Special Issue "Artificial Neural Network Applications in Power Electronics, Communication Networks and IoT"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Prof. Dr. Giacomo Capizzi
Website
Guest Editor
UNICT, Department of Electrical, Electronics and Informatics Engineering (DIEEI), University of Catania, 95125, Italy
Interests: neural networks; wavelet theory; statistical pattern recognition; Bayesian networks; integrated generation systems; renewable energy sources; battery storage modeling and simulation
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Special Issue Information

Dear Colleagues,

The rapid development of power electronics make that it is used in many fields of science and technology. Various sensors and controllers are used in power systems, communication networks and IoT. In these areas of application the use of intelligent methods to enhance the efficiency is very important. Then in this special issue all the latest innovative methods based on neural networks and in similar intelligent method are welcomed. Submissions covering neural networks and other computational intelligence methods are welcomed. However, the topics are not limited to these and any other proposals in the field related to the intelligent power electronics are welcomed too. The topics consist in, but are not limited to, the following:

  • Algorithms and methods
  • Power electronics for Applied computing
  • Power electronics for High performance computing
  • Signal processing for power electronics
  • Circuits theory for power electronics
  • Application of Computational Intelligence in power electronics
  • Hardware Architectures
  • Wireless and sensors networks

Prof. Dr. Giacomo Capizzi
Guest Editor

Manuscript Submission Information

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Keywords

  • Algorithms and methods 
  • Power electronics for Applied computing 
  • Power electronics for High performance computing
  • Signal processing for power electronics
  • Circuits theory for power electronics
  • Application of Computational Intelligence in power electronics
  • Hardware Architectures
  • Wireless and sensors networks

Published Papers (3 papers)

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Research

Open AccessArticle
Indoor Localization System Based on Bluetooth Low Energy for Museum Applications
Electronics 2020, 9(6), 1055; https://doi.org/10.3390/electronics9061055 - 26 Jun 2020
Abstract
In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the [...] Read more.
In the last few years, indoor localization has attracted researchers and commercial developers. Indeed, the availability of systems, techniques and algorithms for localization allows the improvement of existing communication applications and services by adding position information. Some examples can be found in the managing of people and/or robots for internal logistics in very large warehouses (e.g., Amazon warehouses, etc.). In this paper, we study and develop a system allowing the accurate indoor localization of people visiting a museum or any other cultural institution. We assume visitors are equipped with a Bluetooth Low Energy (BLE) device (commonly found in modern smartphones or in a small chipset), periodically transmitting packets, which are received by geolocalized BLE receivers inside the museum area. Collected packets are provided to the locator server to estimate the positions of the visitors inside the museum. The position estimation is based on a feed-forward neural network trained by a measurement campaign in the considered environment and on a non-linear least square algorithm. We also provide a strategy for deploying the BLE receivers in a given area. The performance results obtained from measurements show an achievable position estimate accuracy below 1 m. Full article
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Open AccessArticle
A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter
Electronics 2019, 8(7), 756; https://doi.org/10.3390/electronics8070756 - 04 Jul 2019
Cited by 5
Abstract
This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed “chimeric synchronization”. The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming [...] Read more.
This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed “chimeric synchronization”. The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming input information from digital to analogue, the converter can perform information processing after training the network by selecting control parameters. In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimeric synchronization of the output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels, and the synchronization efficiency parameter. The distribution of solutions depending on the operating mode of the oscillators, sub-threshold mode, or generation mode are revealed. Technological approaches for the implementation of a neural network information converter are proposed, and examples of its application for image filtering are demonstrated. The proposed method helps to significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects. Full article
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Open AccessArticle
Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network
Electronics 2019, 8(3), 290; https://doi.org/10.3390/electronics8030290 - 05 Mar 2019
Cited by 2
Abstract
This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced [...] Read more.
This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy. Full article
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