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Aerospace
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Aeronautical Informatics
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Aeronautical Informatics

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 22376

Special Issue Editor

Prof. Dr. Umut Durak

E-Mail Website
Guest Editor
German Aerospace Center (DLR), Institute of Flight Systems, Braunschweig, Germany
Interests: model-based engineering and simulation-based verification of airborne software-intensive systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent advances in Information and Communication Technologies (ICT) have been phenomenal. Through various disruptive innovations, they have brought us to the digitalization era that is characterized by the keywords “smart” and “connected”. While all previous efforts were intended to automate individual systems, today’s focus is on the integration of all systems within a value chain into digital ecosystems.

After realizing far-reaching automation on aircraft, the aeronautics domain is now looking at the next generation of flight. Aeronautical informatics is here the key applied field of research that focuses on understanding, applying, and enhancing advancement of ICT in aeronautics. This multidisciplinary field is involved in information processing and engineering of information systems in relation to the science or practice of building or flying aircraft.

This Special Issue aims to highlight recent aeronautical informatics research and encourages authors to submit full research articles and review manuscripts that address (but are not limited to) advances in Software Engineering, Cyber-Physical Systems, Internet of Things (IoT), Service-Oriented Architecture (SOA), Digital Twin, Big Data and Data Analytics, Artificial Intelligence, Reconfigurable Computing, and Wireless/Cellular Networking applied to aeronautics.

Dr. Umut Durak
Guest Editor

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. Aerospace is an international peer-reviewed open access monthly 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

  • Software Engineering
  • Cyber-Physical Systems
  • Internet of Things (IoT)
  • Service-Oriented Architecture (SOA)
  • Digital Twin
  • Big Data and Data Analytics
  • Artificial Intelligence
  • Reconfigurable Computing
  • Wireless/Cellular Networking

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Published Papers (3 papers)

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27 pages, 30313 KiB  
Article
Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions
by Oliver Ohneiser, Jyothsna Adamala and Ioan-Teodor Salomea
Aerospace 2021, 8(9), 245; https://doi.org/10.3390/aerospace8090245 - 3 Sep 2021
Cited by 1 | Viewed by 2930
Abstract
Assistant based speech recognition (ABSR) prototypes for air traffic controllers have demonstrated to reduce controller workload and aircraft flight times as a result. However, two aspects of ABSR could enhance benefits, i.e., (1) the predicted controller commands that speech recognition engines use can [...] Read more.
Assistant based speech recognition (ABSR) prototypes for air traffic controllers have demonstrated to reduce controller workload and aircraft flight times as a result. However, two aspects of ABSR could enhance benefits, i.e., (1) the predicted controller commands that speech recognition engines use can be more accurate, and (2) the confirmation process of ABSR recognition output, such as callsigns, command types, and values by the controller, can be less intrusive. Both tasks can be supported by unobtrusive eye- and mouse-tracking when using operators’ gaze and interaction data. First, probabilities for predicted commands should consider controllers’ visual focus on the situation data display. Controllers will more likely give commands to aircraft that they focus on or where there was a mouse interaction on the display. Furthermore, they will more likely give certain command types depending on the characteristics of multiple aircraft being scanned. Second, it can be determined via eye-tracking instead of additional mouse clicks if the displayed ABSR output has been checked by the controller and remains uncorrected for a certain amount of time. Then, the output is assumed to be correct and is usable by other air traffic control systems, e.g., short-term conflict alert. If the ABSR output remains unchecked, an attention guidance functionality triggers different escalation levels to display visual cues. In a one-shot experimental case study with two controllers for the two implemented techniques, (1) command prediction probabilities improved by a factor of four, (2) prediction error rates based on an accuracy metric for three most-probable aircraft decreased by a factor of 25 when combining eye- and mouse-tracking data, and (3) visual confirmation of ABSR output promises to be an alternative for manual confirmation. Full article
(This article belongs to the Special Issue Aeronautical Informatics)
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22 pages, 5071 KiB  
Article
Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling
by Mevlut Uzun, Mustafa Umut Demirezen and Gokhan Inalhan
Aerospace 2021, 8(2), 44; https://doi.org/10.3390/aerospace8020044 - 8 Feb 2021
Cited by 22 | Viewed by 14654
Abstract
This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations [...] Read more.
This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy. Full article
(This article belongs to the Special Issue Aeronautical Informatics)
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22 pages, 2957 KiB  
Article
Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay
by Rasoul Sanaei, Brian Alphonse Pinto and Volker Gollnick
Aerospace 2021, 8(2), 28; https://doi.org/10.3390/aerospace8020028 - 25 Jan 2021
Cited by 10 | Viewed by 3027
Abstract
The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other [...] Read more.
The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency. Full article
(This article belongs to the Special Issue Aeronautical Informatics)
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