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Embry-Riddle Aeronautical University

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2023

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Articles 1 - 12 of 12

Full-Text Articles in Business

Is The Declining Birthrate Really An Issue For The Economy?, Harsh Ramesh Pednekar, Theodore Lee, Darrion Chin Dec 2023

Is The Declining Birthrate Really An Issue For The Economy?, Harsh Ramesh Pednekar, Theodore Lee, Darrion Chin

Introduction to Research Methods RSCH 202

This study aims to explore the complex implications of declining birth rates on the economy, focusing on GDP per capita as a crucial metric, and aims to uncover both potential opportunities and challenges stemming from this demographic transformation using regression analysis. Using a quantitative methodology and secondary data from OECD.stat, World Population Review, and World Bank, the study explores the relationship between declining birth rates and economic impacts. GDP per capita serves as an essential dependent variable, and it accounts for control variables such as labour force participation, literacy, and education levels, child dependence ratio, and physical capital. Past studies …

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Six-Degree-Of-Freedom Optimal Feedback Control Of Pinpoint Landing Using Deep Neural Networks, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua Nov 2023

Six-Degree-Of-Freedom Optimal Feedback Control Of Pinpoint Landing Using Deep Neural Networks, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. …

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Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua Oct 2023

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

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Predicting Dynamic Fragmentation Characteristics From High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data And Machine Learning, Katharine Larsen, Riccardo Bevilacqua, Omkar S. Mulekar, Elisabetta L. Jerome, Thomas J. Hatch-Aguilar Aug 2023

Predicting Dynamic Fragmentation Characteristics From High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data And Machine Learning, Katharine Larsen, Riccardo Bevilacqua, Omkar S. Mulekar, Elisabetta L. Jerome, Thomas J. Hatch-Aguilar

Student Works

To continue space operations with the increasing space debris, accurate characterization of fragment fly-out properties from hypervelocity impacts is essential. However, with limited realistic experimentation and the need for data, available static arena test data, collected utilizing a novel stereoscopic imaging technique, is the primary dataset for this paper. This research leverages machine learning methodologies to predict fragmentation characteristics using combined data from this imaging technique and simulations, produced considering dynamic impact conditions. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, …

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Overview Of Low-Cost Carriers In Russia And Post-Soviet States, Tamilla Curtis, Dawna L. Rhoades Jun 2023

Overview Of Low-Cost Carriers In Russia And Post-Soviet States, Tamilla Curtis, Dawna L. Rhoades

Publications

The low-cost carrier's model made remarkable gains across the globe in the prior decade, although growth was uneven domestically and intraregionally. Within this region, there are significant differences in overall country size, the size of the domestic aviation market, and the number of carriers serving the market. The largest and most developed market is in Russia, but rest of the region also experienced growth in economy airlines' activity as they discovered the power of the model to expand aviation access and lower costs. The success of low-cost carriers, however, has been halting and hampered by government decisions on foreign investment, …

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Evidence-Based Managerial Decision-Making With Machine Learning: The Case Of Bayesian Inference In Aviation Incidents, Aaron Glassman, Burak Cankaya, Kazim Topuz, Dursun Delen May 2023

Evidence-Based Managerial Decision-Making With Machine Learning: The Case Of Bayesian Inference In Aviation Incidents, Aaron Glassman, Burak Cankaya, Kazim Topuz, Dursun Delen

Publications

Understanding the factors behind aviation incidents is essential, not only because of the lethality of the accidents but also the incidents’ direct and indirect economic impact. Even minor incidents trigger significant economic damage and create disruptions to aviation operations. It is crucial to investigate these incidents to understand the underlying reasons and hence, reduce the risk associated with physical and financial safety in a precarious industry like aviation. The findings may provide decision-makers with a causally accurate means of investigating the topic while untangling the difficulties concerning the statistical associations and causal effects. This research aims to identify the significant …

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Connecting Organizational Learning Strategies To Organizational Resilience, Gordon R. Haley Apr 2023

Connecting Organizational Learning Strategies To Organizational Resilience, Gordon R. Haley

Publications

Purpose: The objective of this study is to analyze the conceptual and domain overlap of organizational learning and organizational resilience; specifically, the adaptation or renewal domain in organizational resilience. From the findings, strategies to foster collective learning leading to organizational resilience are identified and outlined.

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Technology Within Cultures: Segmenting The Wired Consumers In Canada, France, And The Usa, Maria Petrescu, Aidin Namin, Marie-Odile Richard Apr 2023

Technology Within Cultures: Segmenting The Wired Consumers In Canada, France, And The Usa, Maria Petrescu, Aidin Namin, Marie-Odile Richard

Publications

This paper uses a state-of-the-art quantitative modeling approach to latent class analysis to analyze American, Canadian, and French consumers’ perception of technology-based products and their cultural values. It identifies hidden segments of consumers based on technology adoption propensity, cosmopolitan characteristics, and identification with the global consumer culture. The study emphasizes the diversity and variability between and among countries regarding localism, globalism, cosmopolitanism, and the global consumer culture. The framework provides a new way to evaluate modern consumers and reflects the combination of national/regional cultural characteristics and global culture elements while highlighting the relevance of modern technologies and communication methods in …

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Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman Jan 2023

Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman

Publications

Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "high-impact, low-probability" (HILP) events. Aviation incident reports are inspected by …

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Integrated Dynamic Airline Route And Schedule Optimization, Bayan Begaliyeva Jan 2023

Integrated Dynamic Airline Route And Schedule Optimization, Bayan Begaliyeva

Student Works

By harnessing real-time and historical data in conjunction with advanced AI technologies, this project revolutionizes route and schedule planning, leading to enhanced efficiency, cost reduction, and an improved passenger experience.

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Connecting Organizational Learning Strategies To Organizational Resilience, Stephanie Douglas, Gordon Haley Jan 2023

Connecting Organizational Learning Strategies To Organizational Resilience, Stephanie Douglas, Gordon Haley

Publications

The objective of this study is to analyze the conceptual and domain overlap of organizational learning and organizational resilience; specifically, the adaptation or renewal domain in organizational resilience. From the findings, strategies to foster collective learning leading to organizational resilience are identified and outlined.

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Man Vs Machine – Detecting Deception In Online Reviews, Maria Petrescu Jan 2023

Man Vs Machine – Detecting Deception In Online Reviews, Maria Petrescu

Publications

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights …

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