Aviation Commons^™

Predicting Success Of Pilot Training Candidates Using Interpretable Machine Learning, Alexandra S. King

Theses and Dissertations

The United States Air Force (USAF) has struggled with a sustained pilot shortage over the past several years; senior military and government leaders have been working towards a solution to the problem, with no noticeable improvements. Both attrition of more experienced pilots as well as wash out rates within pilot training contribute to this issue. This research focuses on pilot training attrition. Improving the process for selecting pilot candidates can reduce the number of candidates who fail. This research uses historical specialized undergraduate pilot training (SUPT) data and leverages select machine learning techniques to determine which factors are associated with …

Examining Failures Of Kc-135 Boom Assemblies Using Survival Analysis, Benjamin D. Miller

Theses and Dissertations

The purposes of this study are to confirm the applicability of survival analysis for predicting recurrent failures of a component of a military aircraft and to provide practical insights to maintenance managers and mission planners. The results of this study also can help the United States Department of Defense improve the CBM+ program. This study was able to predict recurrent failures of the component using Nelson-Aalen cumulative estimates. In addition, this study used a Cox proportional hazards regression model with shared frailty for measuring the effect of covariates on recurrent failures and unidentified heterogeneity in the model, which warranted future …

A Reinforcement Learning Approach To A Beyond Visual Range Air Combat Maneuvering Problem, Caleb A. Taylor

Theses and Dissertations

A one-versus-one air combat maneuvering problem is considered wherein a friendly autonomous aircraft must engage and defeat an adversary autonomous aircraft in a beyond visual range environment. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) is leveraged to model the complex and interdependent operations of aircraft, sensors, and weapons utilized in beyond visual range air combat. We formulate a Markov decision process to obtain high-quality decision policies wherein our autonomous aircraft makes maneuvering and missile firing decisions. We utilize a reinforcement learning solution procedure that implements a linear value function approximation to represent state-decision pairs due to the high …

Workforce Of The Future Begins With Aviation Stem, Lyndsay Digneo

National Training Aircraft Symposium (NTAS)

The United States has always been a world leader in aviation. This leadership position relies on the strength of the American STEM workforce and the quality of the nation’s educational, industrial, and government institutions. Therefore, it is imperative to nurture today’s students to become a well-trained STEM workforce in the future.

The Federal Aviation Administration (FAA) William J. Hughes Technical Center (WJHTC) recognizes that in pursuing its mission of aviation research, engineering, development, and test and evaluation, it is in a unique position to support aviation STEM activities for schools (K-12), post-secondary institutions, and community organizations. In 2016, the Technical …

A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas

National Training Aircraft Symposium (NTAS)

Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes' mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction …

Accuracy Assessment Of The Ebee Using Rtk And Ppk Corrections Methods As A Function Of Distance To A Gnss Base Station, Joseph Cerreta, David Thirtyacre, Peter Miller, Scott S. Burgess, William J. Austin

International Journal of Aviation, Aeronautics, and Aerospace

The use of unmanned aircraft systems to collect data for photogrammetry models has grown significantly in recent years. The accuracy of a photogrammetric model can depend on image georeferencing. The distance from a reference base station can affect the accuracy of the results. Positioning corrections data relies on precise timing measurements of satellite signals. The signals travel through the Earth's atmosphere, which introduces errors due to ionospheric and tropospheric delays. The aim of this research was to examine the eBee X and its global GNSS accuracy by comparing the RTK and PPK methods at different base station distances in photogrammetry …

Stochastic Optimization To Reduce Aircraft Taxi-In Time At Igia, New Delhi, Rajib Das, Saileswar Ghosh, Rajendra Desai, Pijus Kanti Bhuin, Stuti Agarwal

International Journal of Aviation, Aeronautics, and Aerospace

Since there is an uncertainty in the arrival times of flights, pre-scheduled allocation of runways and stands and the subsequent first-come-first-served treatment results in a sub-optimal allocation of runways and stands, this is the prime reason for the unusual delays in taxi-in times at IGIA, New Delhi.

