Intelligent Systems and Control

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 8775

Special Issue Editors


E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, 15405 Ferrol, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modeling; optimization, and control; fault and anomaly detection using traditional and intelligent techniques; new sensors; robust sensors; and virtual sensors
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
CTC, Department of Industrial Engineering, CITIC, University of A Coruña, EPEF, Calle Mendizábal, s/n, Campus de Esteiro, Ferrol, 15403 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modelling, optimization and control; fault and anomalies detection using traditional and intelligent techniques; new sensors; robust sensors and virtual sensors
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
CTC, Department of Industrial Engineering, CITIC, University of A Coruña, EPEF, Calle Mendizábal, s/n, Campus de Esteiro, Ferrol, 15403 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modelling, optimization and control; fault and anomalies detection using traditional and intelligent techniques; new sensors; robust sensors and virtual sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change and the consequent problems from contamination have encouraged society to look for a new source of energy, especially clean energy sources. In addition, there are new engineering and industrial requirements which make it necessary to overcome some key challenges. Some of them are, for instance, energy consumption minimization, atmosphere emission reduction, humans’ quality of life improvement, or reaching industrial enhancement requests. Researchers and technical experts must pay special attention to these, in addition to being bound to attempt to improve the current trends based on examples like those mentioned above. Traditional techniques, although they are very important and solve the vast majority of cases, often have limitations. Therefore, new advanced developments are required.

This Special Issue offers a fascinating opportunity to expose and deliberate on the newest advances and real-world applications in modeling, optimization, and control where intelligent systems are used.

The aim of this Special Issue is to promote advancement in, but not limited to, the following topics:

  • Fault detection and diagnosis;
  • Traditional system upgrading;
  • Improvement or new intelligent control techniques and topologies;
  • Complex system modeling;
  • Optimization of processes and procedures;
  • Intelligent system applications over industrial processes;
  • Systems efficiency improvement and optimization;
  • Intelligent system applications;
  • Intelligent control applications;
  • Biomedical applications;
  • Smart grids and microgrid applications;
  • Mobility and electromobility applications;
  • Electronics and power electronics applications;
  • Internet of Things.

Dr. Jose Luis Calvo-Rolle
Dr. Esteban Jove
Dr. José-Luis Casteleiro-Roca
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

  • Artificial intelligence
  • Edge computing
  • Machine learning
  • Soft computing
  • Smart learning
  • Deep learning
  • Optimization
  • Process control
  • IoT

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 3627 KiB  
Article
Automated Data Acquisition System Using a Neural Network for Prediction Response in a Mode-Locked Fiber Laser
by Jose Ramon Martinez-Angulo, Eduardo Perez-Careta, Juan Carlos Hernandez-Garcia, Sandra Marquez-Figueroa, Jose Hugo Barron Zambrano, Daniel Jauregui-Vazquez, Jose David Filoteo-Razo, Jesus Pablo Lauterio-Cruz, Olivier Pottiez, Julian Moises Estudillo-Ayala and Roberto Rojas-Laguna
Electronics 2020, 9(8), 1181; https://doi.org/10.3390/electronics9081181 - 22 Jul 2020
Cited by 2 | Viewed by 2803
Abstract
In this paper, we proposed a system to integrate optical and electronic instrumentation devices to predict a mode-locking fiber laser response, using a remote data acquisition with processing through an artificial neural network (ANN). The system is made up of an optical spectrum [...] Read more.
In this paper, we proposed a system to integrate optical and electronic instrumentation devices to predict a mode-locking fiber laser response, using a remote data acquisition with processing through an artificial neural network (ANN). The system is made up of an optical spectrum analyzer (OSA), oscilloscope (OSC), polarimeter (PAX), and the data acquisition automation through transmission control protocol/internet protocol (TCP/IP). A graphic user interface (GUI) was developed for automated data acquisition with the purpose to study the operational characteristics and stability at the passively mode-locked fiber laser (figure-eight laser, F8L) output. Moreover, the evolution of the polarization state and the behavior of the pulses are analyzed when polarization is changed by proper control plate adjustments. The data is processed using deep learning techniques, which provide the characteristics of the pulse at the output. Therefore, the parameter classification-identification is in accordance with the input polarization tilt used for the laser optimization. Full article
(This article belongs to the Special Issue Intelligent Systems and Control)
Show Figures

Figure 1

19 pages, 6392 KiB  
Article
Trajectory Planning for Spray Painting Robot Based on Point Cloud Slicing Technique
by Wei Chen, Xu Li, Huilin Ge, Lei Wang and Yuhang Zhang
Electronics 2020, 9(6), 908; https://doi.org/10.3390/electronics9060908 - 29 May 2020
Cited by 31 | Viewed by 4988
Abstract
In this paper, aiming at the problem of poor quality and low spraying efficiency of irregular for complex freeform surfaces, a new spray painting robot trajectory planning method based on point cloud slicing technology is proposed. Firstly, the point cloud data of the [...] Read more.
In this paper, aiming at the problem of poor quality and low spraying efficiency of irregular for complex freeform surfaces, a new spray painting robot trajectory planning method based on point cloud slicing technology is proposed. Firstly, the point cloud data of the workpiece to be sprayed is obtained by laser scanning. The point cloud data is processed to obtain the point cloud model of the sprayed workpiece. Then the section polysemy line is obtained after slice acquisition and section data processing of the point cloud model. The section polysemy line is sampled on average, and the normal vector of all sampling points is estimated. Finally, interpolation algorithm is used to connect the data points to obtain the space trajectory of spraying robot. In addition, the droplet trajectory model for electrostatic spray painting is established. The experimental results show that the method fully meets the requirements of coating thickness and improves the spraying efficiency and uniformity of coating. Full article
(This article belongs to the Special Issue Intelligent Systems and Control)
Show Figures

Figure 1

Back to TopTop