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Selected papers from the 2020 IEEE International Workshop on Metrology...
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Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace

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

Deadline for manuscript submissions: closed (20 November 2020) | Viewed by 17153

Special Issue Editors

Dr. Mirko Marracci

E-Mail Website
Guest Editor
Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, 56122 Pisa, Italy
Interests: characterization of magnetic materials; electromagnetic compatibility; magnetic shielding and sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Picariello

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: electrical and electronic instrumentation; data acquisition systems (DAQs) based on compressive sampling (CS); biomedical instrumentation; distributed measurement systems, including wireless sensor networks (WSNs); Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

2020 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) (http://www.metroaerospace.org/home) will be held in Pisa, Italy, 22–24 June 2020. Authors of papers related to sensors presented at the workshop are invited to submit extended versions of their work to this Special Issue for publication.

Since the first edition, MetroAeroSpace has represented an international meeting place in the world of research in the field of metrology for aerospace involving national and international institutions and academia in a discussion on the state-of-the-art concerning issues that require a joint approach by experts regarding measurement instrumentation and industrial testing, typically professional engineers, and experts in innovation metrology, typically academics.

This seventh edition will keep pursuing the state of the art and practice carried out in past years. Attention will be pai but not limited to new technology for metrology-assisted production in the aerospace industry, aircraft component measurement, sensors and associated signal conditioning for aerospace, and calibration methods for electronic testing and measurement for aerospace.

Topics:

  • Electronic instrumentation for aerospace;
  • Automatic test equipment for aerospace;
  • Sensors and sensor systems for aerospace applications;
  • Wireless sensor networks in aerospace;
  • Attitude and heading reference systems;
  • Monitoring systems in aerospace;
  • Metrology for navigation and precise positioning;
  • Sensors and data fusion techniques for avionics and air traffic management;
  • Flight testing instrumentation and flight test techniques.

Dr. Mirko Marracci
Dr. Francesco Picariello
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 (5 papers)

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15 pages, 2107 KiB  
Article
Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition
by Pia Addabbo, Mario Luca Bernardi, Filippo Biondi, Marta Cimitile, Carmine Clemente and Danilo Orlando
Sensors 2021, 21(2), 381; https://doi.org/10.3390/s21020381 - 07 Jan 2021
Cited by 20 | Viewed by 3164
Abstract
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in [...] Read more.
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches. Full article
(This article belongs to the Special Issue Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace)
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18 pages, 14121 KiB  
Article
Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling
by Martyna Wiciak-Pikuła, Agata Felusiak-Czyryca and Paweł Twardowski
Sensors 2020, 20(20), 5798; https://doi.org/10.3390/s20205798 - 13 Oct 2020
Cited by 17 | Viewed by 2320
Abstract
This article deals with the phenomenon of tool wear prediction in face milling of aluminum matrix composite materials (AMC), class as hard-to-cut materials. Artificial neural networks (ANN) are one of the tools used to predict tool wear or surface roughness in machining. Model [...] Read more.
This article deals with the phenomenon of tool wear prediction in face milling of aluminum matrix composite materials (AMC), class as hard-to-cut materials. Artificial neural networks (ANN) are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results. Because of their mechanical properties based on SiC or Al2O3 reinforcement, AMCs are applied in the automotive and aerospace industry. Due to these materials’ abrasive nature, a three-edged end mill with diamond coating was selected to carry out milling tests. In this work, multilayer perceptron (MLP) models were used to predict the tool flank wear VBB and tool corner wear VBC during milling of AMC with 10% SiC content. The signals of vibration acceleration and cutting forces were selected as input to the network, and the tests were carried out with three cutting speeds. Based on the analysis of the developed models, the models with the best efficiency were selected, and the quality of wear prediction was assessed. The main criterion for evaluating the quality of the developed models was the mean square error (MSE) in order to compare measured and predicted value of tool wear. Full article
(This article belongs to the Special Issue Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace)
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22 pages, 11248 KiB  
Article
Measurement of the Flow Field Generated by Multicopter Propellers
by Zbigniew Czyż, Paweł Karpiński and Wit Stryczniewicz
Sensors 2020, 20(19), 5537; https://doi.org/10.3390/s20195537 - 27 Sep 2020
Cited by 10 | Viewed by 2971
Abstract
This paper presents the results of research on the airflow around a multirotor aircraft. The research consisted of the analysis of the velocity field using particle image velocimetry. Based on the tests carried out in a wind tunnel, the distribution of the velocity [...] Read more.
This paper presents the results of research on the airflow around a multirotor aircraft. The research consisted of the analysis of the velocity field using particle image velocimetry. Based on the tests carried out in a wind tunnel, the distribution of the velocity and its components in the vertical plane passing through the propeller axis were determined for several values of the angle of attack of the tested object for two values of airflow velocity inside the tunnel, i.e., vwt = 0 m/s and vwt = 12.5 m/s. Determining the velocity value as a function of the coordinates of the adopted reference system allowed for defining the range of impact of the horizontal propellers and the fuselage of the research object itself. The tests allowed for quantitative and qualitative analyses of the airflow through the horizontal rotor. Particular attention was paid to the impact of the airflow and the angle of attack on the obtained velocity field distributions. Full article
(This article belongs to the Special Issue Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace)
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14 pages, 2934 KiB  
Article
Measurements of Aerodynamic Interference of a Hybrid Aircraft with Multirotor Propulsion
by Zbigniew Czyż and Mirosław Wendeker
Sensors 2020, 20(12), 3360; https://doi.org/10.3390/s20123360 - 13 Jun 2020
Cited by 8 | Viewed by 3262
Abstract
This article deals with the phenomenon of aerodynamic interference occurring in the innovative hybrid system of multirotor aircraft propulsion. The approach to aerodynamics requires a determination of the impact of active sources of lift and thrust upon the aircraft aerodynamic characteristics. The hybrid [...] Read more.
This article deals with the phenomenon of aerodynamic interference occurring in the innovative hybrid system of multirotor aircraft propulsion. The approach to aerodynamics requires a determination of the impact of active sources of lift and thrust upon the aircraft aerodynamic characteristics. The hybrid propulsion unit, composed of a conventional multirotor source of thrust as well as lift in the form of the main rotor and a pusher, was equipped with an additional propeller drive unit. The tests were conducted in a continuous-flow low speed wind tunnel with an open measuring space, 1.5 m in diameter and 2.0 m long. Force testing made it possible to develop aerodynamic characteristics as well as defining aerodynamic characteristics and defining the field of speed for the considered design configurations. Our investigations enabled us to analyze the results in terms of a mutual impact of particular components of the research object and the area of impact of active elements present in a common flow. Full article
(This article belongs to the Special Issue Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace)
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16 pages, 10176 KiB  
Article
Single Channel Source Separation with ICA-Based Time-Frequency Decomposition
by Dariusz Mika, Grzegorz Budzik and Jerzy Józwik
Sensors 2020, 20(7), 2019; https://doi.org/10.3390/s20072019 - 03 Apr 2020
Cited by 14 | Viewed by 4754
Abstract
This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows [...] Read more.
This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal. Full article
(This article belongs to the Special Issue Selected papers from the 2020 IEEE International Workshop on Metrology for AeroSpace)
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