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Special Issue "Artificial Intelligence and Sensors"

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

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editors

Dr. Miguel Arevalillo-Herráez
E-Mail Website
Guest Editor
Departament d’Informàtica, Escola Tècnica Superior d’Enginyeria, Universitat de València
Interests: affective computing; machine learning; artificial intelligence; education and intelligent tutoring systems
Dr. Miguel García-Pineda
E-Mail Website
Guest Editor
Computer Science, Dept. University of Valencia, Spain
Interests: Multimedia Networks; Streaming; QoE; QoS; IoTs; Cloud Computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors provide valuable data about physical magnitudes and environmental phenomena. However, the translation of these data into concrete actions requires processing the inputs that may come from one or many types of sensors, including sensor networks. Such processing can benefit from Artificial Intelligence (AI), and the use of machine learning, neural networks (including deep architectures), and information fusion methods have been common in this field. Currently, these concepts can be applied in different IoT architectures, where there are sensor and actuator nodes that communicate and create the networks. These types of networks tend to be autonomous networks that adapt to several conditions, creating smart IoT networks. These smart IoT networks would not be possible to carry out without use of artificial intelligence algorithms in their core.

This Special Issue will focus on the applications of AI to transform the data acquired from sensors into valuable information. Topics of interest include but are not limited to:

  • AI to process data coming from sensor networks
  • Information fusion methods to combine information from multiple sensors
  • Machine learning and decision making to issue responses to sensor data
  • Deep learning architectures for sensor applications
  • Smart sensors
  • Smart IoT networks
  • Machine learning methods to process sensor outputs
  • Explainable AI for sensor applications
  • AI-based sensors for efficient energy management
  • Databases to enable research on AI-based sensor applications

Dr. Miguel Arevalillo-Herráez
Dr. Miguel García-Pineda
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 papers will be 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 2000 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
  • intelligent sensors
  • deep learning
  • neural networks
  • information fusion
  • explainable AI
  • smart IoT networks

Published Papers (2 papers)

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Research

Open AccessArticle
Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods
Sensors 2020, 20(4), 1030; https://doi.org/10.3390/s20041030 - 14 Feb 2020
Abstract
Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification [...] Read more.
Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
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Open AccessArticle
Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods
Sensors 2019, 19(22), 4909; https://doi.org/10.3390/s19224909 - 10 Nov 2019
Abstract
This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. [...] Read more.
This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
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