11-20 of 238 results
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Development of a Safety Performance Decision-Making Tool for Flight Training Organization
PI Marisa Aguiar
CO-I Carolina Anderson
Title 14 of the Code of Federal Regulations (CFR) Part 141 flight training organizations are actively pursuing ways to increase operational safety by introducing advanced risk assessment and decision-making techniques. The purpose of the dissertation was to create and validate a safety performance decision-making tool to transform a reactive safety model into a predictive, safety performance decision-making tool, specific to large, collegiate Title 14 CFR Part 141 flight training organizations, to increase safety and aid in operational decision-making. The validated safety decision-making tool uses what-if scenarios to assess how changes to the controllable input variables impact the overall level of operational risk within an organization’s flight department.
Utilizing SPIs determined to be most indicative of flight risk within large, collegiate flight training organizations, a predictive, safety performance decision-making tool was developed utilizing Monte Carlo simulation. In a high-risk system beset with uncertainty, applying Monte Carlo simulation addresses the need to accommodate uncontrollable inputs into the model in a manner that enables the model to produce meaningful output data. This research utilizes the validated equations drawn from the non-statistical model developed by Anderson, Aguiar, Truong, Friend, Williams, & Dickson (2020) for the mathematical inputs driving the computational nodes, including the SPIs, as the foundation to develop the safety performance decision-making tool.
The probability distributions of the uncontrollable inputs were drawn from a sample of operational data from September 2017 to September 2019 from a large, collegiate 14 CFR Part 141 flight training organization in the southeastern United States. The study conducted simulation runs based on true operational ranges to simulate the operating conditions possible within large, collegiate CFR Part 141 flight training organizations with varying levels of controllable resources including personnel (Aviation Maintenance Technicians and Instructor Pilots) and expenditures (active flight students and available aircraft).
The study compared the output from three different Verification Scenarios—each using a unique seed value to ensure a different sample of random numbers for the uncontrollable inputs. ANOVA testing indicated no significant differences appeared among the three different groups, indicating the results are statistically reliable.
Four What-if Scenarios were conducted by manipulating the controllable inputs. Mean probability was the key output and represents the forecasted level of operational risk on a standardized 0-5 risk scale for the Flight Score, Maintenance Score, Damage and Related Impact, and an Overall Risk Score. Results indicate the lowest Overall Risk Score occurred when the level of personnel was high yet expenditures were moderate.
Changes to the controllable inputs are reflected by variations to the outputs demonstrating the utility and potential for the safety performance decision-making tool. The outputs could be utilized by safety personnel and administrators to make more informed safety-related decisions without expending unnecessary resources. The model could be adapted for use in any CFR Part 141 flight training organization with data collection capabilities and an SMS by modifying the input value probability distributions to reflect the operating conditions of the selected 14 CFR Part 141 flight training organization.
Categories: Graduate
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Novel n x n Bit-Serial Multiplier Architecture Optimized for Field Programmable Gate Arrays (FPGA)
PI Akhan Almagambetov
CO-I David Feinauer
CO-I Holly Ross
Bit-serial multipliers have a variety of applications, from the implementation of neural networks to cryptography. The advantage of a bit-serial multiplier is its relatively small footprint, when implemented on a Field Programmable Gate Array (FPGA) device. Despite their apparent advantages, however, traditional bit-serial multipliers typically require a substantial overhead, in terms of component usage, which directly translates to a large area of the chip being reserved while many of those resources are unused.
This research addresses the possibility of an efficient two's complement bit-serial multiplier (serial-serial multiplier) implementation that would minimize flip-flop and control set usage on an FPGA device, thereby potentially reducing the overall area of the circuit. Since the proposed architecture is modular, it functions as a "generic" definition that can be effortlessly implemented on an FPGA device for any number of bits.
Categories: Faculty-Staff
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Organizational Design of Secondary Aviation/Aerospace/Engineering Career Education Programs
PI Susan Archer
CO-I David Esser
Modern nations operate within a global economy, relying heavily on the aviation industry for efficient and effective transportation of passengers and goods. The Boeing 2018 Pilot and Technical Outlook Report indicated that over the next 20 years, the aviation industry will need almost two and a half million new aircrew and maintenance employees to meet anticipated global demand. The industry will also need engineers, aviation managers, and workers in other aviation and aerospace disciplines. Aviation and aerospace jobs require solid backgrounds in mathematics, science, and technology; the development of pre-college aviation / aerospace / engineering career education programs would presumably enhance student preparation in these areas and increase the workforce pipeline for the industry. The goal of this study was to identify and evaluate the underlying organizational factors of successful secondary aviation / aerospace / engineering career education programs, through application of measures traditionally associated with organizational theory.