We simulated the arrival pattern of aircraft and utilized stochastic optimization to arrive at the best runway-stands allocation for a day. Optimization is done using a GRG Non-Linear algorithm in the Frontline Systems Analytic Solver platform. We applied this model to eight representative scenarios of two different days. Our results show that without …

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …

The Evolution Of Ai On The Commercial Flight Deck: Finding Balance Between Efficiency And Safety While Maintaining The Integrity Of Operator Trust, Mark Miller, Sam Holley, Leila Halawi

Publications

As artificial intelligence (AI) seeks to improve modern society, the commercial aviation industry offers a significant opportunity. Although many parts of commercial aviation including maintenance, the ramp, and air traffic control show promise to integrate AI, the highly computerized digital flight deck (DFD) could be challenging. The researchers seek to understand what role AI could provide going forward by assessing AI evolution on the commercial flight deck over the past 50 years. A modified SHELL diagram is used to complete a Human Factors (HF) analysis of the early use for AI on the commercial flight deck through introduction of the …

Using Unmanned Aircraft Systems To Investigate The Detectability Of Burmese Pythons In South Florida, Joseph Cerreta, William Austin, David Thirtyacre, Scott S. Burgess, Peter Miller

Journal of Aviation/Aerospace Education & Research

Burmese pythons are an invasive, non-native species of snake to southern Florida and attempts at eradicating the snakes had yielded mixed results. The current rate of detection had been reported as 0.05%. The purpose of this research project was to determine if a UAS equipped with a near-infrared (NIR) camera could be used to detect pythons at a higher rate when compared to a RGB camera. The approach involved collecting 55 images from RGB and NIR cameras, over carcass pythons at flying heights of 3, 6, 9, 12, and 15 meters. A likelihood ratio consisting of a true positive rate …

An Unmanned Aircraft System For Marking Live Power Lines To Prevent Bird Collisions, Mattheuns D. Pretorius, Lourens Leeuwner, Kaajial Durgapersad, Constant Hoogstad, Peter Beckedahl, Michael D. Michael, Kishaylin Chetty

Human–Wildlife Interactions

Collisions with overhead power lines represent a significant cause of mortality for a number of large bird species worldwide. Certain threatened species are severely impacted by power line collisions, particularly those for which there are no other known significant threats. In South Africa, bird collisions with overhead power line cables, including collisions with phase conductors and shield wires, contribute significantly to annual threatened bird species mortalities associated with Eskom power lines. The most practical mitigation measure is to attach bird anti-collision devices as markers on the cables. Current marking methods involve installations by hand, where linemen are hoisted up to …

Directional Speaker Poster, Eugene Ng, Bryan Wong, Ruhaan Das

Student Works

Changi Airport is set to expand with a new terminal, Terminal 5. Currently, many of the airport's processes are manual, requiring a high dependence on staff. This proposal aims to incorporate automation and AI for a smoother passenger experience.

Validated Question Bank For Assessing Pilot Knowledge Of Aviation Weather Appendix: Weather Product Interpretation Questions, Robert Thomas, Cassandra Dommingo, John Kleber, Jackie Mcsorely, Amber Cole, Thomas Guinn, Elizabeth Blickensderfer

General Aviation Weather Display Interpretation

This appendix supplements the Thomas et al. (2022) paper titled “Validated Question Bank for Assessing Pilot Knowledge of Aviation Weather ” which validates a set of weather product interpretation questions that can be used to measure a pilot’s understanding of weather. The assessment consists of 15 weather product interpretation topics which can be administered as a single 65-question survey or, as in the Thomas et al. (2022) study, two assessments of 33 and 32 questions each separated by topic. The set of 65 questions can be found in this appendix along with a table which demonstrates how to separate the …

Pilot Development: An Empirical Mixed-Method Analysis, Jonathan Slottje, Jason Anderson, John M. Dickens, Adam D. Reiman

Faculty Publications

Purpose — Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact future pilot training efforts that lower cost and increase operator quality and proficiency.
Design/methodology/approach — This research employed a mixed-method approach. First, the authors collected data and analyzed 90 C-130J pilots' aviation records and then contextualized this analysis with interviews of experts. Finally, the authors present a modified version of Six Sigma's define–measure–analyze–improve–control (DMAIC) that identifies and reduces the …

Transportation Service Level Impact On Aircraft Availability, Vincent Mclean, Adam D. Reiman

Faculty Publications

Purpose — Aircraft fail to meet mission capable rate goals due to a lack of supply of aircraft parts in inventory where the aircraft breaks. This triggers an order at the repair location. To maximize mission capable rate, the time from order to delivery needs to be minimized. The purpose of this research is to examine the case of three airfields for the order to delivery time of mission critical aircraft parts for a specific aircraft type. Design/methodology/approach — This research captured data from three information systems to assess the order fulfillment process. The data were analyzed to determine the …

