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Electronics
Special Issues
Applications of Computational Intelligence in Real World Projects
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Applications of Computational Intelligence in Real World Projects

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 5063

Special Issue Editors

Prof. Dr. Francisco Serradilla-García

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Guest Editor
ETSI of Information Systems, Polytechnic University of Madrid, 28031 Madrid, Spain
Interests: soft computing; intelligent agents; software robots; ambient intelligence
Dr. Alberto Díaz-Álvarez

E-Mail Website
Guest Editor
Departamento de Sistemas Informáticos, E.T.S.I. de Sistemas Informáticos, Universidad Politécnica de Madrid, C/Alan Turing s/n, 28031 Madrid, Spain
Interests: artificial intelligence; deep learning; genetic algorithms; neural networks; fuzzy logic; driver behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Fernando de Mingo López

E-Mail Website
Guest Editor
Department of Computer Systems, Higher Technical School of Computer Systems Engineering, Polytechnic University of Madrid, Calle Alan Turing sn, 28031 Madrid, Spain
Interests: machine learning; grammatical swarm; bio-inspired computing; artificial intelligence; optimization

Special Issue Information

Dear Colleagues,

Computational Intelligence is the set of biologically inspired artificial intelligence techniques capable of modeling and optimizing complex phenomena. Its main techniques are Neural Networks, including deep learning, evolutionary computing, and fuzzy logic.

These methods, especially deep learning, are reaching a level of maturity that allows its application in real commercial products and services. We are starting to see software incorporating these techniques in photographic packages, natural language tools, surveillance systems, and autonomous driving, to name just a few.

The transition from purely theoretical research techniques to real-world applications is not a trivial process. It involves significant application and adaptation efforts due to the real needs of the field of use. Sometimes, applicability may require modification of some theoretical foundations. This arises from an iterative process, in which theory is revised for the sake of applicability to real problems in society. This is the gap between science and engineering.

In this sense, we are looking for works that show real applications of Computational Intelligence, both from the designer’s point of view (what theory is applied, with what restrictions, how that theory is adapted to make it applicable), and from the user’s point of view (what can be done that was not possible before, what restrictions are imposed, what benefits it provides) and, if applicable, from society’s point of view (what aspects of people’s lives change, what ethical problems arise).

This Special Issue aims to bring a set of high-quality articles on new practical contributions to industry and business in modeling, optimizing, and planning particularly complex processes, which have led to a significant improvement in the productive capacity of companies and organizations.

Topics include but are not limited to:

  • Practical applications of deep learning;
  • Meta-heuristics techniques in real systems;
  • Applications on intelligent transportation systems;
  • Artificial Intelligence applications towards Industry 4.0 and IoT;
  • Modeling economics and business;
  • Improving society and education through intelligent systems;
  • Deep learning in arts and science;
  • Computational Intelligence applied to cybersecurity;
  • Explainable Computational Intelligence.

Prof. Dr. Francisco Serradilla-García
Dr. Alberto Díaz-Álvarez
 Prof. Dr. Luis Fernando de Mingo López
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. Electronics 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 2400 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.

Keywords

  • Computational Intelligence
  • evolutionary computing
  • neural networks
  • deep learning
  • real-world applications
  • meta-heuristics
  • industry optimization

Published Papers (2 papers)

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19 pages, 4261 KiB  
Article
Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks
by Liesle Caballero, Álvaro Perafan, Martha Rinaldy and Winston Percybrooks
Electronics 2021, 10(8), 920; https://doi.org/10.3390/electronics10080920 - 13 Apr 2021
Cited by 7 | Viewed by 2143
Abstract
This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset [...] Read more.
This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications. Full article
(This article belongs to the Special Issue Applications of Computational Intelligence in Real World Projects)
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20 pages, 354 KiB  
Article
Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks
by Eduardo G. Pardo, Jaime Blanco-Linares, David Velázquez and Francisco Serradilla
Electronics 2020, 9(11), 1923; https://doi.org/10.3390/electronics9111923 - 16 Nov 2020
Cited by 9 | Viewed by 1742
Abstract
The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, [...] Read more.
The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory. Full article
(This article belongs to the Special Issue Applications of Computational Intelligence in Real World Projects)
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