Analysis of collected data involved exploratory factor analysis to identify underlying factors, confirmatory factor analysis to verify significant relationships between manifest variables and latent constructs and to ensure a good-fitting measurement model, and structural equation modeling to identify significant relationships between latent constructs and achieve the best-fitting model of these relationships for the collected data. Variables were Likert-scale responses to literature-based survey items associated with organizational vision, leadership, communication, collaboration, decision-making, flexibility, accountability, resource availability, motivation, and learning. Additionally, participants were invited to provide comments related to any of the survey items to explain or add detail to their response selection. These comments were reviewed both as they related to individual survey items and for detection of underlying themes. Participants in the study comprised stakeholders associated with career education programs in the disciplines of interest, including students, parents, alumni, school / program faculty and staff, industry members, and advisory board members.
Hypothesis testing results suggested that the most important factor in predicting success for an aviation / aerospace / engineering academy or program is personal motivation related to learning. Though other underlying factors, including leadership / collaborative environment, organizational accountability, and resource availability were clearly related to perceived program success, they appeared to have indirect relationships with success. It is also important to recognize that a paired qualitative analysis of participant comments generated themes that transcended survey item topics, and the identification of these themes supported the conclusions from hypothesis testing regarding underlying factors. Personal motivation was the most commonly recurring theme in comments, supporting the hypothesis testing result indicating its predictive strength for an organization’s success.
Understanding the constructs that are most closely related to an organization’s success, as they are perceived by its stakeholders, offers current program leaders and groups interested in creating new programs evidence they can use to design the frameworks for their programs. Anticipated workforce shortages warrant study of how to increase the number of candidates not only in post-secondary academic and training programs, but to shift recruiting earlier through implementation of quality secondary-level programs that are established on a foundation of research-based strategies for success.
Categories: Graduate
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Increasing student learning and engagement using a TV series: Leadership in the Final Frontier
PI Anke Arnaud
Educators are continuously concerned with developing innovative and effective teaching methodology to increase student learning and engagement. This study is designed to assess the effectiveness of an innovative instructional methodology, using a TV series to teach and develop leadership understanding, skills and knowledge.
During a semester long class on leadership, students were taught abstract leadership concepts and theories using Episodes from the Star Trek Series. We used inductive reasoning methodology, watching an episode of Star Trek and then developing leadership theory, and deductive reasoning methodology, learning about a leadership theory and then analyzing the theory using an episode of Star Trek, to develop leadership understanding, skills and knowledge. Student journal entries, questionnaires on student engagement and learning, and end of course evaluations were used to assess the effectiveness of the teaching methodology. Results support our expectation that student learning and engagement can be enhanced using the effective application of TV episodes.Categories: Faculty-Staff
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Automated Homework System: Improving Teaching Quality by Utilizing Technology
PI Farshid Azadian
One of the essential elements in improving the students' skills and abilities and helping them to better understand the course materials is homework assignments. A well designed and purposeful homework not only enhances the student's understanding but also may provide valuable feedback to instructors.
However, the process of designing and grading homework assignments are laborious from the instructor's perspective for large classes. Moreover, similarity of the assignments for all students set the stage for potential plagiarism which when is left undetected can set an undesirable ethical precedence.
In this research, our objective is to provide an automated procedure that assists instructors to utilize homework assignments more productively and reduces the possibility of unethical practices. Our main idea is to create a tool that uses the existing teaching resources to produce individual (non-identical) homework assignments for each student, automatically grade them and provide feedback to students.Categories: Faculty-Staff
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Gravitation
PI Quentin Bailey
CO-I Andri Gretarsson
CO-I Brennan Hughey
CO-I Michele Zanolin
CO-I Preston Jones
Einstein’s theory of General Relativity offers a remarkable description of gravity as curved space and time. Many of the consequences of this theory have been confirmed, and some are used daily, such as the gravitational redshift effect on GPS satellite atomic clocks. In 2015, the first observation of a gravitational wave from two inspiraling black holes occurred using the gravitational wave observatories as part of the worldwide LIGO-VIRGO collaboration. This discovery won the Nobel prize, and the observations of these events have continued, including a multi-messenger event of two colliding neutron stars.
Embry-Riddle Prescott faculty and student researchers are part of the LIGO-VIRGO collaboration and work on aspects of detecting and studying gravitational waves. Faculty and students also study more broadly tests of the foundational principles of General Relativity, such as spacetime symmetries like Lorentz symmetry. These tests include gravitational wave observation but also solar system tests like short-range gravity and lunar laser ranging. One of the long-standing problems in gravity research is the connection between gravity and quantum field theory. Our faculty is actively working on this problem and, in particular, the relation between gravity and electromagnetism. There are both theorists and experimentalists among the faculty at ERAU Prescott. Most faculty receive funding from the National Science Foundation and regularly publish articles in to journals, many with students involved.
Categories: Faculty-Staff
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Predicting General Aviation Accidents Using Machine Learning Algorithms
PI Bradley Baugh
CO-I Bruce Conway
Aviation safety management is implemented through reactive, proactive, and predictive methodologies. Unlike reactive and proactive safety, predictive safety can predict the next accident and enable prevention before an actual occurrence. The study outlined here promotes predictive safety management through machine learning technologies using large amounts of data to facilitate predictive modeling.