Scheduling Aircraft Departures To Avoid Enroute Congestion, Johannes Martinez

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

When scheduled flights are forecast to overcrowd sections of enroute airspace, an air traffic control authority may need to delay departures. Mixed integer linear programming can be used to compute a schedule that resolves the congestion while bringing the sum of all delays to a minimum. Standard linear programming constraint formulations for such scheduling problems, however, have poor run times for instances of realistic size. A new constraint formulation based on cycles and paths through a route graph reduces run times in computational experiments. It shows particularly strong performance for schedules that approach the worst-case solution times in standard formulations.

Monocular Pose Estimation For Automated Aerial Refueling Via Perspective-N-Point, James C. Lynch

Theses and Dissertations

Any Automated Aerial Refueling (AAR) solution requires the quick and precise estimation of the relative position and rotation of the two aircraft involved. This is currently accomplished using stereo vision techniques augmented by Iterative Closest Point (ICP), but requires post-processing to account for environmental factors such as boom occlusion. This paper proposes a monocular solution, combining a custom-trained single-shot object detection Convolutional Neural Network (CNN) and Perspective-n-Point (PnP) estimation to calculate a pose estimate with a single image. This solution is capable of pose estimation at contact point (22m) within 7cm of error and a rate of 10Hz, regardless of …

Real Time Evaluation Of Boom And Drogue Occlusion With Aar, Xiaoyang Wu

Theses and Dissertations

In recent years, Unmanned Aerial Vehicles (UAV) have seen a rise in popularity. Various navigational algorithms have been developed as a solution to estimate a UAV’s pose relative to the refueler aircraft. The result can be used to safely automate aerial refueling (AAR) to improve UAVs’ time-on-station and ensure the success of military operations. This research aims to reach real-time performance using a GPU accelerated approach. It also conducts various experiments to quantify the effects of refueling boom/drogue occlusion and image exposure on the pose estimation pipeline in a lab setting.

Examining Failures Of Kc-135s Using Survival Analysis, Vanessa I. R. Unseth

Theses and Dissertations

The United States Air Force manages an inventory of 396 KC-135 Stratotanker aircraft. With mission capability rates falling and total non-mission capability supply rates increasing, it is necessary to take a deeper look at recurrent failures. The study applies non-parametric and semi-parametric survival models to a dataset retrieved from LIMS-EV to look at the duration(s) until failure for the KC-135. Results of non-parametric models show cumulative failure rates increase as sorties or flight hours increase. In addition, semi-parametric models or Cox proportional hazards models with frailty confirm that locations or air bases are not associated with recurrent failures.

Timestamp Estimation From Outdoor Scenes, Tawfiq Salem, Jisoo Hwang, Rafael Padilha

Annual ADFSL Conference on Digital Forensics, Security and Law

The increasing availability of smartphones allowed people to easily capture and share images on the internet. These images are often associated with metadata, including the image capture time (timestamp) and the location where the image was captured (geolocation). The metadata associated with images provides valuable information to better understand scenes and events presented in these images. The timestamp can be manipulated intentionally to provide false information to convey a twisted version of reality. Images with manipulated timestamps are often used as a cover-up for wrongdoing or broadcasting false claims and competing views on the internet. Estimating the time of capture …

Anatomy Of An Internet Hijack And Interception Attack: A Global And Educational Perspective, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Annual ADFSL Conference on Digital Forensics, Security and Law

The Internet’s underlying vulnerable protocol infrastructure is a rich target for cyber crime, cyber espionage and cyber warfare operations. The stability and security of the Internet infrastructure are important to the function of global matters of state, critical infrastructure, global e-commerce and election systems. There are global approaches to tackle Internet security challenges that include governance, law, educational and technical perspectives. This paper reviews a number of approaches to these challenges, the increasingly surgical attacks that target the underlying vulnerable protocol infrastructure of the Internet, and the extant cyber security education curricula; we find the majority of predominant cyber security …

Ga Pilot Perceptions Of Speech Systems To Transcribe And Submit Pireps, Deborah S. Carstens Ph.D., Pmp, Michael S. Harwin, J.D., M.S., Tianhua Li, Ph.D., Brandon J. Pitts, Ph.D., Mel Futrell, M.A., Barrett Caldwell, Ph.D.

International Journal of Aviation, Aeronautics, and Aerospace

Flying into hazardous weather can be a cause of aviation incidents and accidents. Accidents involving general aviation (GA) pilots who are not instrument rated who fly into instrument meteorological conditions (IMC) are often fatal. Pilot weather reports (PIREPs) can increase the accuracy and timeliness of current and forecasted weather conditions. They are an essential tool used by pilots to avoid flying into hazardous weather as well as meteorologists to develop and update aviation forecasts. Thus, a large number of accurate PIREPs with the best source of current weather coming from pilots and air traffic controllers are needed. Pilots are often …

A Lightweight Reliably Quantified Deepfake Detection Approach, Tianyi Wang, Kam Pui Chow

Annual ADFSL Conference on Digital Forensics, Security and Law

Deepfake has brought huge threats to society such that everyone can become a potential victim. Current Deepfake detection approaches have unsatisfactory performance in either accuracy or efficiency. Meanwhile, most models are only evaluated on different benchmark test datasets with different accuracies, which could not imitate the real-life Deepfake unknown population. As Deepfake cases have already been raised and brought challenges at the court, it is disappointed that no existing work has studied the model reliability and attempted to make the detection model act as the evidence at the court. We propose a lightweight Deepfake detection deep learning approach using the …

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation, Jyoti Fakirah, Lauhim Mahfuz Zishan, Roshni Mooruth, Michael L. Johnstone, Wencheng Yang

Annual ADFSL Conference on Digital Forensics, Security and Law

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS …

Human-Controlled Fuzzing With Afl, Maxim Grishin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Fuzzing techniques are applied to reveal different types of bugs and vulnerabilities. American Fuzzy Lop (AFL) is a free most popular software fuzzer used by many other fuzzing frameworks. AFL supports autonomous mode of operation that uses the previous step output into the next step, as a result fuzzer spends a lot of time analyzing minor code sections. By making fuzzing process more focused and human controlled security expert can save time and find more bugs in less time. We designed a new module that can fuzz only the specified functions. As a result, the chosen ones will be inspected …

The Amorphous Nature Of Hackers: An Exploratory Study, Kento Yasuhara, Daniel Walnycky, Ibrahim Baggili, Ahmed Alhishwan

Annual ADFSL Conference on Digital Forensics, Security and Law

In this work, we aim to better understand outsider perspectives of the hacker community through a series of situation based survey questions. By doing this, we hope to gain insight into the overall reputation of hackers from participants in a wide range of technical and non-technical backgrounds. This is important to digital forensics since convicted hackers will be tried by people, each with their own perception of who hackers are. Do cyber crimes and national security issues negatively affect people’s perceptions of hackers? Does hacktivism and information warfare positively affect people’s perception of hackers? Do individual personality factors affect one’s …

Digital Forensics For Mobility As A Service Platform: Analysis Of Uber Application On Iphone And Cloud, Nina Matulis, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Uber is a ride-hailing smartphone application (app) that allows users to order a ride in a highly efficient manner. The Uber app provides Mobility as a Service and allows users to easily order a ride in a private car with just a few clicks. Uber stores large amounts of data on both the mobile device the app is being used on, and in the cloud. Examples of this data include geolocation data, date/time, origin/destination addresses, departure/arrival times, and distance. Uber geolocation data has been previously researched to investigate the privacy of the Uber app; however, there is minimal research relating …

Smart Home Forensics: Identifying Ddos Attack Patterns On Iot Devices, Samuel Ho, Hope Greeson, Umit Karabiyik

Annual ADFSL Conference on Digital Forensics, Security and Law

Smart homes are becoming more common as more people integrate IoT devices into their home environment. As such, these devices have access to personal data on their homeowners’ networks. One of the advantages of IoT devices is that they are compact. However, this limits the incorporation of security measures in their hardware. Misconfigured IoT devices are commonly the target of malicious attacks. Additionally, distributed denial-of-service attacks are becoming more common due to applications and software that provides users with easy-to-use user interfaces. Since one vulnerable device is all an attacker needs to launch an attack on a network, in regards …

Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av, Denis Pogonin, Igor Korkin, Phd

Annual ADFSL Conference on Digital Forensics, Security and Law

Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers’ techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. …