [...] Read more.
Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the context of solar observation, the usage of Adaptive Optics on solar differs from nocturnal operations, bringing up a challenge to correct the image aberrations. In this work, a convolutional approach is given to address this issue, considering SCAO configurations. A reconstruction algorithm is presented, “Shack-Hartmann reconstruction with deep learning on solar–prototype” (proto-HELIOS), to correct on fixed solar images, achieving an average 85.39% of precision in the reconstruction. Additionally, results encourage to continue working with these techniques to achieve a reconstruction technique for all the regions of the sun. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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