The study addresses efforts to reduce General Aviation accidents, an effort that was renewed in earnest with the Federal Aviation Administration’s 1998 Safer Skies Initiative. Over the past 22 years, the General Aviation fatality rate has decreased. However, accidents still happen, and there is some evidence showing the number of accidents, representing hazard exposure, is increasing. The accident data suggest that the aviation community still has more to learn about the variables involved in an accident sequence.
The purpose of the study was to conduct an exploratory data-driven examination of General Aviation accidents in the United States from January 1, 1998, to December 31, 2018, using machine learning and data mining techniques. The goal was to determine what model best predicts fatal and severe injury aviation accidents and further, what variables were most important in the prediction model.
The study sample comprised 26,387 fixed-wing general aviation accidents accessed through the publicly accessible National Transportation Safety Board Aviation Accident Database and Synopses archive. Using a mixed-methods approach, the study employed both unstructured narrative text and structured tabular data within the predictive modeling. First, the accident narratives were culled using text mining algorithms to develop text-based quantitative variables. Next, data mining algorithms were used to develop models based on both text- and data-based variables derived from the accident reports.
Five types of machine learning models were created using SAS® Enterprise Miner™, including the Decision Tree, Gradient Boosting, Logistic Regression, Neural Network, and Random Forest. Additionally, three broad sets of variables were used in modeling, including text-only, data-only, and a combination of text and data variables. Three models, Logistic Regression (text-only variables), Random Forest (text-only variables), and Gradient Boosting (text and data variables), emerged with a similar prediction capability. The top six variables within the models were all text-based covering Medical, Slow-flight and stalls, Flight control, IMC flight, Weather factors, and Flight hours topics. The Logistic Regression (Text) model was selected as the champion model: Misclassification Rate = 0.098, ROC Index = 0.945, and Cumulative Lift = 3.46.
The results of the study provide insights to the entire General Aviation community, including government, industry, flight training, and the operational pilot. Specific recommendations include the following areas: 1) improve the quality and usefulness of accident reports for machine learning applications, 2) investigate ways to capture and publish more open-source flight data for use in safety modeling, 3) invest in additional medical education and find ways to address impairing medications and high risk medical conditions, 4) renew efforts on improving flight skills and combatting decision-based errors, 5) emphasize the importance of weather briefings, pre-flight planning, and weather-based risk management, and 6) create an aviation-specific corpus for text mining to improve text analysis and transformation.
Categories: Graduate
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Aircraft Boarding Strategies
PI Massoud Bazargan
Airlines today employ various strategies to cut costs and become lean and efficient.
One of the ways that this can be achieved is by improving the boarding process since airplanes only make money while they are in the air. This paper uses simulation approach to deal specifically with the boarding strategies in use today by the major airlines. To properly simulate the boarding process, the simulation model accounts for passenger interferences (aisle & seat), the time it takes to stow away baggage, and the passenger arrival rate through the main cabin door. We applied our simulation model to study the AirTran Boeing 737-700 short haul aircraft. We looked at five major boarding strategies from random to the customary back to front and the results are very encouraging. Our analyses identifies that the arrival rate has an effect on the total boarding time and that the Reverse Pyramid and Window middle Aisle (WilMA) were among the efficient boarding strategies.Categories: Faculty-Staff
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Aircraft Replacement Strategy
PI Massoud Bazargan
The analyses of this study attempts to address:- How the cost data relevant to this study, such as aircraft market values, lease prices, operations and maintenance costs were compiled and analyzed as the airlines do not or cannot provide them.
- Identify aircraft replacement strategies for the airlines and explore their differences according to their business models.
- Compare and contrast the recommended and current aircraft replacement strategies for the airlines.
- Identify decisions with respect to lease and/or buy for the airlines and how sensitive these strategies are to changes to aircraft values and lease prices.
- Explore future fleet diversity for the airlines and how sensitive these strategies are to their existing and on-order fleet.
- How the fixed costs pertaining to aircraft buy and/or lease compare and contrast with variable costs such as operations and maintenance over the planning horizon.
Categories: Faculty-Staff
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A Database Management System for General Aviation Safety
PI Massoud Bazargan
CO-I Michael Williams
CO-I Alan Stolzer
The research team at Embry-Riddle proposes to conduct a series of analyses to find patterns and associations among general aviation (GA) accidents and incidents.
This research work is intended to provide the FAA with analyses of fatal and non-fatal accidents by examining the NTSB database and recommending strategies to mitigate risks associated with such events.Some of the potential studies that the team proposes to conduct include: analysis of primary ten causes leading to fatal and non-fatal accidents for each region by aircraft complexity and pilot demographics, statistical analyses on existing General Aviation accidents and incidents NTSB database on a national and regional basis to identify associations and patterns between flight elements and risk factors. This study will address multiple factors including pilots' demographics, light conditions, weather conditions and equipment used.Categories: Faculty-Staff
11-20 of 238 results