Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Statistical Modeling and Data-Driven Methods in Aviation Systems
share announcement

Statistical Modeling and Data-Driven Methods in Aviation Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 11427

Special Issue Editor

Dr. John H. Mott

E-Mail Website
Guest Editor
School of Aviation and Transportation Technology, Purdue University, West Lafayette, IN 47907, USA
Interests: statistical process modeling and simulation; aviation applications of Bayesian inference; acquisition and analysis of distributed transportation data

Special Issue Information

Dear Colleagues, 

Statistical modeling is an interdisciplinary subject involving, among other topics, probability theory, statistics, approximation theory, optimization, and computation. It focuses on the development of computer models that accurately translate the operation of real-world processes into algorithms that may be executed by computing devices to provide designers and analysts with information on the operational details of those processes. That information, in turn, allows these individuals to properly optimize processes that can produce the desired results. Valid simulation models based upon measurable data, often collected from widely disparate and distributed sources, are key to minimizing implementation costs, optimizing desired output parameters, and maximizing quality and safety in aviation systems.

The main focus of this Special Issue is the progress of the development and implementation of statistical modeling and machine learning methods in the design and analysis of aviation systems. Such systems can be related to areas such as air traffic management, baggage handling, passenger security, and enhancement of quality and safety in air transportation. Our goal is to facilitate communication on current research and the translation of those research developments into practical applications and tools. We welcome scholars to submit research related to the theory that underlies all forms of statistical modeling and data analysis in order to apply it to aviation systems, and to practical applications thereof. Topics of interest include, but are not limited to, new machine learning methods, statistical methodology, pattern recognition as applied to air vehicle recognition, optimization techniques for air traffic management, advances in computational aspects of simulation, and acquisition and analysis of distributed system data.

Dr. John H. Mott
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. Mathematics 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.

Keywords

  • artificial intelligence
  • big data and analysis
  • machine learning
  • deep learning
  • pattern recognition
  • computer vision
  • data mining
  • statistical modeling applications
  • air traffic systems modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

32 pages, 2250 KiB  
Article
Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems
by Igor Kabashkin
Mathematics 2024, 12(21), 3428; https://doi.org/10.3390/math12213428 - 31 Oct 2024
Cited by 2 | Viewed by 1436
Abstract
The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the [...] Read more.
The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture uses the strengths of both centralized and decentralized learning approaches, combining the broad knowledge capture of foundation models with the privacy-preserving and adaptive nature of federated learning. Through extensive simulations on a representative aircraft fleet, the integrated FM + FL approach demonstrated consistently superior performance compared to standalone implementations across multiple key metrics, including prediction accuracy, model size efficiency, and convergence speed. The framework establishes a robust digital twin ecosystem for real-time monitoring, predictive maintenance, and fleet-wide optimization. Comparative analysis reveals significant improvements in anomaly detection capabilities and reduced false alarm rates compared to traditional methods. The study conducts a systematic evaluation of the benefits and limitations of FM, FL, and integrated approaches in AHMS, examining their implications for system robustness, scalability, and security. Statistical analysis confirms that the integrated approach substantially enhances precision and recall in identifying potential failures while optimizing computational resources and training time. This paper outlines a detailed aviation ecosystem architecture integrating these advanced AI technologies across centralized processing, client, and communication domains. Future research directions are identified, focusing on improving model efficiency, ensuring generalization across diverse operational conditions, and addressing regulatory and ethical considerations. Full article
(This article belongs to the Special Issue Statistical Modeling and Data-Driven Methods in Aviation Systems)
►▼ Show Figures

Figure 1

36 pages, 1445 KiB  
Article
Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models
by Igor Kabashkin
Mathematics 2024, 12(19), 2979; https://doi.org/10.3390/math12192979 - 25 Sep 2024
Cited by 4 | Viewed by 5101
Abstract
This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G [...] Read more.
This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. Full article
(This article belongs to the Special Issue Statistical Modeling and Data-Driven Methods in Aviation Systems)
►▼ Show Figures

Figure 1

12 pages, 14602 KiB  
Article
ChronoVectors: Mapping Moments through Enhanced Temporal Representation
by Qilei Zhang and John H. Mott
Mathematics 2024, 12(17), 2651; https://doi.org/10.3390/math12172651 - 26 Aug 2024
Viewed by 637
Abstract
Time-series data are prevalent across various fields and present unique challenges for deep learning models due to irregular time intervals and missing records, which hinder the ability to capture temporal information effectively. This study proposes ChronoVectors, a novel temporal representation method that addresses [...] Read more.
Time-series data are prevalent across various fields and present unique challenges for deep learning models due to irregular time intervals and missing records, which hinder the ability to capture temporal information effectively. This study proposes ChronoVectors, a novel temporal representation method that addresses these challenges by enabling a more specialized encoding of temporal relationships through the use of learnable parameters tailored to the dataset’s dynamics while maintaining consistent time intervals post-scaling. The theoretical demonstration shows that ChronoVectors allow the transformed encoding tensors to map moments in time to continuous spaces, accommodating potentially infinite extensions of the sequence and preserving temporal consistency. Experimental validation using the Parking Birmingham and Metro Interstate Traffic Volume datasets reveals that ChronoVectors enhanced the predictive capabilities of deep learning models by reducing prediction error for regression tasks compared to conventional time representations, such as vanilla timestamp encoding and Time2Vec. These findings underscore the potential of ChronoVectors in handling irregular time-series data and showcase its ability to improve deep learning model performance in understanding temporal dynamics. Full article
(This article belongs to the Special Issue Statistical Modeling and Data-Driven Methods in Aviation Systems)
►▼ Show Figures

Figure 1

19 pages, 4523 KiB  
Article
A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants
by Zehong Li, Chuyang Yang and Chenyu Huang
Mathematics 2024, 12(1), 53; https://doi.org/10.3390/math12010053 - 23 Dec 2023
Cited by 5 | Viewed by 3471
Abstract
The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual [...] Read more.
The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role. Full article
(This article belongs to the Special Issue Statistical Modeling and Data-Driven Methods in Aviation Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop