For the last six years, I have designed and delivered a special course: Research Project in Industrial Mathematics. This course was initiated by NSF-funded MAA PIC Math (Preparation for Industrial Careers in Mathematics) Program in 2017. This course provides a unique educational experience by introducing students to the ways that advanced mathematics and statistics are used in the real world to analyze and solve complex problems. The students work in small teams on real problems, which are directly provided by business and industry. Most of the projects are data enabled and machine-learning oriented.
PROJECTS ADVISED
| Summer 2021 |
Future in the VTOL and EVTOL Industries
Industrial Sponsor: OneSky, MicaPlex, Daytona Beach, FL
This project was supported by ERAU REU DEIM program
Description and resultsStudents: ERAU REU: Research Projects in Data-Enabled Industrial Mathematics (Summer 2021) John Redman ('22) Aerospace Engineering Description: OneSky Flight supports the technology needs of seven established private aviation brands; Flexjet, Sentient Jet, FXAIR, PrivateFly, AAG, Halo, and Sirio. With AAG and Halo as recent acquisitions, research must be done around the VTOL/EVTOL industry, regarding its growth and evolution. The purpose of the project is to focus on this research and draw valuable conclusions: 1. Assess the pertinent industry data required and aggregate this data. 2. Predict the next hotspot locations of greatest importance and pace of growth in the VTOL/EVTOL industry. 3. Determine what early investments in infrastructure, marketing, aircraft placement, etc. should be made in future hotspots to capture maximum market share. A numerical analysis was done to see how the past could predict the future, while it showed EV Chagrining Stations have been exponentially rising and Heliports have been declining. Visuals on map further concluded that hotspots were located on the east and west coasts. For travel patterns, data showed there were differences between Private Jets and VTOLs since less than 3% of the data were flights 30 minutes or less. This data set further proved where the most used airports were, leading to recommendations to be made to OneSky. The recommendations for future hotspots were for OneSky to focus on east and west coast for both VTOLs and EVTOLs. Since OneSky own AGG and is located on the east coast, they should expand their VTOL fleet to the west coast. For EVTOLs, both New York City and Los Angles, California look like the most promising cities for them to start the industry. Also, these cities are in close proximity to other cities meaning future expansion could be possible after proven success. Major outcomes:
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| Summer 2021 |
Understanding Regional and Local Climate Dynamics from Gridded Data Sets
Industrial Sponsor: Pacific Northwest National Laboratory, Richland, WA
This project was supported by ERAU REU DEIM program
Description and resultsStudents: ERAU REU: Research Projects in Data-Enabled Industrial Mathematics (Summer 2021) Penunuri Grace ('22) University of Portland - Applied Mathematics Description: he Disruptive Technologies Group in the National Security Directorate of Pacific Northwest National Laboratory is enthusiastic to collaborate with students participating in the Embry- Riddle Aeronautical University Research Experience for Undergraduates (REU) on a research project whose goal is to develop modern analytics approaches to analyzing regional and local climate dynamics. Characterizing the natures of the changes in temperature and precipitation helps us to understand the mechanisms behind the evolution of regional climates, which do not always track with the larger-scale trends. This can help with regional conservation efforts and help to protect some of the most fragile environments in the US. The task posed by the Pacific Northwest National Laboratory is to characterize the nature of climate change in the Northeast United States through gridded data sets collected from the PRISM website. By looking at local climate trends we can locate specific environments that are most effected by climate change that may be overlooked on larger scale climate change research. The main goal of this project is to compare, contrast and analyze the quantities of interest (QOI’s) over the span of 1980-2020 using the data from the PRISM Climate Group in order to recognize trends in regional areas. The QOI’s are the minimum, maximum, and mean temperatures as well as the total precipitation of the selected regions. To analyze the years 180-2020 the data. From 1951-2020 needs to be accessed and analyzed. To best analyze the time period the 30 year normal of each year were computed. After this computation the spatial and temporal analysis of the time period was conducted. In conclusion there are spots in the Northeast region that do not follow the trends that the region as a whole experience. These local environments are lost in larger scale research, overpowered by the thousands of other values that follow the normal trend. It is important to scale down research in Climate change so that these fragile local environments are not overlooked. Major outcomes:
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| Summer 2021 |
Social Media Analysis of High Net-Worth Individuals
Industrial Sponsor: OneSky, MicaPlex, Daytona Beach, FL
This project was supported by ERAU REU DEIM program
Description and resultsStudents: ERAU REU: Research Projects in Data-Enabled Industrial Mathematics (Summer 2021) Piper Hasenberg ('22) University of West Florida - Mathematics Description: OneSky Flight supports the technology needs of seven established private aviation brands; Flexjet, Sentient Jet, FXAIR, PrivateFly, AAG, Halo, and Sirio. The majority of clients for these companies are high net-worth individuals, who travel for business and pleasure. Due to the unique online behaviors of these clients, research must be done on their social media preferences and activity. The purpose of the project is to focus on this research and draw valuable conclusions: Determine the most critical social media features and their degree of importance/value. Determine the collection of features that would create the optimal experience for these individuals who are all part of the Flexjet Owner club. Ensure attention is given to how differences in net-worth, age, industry, etc. affect the importance of features. Note that many of these individuals have been known to value privacy the most when it comes to their data. We are confident that the suggested features and services can be combined to create a successful platform for the intended target audience being Flexjet’s client base. The nine platforms analyzed show that social media primarily targets audiences that are young in age and have a household income of around $65,000. Due to the combination of these two demographics, it can be concluded that social media does not target the Flexjet client base, so the users of the proposed platform would not use the platform if it was primarily created as a social media platform. With a combination of a concierge and user-to-user interactions through social media features, each user will have a unique experience tailored to their wants and needs. Through this research, it has been interesting to see how social media is targeting certain demographics and how high-net-worth individuals may seem as though they are active on social media, but there are few individuals in the target demographic that actually are. Major outcomes:
| Spring - Summer 2021 |
Enhanced Deep Learning with Applications to Diabetic Retinopathy and National Security
Industrial Sponsor: Nevada National Security Site, Las Vegas, NV
This project was supported by ERAU REU DEIM program
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Danaher Brian ('22) DB BS Aerospace Engineering/2nd Major Computational Math Research Project in Industrial Mathematics Zheng Ying ('23) DB BS Computational Mathematic - Data Science Track Lillian Borchardt (‘23)DB BS Computational Mathematic - Engineering Application Track Theryn Compoc (‘21) DB BS Aerospace Engineering/Daytona Comp Math Minor ERAU REU: Research Projects in Data-Enabled Industrial Mathematics (Summer 2021) Brown Mathew ('22) University of Toledo - Pure Mathematics Zheng Ying ('23) DB BS Computational Mathematic - Data Science Track Danaher Brian ('22) DB BS Aerospace Engineering/2nd Major Computational Math Description: The Signal Processing and Applied Mathematics research group at the Nevada National Security Site (NNSS) is pleased to partner with students at Embry-Riddle Aeronautical University (ERAU) to study and develop a novel pooling method for convolutional neural networks, using a publicly available data set. Most advances in CNNs have been made in the last 10 years and it is still a very interesting, quick-moving field of research. While most image data sets perform well under existing CNN architectures, there are still challenging data sets which require further advancements before they can be considered “solved" problems. The main goal of this project was to examine pooling layer using new algorithm - variable stride. Since it has been developed recently, it has not been used or tested compared to the other pooling methods. To examine this method, a small data set of diabetic retinopathy eye images were given to use to implement into the convolutional neural network (CNN). Variable stride has been evaluated using metrics such as the ROC curve. Along with the examination, variable stride was compared to AvgPool and MaxPool. Major outcomes:
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| Spring - Summer 2021 |
Developing Computational Models to Detect Radiation in Urban Environments
Industrial Sponsor: Pacific Northwest National Laboratory, Richland, WA
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Summer Undergraduate Research Fellowship Gachancipa Parga Jose "Nicholas" ('21) DB BS Aerospace Engineering/2nd Major Computational Math Description: The Disruptive Technologies Group in the National Security Directorate of Pacific Northwest National Laboratory is enthusiastic to collaborate with students at Embry-Riddle Aeronautical University on a research project whose goal is to develop computational models for analyzing data collected by mobile radiation detectors in urban environments. New methods could assist incident response personnel in detecting illicit materials before they cause emergencies and potential subject urban residents to ionizing radiation. In public areas and events, the presence of a radioactive source can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams. The purpose of this project is to build a computational model capable of detecting and characterizing radiation sources, using machine learning methods and statistical analysis. Specifically, the project explores the use of signal processing techniques and artificial neural networks for the analysis of radiation data. The computational model detects unnatural radiation events in urban environments, which may have disastrous consequences if undetected or ignored. Moreover, the model identifies the types of radioactive sources, classifying them as innocuous or harmful, and discerning between weapons-grade material and radioactive isotopes used in medical or industrial settings. Major outcomes:
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| Spring 2021 |
Maintenance Prediction Project
Industrial Sponsor: OneSky, MicaPlex, Daytona Beach, FL
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Tyler Wise ('21) DB BS Computational Mathematic Description:Maintenance and upkeep of private aviation fleets is essential to the success of the business surrounding them. Advance knowledge of when mandated inspections may be required allow for improved scheduling of these events resulting in improved fleet efficiency. Additional awareness of potential unscheduled failures will further contribute to this pursuit, as well as increased customer satisfaction due to a reduction in maintenance-related delays. For this project, data from the Flexjet fleet of aircraft, as well as the trips conducted on said aircraft, was used. The data is dynamically loaded at runtime up to the present time beginning from August 20, 2018. As of the time of this writing, the fleet includes 159 aircraft spanning 14different models from 7 different manufacturers. On average, this fleet will operate 130 customer flights daily. These flights account for approximately 280 hours of the 410 average hours that the fleet operates every day. Additionally, there are, on average, 10 aircraft in maintenance on any given day. This reduces the usable portion of the fleet since those aircraft are unusable while in service. This algorithm is designed to take in information about booking data as a time-series that illustrate show the hours booked for a single aircraft type grew as the date of the trips approaches. The existing product aims to predicting how many hours customers will have booked for a set of flying days. The existing software will project from the day it is run up through 60 days into the future. For consistency with the previous product, this characteristic is carried forward into this project. By re-architecting the existing algorithm to output a different set of values that correlate to the amountof hours an aircraft type will fly for the set of flight days, the hours can be allocated to the hours among that type and projections regarding maintenance can be made. Major outcomes:
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| Spring 2021 |
Predictive Analytics for Dynamic Pricing in Private Aviation
Industrial Sponsor: OneSky, MicaPlex, Daytona Beach, FL
This project was supported by NREUP program
Description and resultsStudents: MAA NREUP - Winter 2020-2021 Mariah Marin ('21) DB BS Computational Mathematic Research Project in Industrial Mathematics Camryn Wills ('21) DB BS Aerospace Engineering/Daytona Applied Math Minor Mariah Marin ('21) DB BS Computational Mathematic Mafalda Soares ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Description:OneSky Flight supports the technology needs of four established private jet brands; Flexjet, Sentient Jet, PrivateFly, and Sirio. One key function required by these businesses is trip pricing. This is the exercise of determining the appropriate price for a trip, considering many factors. Today, this process is manual. The ultimate goal of this project is to create a dynamic pricing tool that generates an appropriate price for trips in the US and EU. There is a large part of this project that needs to be addressed: Event Calendar. The Event Calendar includes a factor for each day of the year. These factors are based on the events/holidays that happen throughout the year and their impact on the demand for days on and surrounding the events/holidays. Using previously identified patterns, the corresponding quantitatively model for model of dynamic pricing algorithm was developed. It is the hierarchical model with primary and secondary factors: considering normal weekly traffic, seasonal increments, and day of the week of a selected event. With given three years of flight history, model was build using any two years and validated using third on: the prediction of demand for third year was modeled and compared with third year actual data. Major outcomes:
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| Winter 2020 - Summer 2021 |
Advance analysis of Hispanic voters activity in Florida through sub-ethnicities identification
Industrial Sponsor: Florida Democratic Party, Daytona Beach, FL
This project was supported by ERAU REU DEIM program
Description and resultsStudents: ERAU REU: Research Projects in Data-Enabled Industrial Mathematics (Summer 2021) Hernandez Nimzay (‘22) - Universidad Ana G. Mendez - Recinto Gurabo – Applied mathematics Outlaw Abigail (‘23) - Converse College – Mathematics and Data Science Soto-Ortiz Kamila (’23) DB BS Astronomy & Astrophysics/ Daytona Comp Science Minor Rodriguez Naomi (‘22) DB BS Computational Mathematic Engineering Application Track MAA NREUP - Winter 2020-2021 Rivera Emily (‘22) DB BS Computational Mathematic Data Science Track Soto-Ortiz Kamila (’23) DB BS Astronomy & Astrophysics Rodriguez Naomi (‘22) DB BS Computational Mathematic Engineering Application Track Research Project in Industrial Mathematics Nickolas Bauman (‘23) DB BS Meteorology/2nd Major Computational Math Rivera Emily (‘22) DB BS Computational Mathematic Data Science Track Soto-Ortiz Kamila (’23) DB BS Astronomy & Astrophysics Rodriguez Naomi (‘22) DB BS Computational Mathematic Engineering Application Track Description: The Hispanic population is considered to be those that identify with a “Spanish-speaking background and trace their origin or descent from… Spanish-speaking countries”. Hispanics are currently about a quarter of Florida’s population and they are expected increase in population to 33% by 2045. Understanding how Hispanics vote then becomes a very crucial task for future elections, due to them being a key demographic. For starters, certain social factors such as “religion, region, and social class appear to be the characteristics that have most closely related to voting”. The factors considered in this project are the voter’s age, zip code population density, income and Hispanic group Comprehending these factors can potentially capture previously unseen voting trends in the Hispanic community. These are being compared with the voter’s activity and party affiliation. Party affiliation and voter activity are also compared for general party affiliation behavior. Publication in preparation Major outcomes:
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| Spring 2020 |
Using Matrix and Tensor Factorizations for Analyzing Radiation Transport Data
Industrial Sponsor: Pacific Northwest National Laboratory, Richland, WA
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Emma Galligan ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Grant Johnson ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math DeAndre Lesley ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Research Project in Industrial Mathematics Seth Garcia ('21) DB BS Electrical Engineering/2nd Major Computational Math Petar Grigorov ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Brittney Marzen ('20) DB BS Computational Mathematic/Daytona Space Studies Minor Description: The Disruptive Technologies Group in the National Security Directorate of Pacific Northwest National Laboratory is enthusiastic to collaborate with students at Embry-Riddle Aeronautical University on a research project whose goal is to develop quantitative methods for characterizing features in radiation transport simulation data and comparing features across different computational approaches. Understanding how radiation particles are transported throughout a system and interact with shielding is extremely computationally expensive. Reduced order models (ROMs) can be used to significantly increase the speed of these calculations. This project focuses on analysis of the simulated radiation transport for Cobalt-60, Cesium-137, and Technetium-99. A ROM may be developed from several formalisms and then analyzing the feature vectors of each. The methods considered here include developing algorithms afor principal component analysis (PCA), non-negative matrix factorization (NNMF), and CP tensor decomposition (CPT). The tests students conducted focused on investigated the similarities between vectors, the effects of noise on reconstructing the data, and the ability of each ROM to identify the different sources of radiation. The conclusion from this analysis plus the qualitative features of each method indicate the CPT is the best method for developing a ROM to describe the data. Major outcomes:
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| Spring 2020 | Enchance the functionality of Personal Weather Stations using Data Analytics
Industrial Sponsor: WeatherFlow Inc, Daytona Beach, FL
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Evelyn Taylor (‘20) DB BS Computer Science/2nd Major Computational Math Research Project in Industrial Mathematics Kade Mahoney ('20) DB BS Computational Mathematic/Daytona Physics Minor Hailey DeNys (‘21) DB BS Computational Mathematic Description: Description: WeatherFlow, a personal weather systems company, collaborated with undergraduate students at Embry-Riddle Aeronautical University to find a way to enhance visualization of real time meteorological data. Students used the meteorological data from WeatherFlow network to develop a new feature to enhance the user interface experience by creating a real time display of the weather in relation to a principal station. Temperature and rain were tracked in a grid structure with ten-mile intervals from ten to fifty miles along the eight cardinal directions. This visualization tool will give users a new understanding of their local conditions allowing them to better plan their daily activities.
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| Spring - Fall 2020 |
Dynamic Pricing Algorithm Project
Industrial Sponsor: OneSky, Daytona Beach, FL
Description and resultsSpring 2020 Students: Research Project in Industrial Mathematics - Capstone Allison Acosta ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Paige Rasmussen ('21) DB BS Computational Mathematic Research Project in Industrial Mathematics Payton Boliek ('20) DB BS Computational Mathematic/Daytona Comp Science Minor Mariah Marin ('21) DB BS Computational Mathematic Fall 2020 Students: Research Project in Industrial Mathematics - Capstone Payton Boliek ('20) DB BS Computational Mathematic/Daytona Comp Science Minor Betiay Babacan ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Description: OneSky Flight supports the technology needs of four established private jet brands; Flexjet, Sentient Jet, PrivateFly, and Sirio. One key function required by these businesses is trip pricing. This is the exercise of determining the appropriate price for a trip, considering many factors. Today, this process is manual. The ultimate goal of this project is to create a dynamic pricing tool that generates an appropriate price for trips in the US and EU. There is a large part of this project that needs to be addressed: Event Calendar. The Event Calendar includes a factor for each day of the year. These factors are based on the events/holidays that happen throughout the year and their impact on the demand for days on and surrounding the events/holidays. In order to build the list of possible events that affect the intensity of flights extended data analysis was performed. Along with processing 3 years of raw flight data (~8,000,000 single flights), students did independent research on theory behind dynamic pricing. Team determined the patterns in raw data and identify anomalies as possible event, mapping them and sorting them in holidays, sport events, extreme weather events, etc. based on open sources, also recognized the local and global patterns before identification. The corresponding quantitatively model for model of dynamic pricing algorithm was developed. It is the hierarchical model with primary and secondary factors: considering normal weekly traffic, seasonal increments, and day of the week of a selected event. With given three years of flight history, model was build using any two years and validated using third on: the prediction of demand for third year was modeled and compared with third year actual data. Major outcomes:
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| Spring 2020 |
Identifying Individual Voter Sub-Ethnicity
Industrial Sponsor: Florida Democratic Party, Daytona Beach, FL
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Ian Mungovan ('20) DB BS Aerospace Engineering/Daytona Astronomy Minor Liana Parker ('20) DB BS Computational Mathematic/ Business Admin Minor Research Project in Industrial Mathematics Richard Beredo ('21) DB BS Electrical Engineering/Daytona Military Science Minor Description: The Democratic performance in Florida among voters regressed in the 2016-2018 election cycle. Most of the regression was caused by the low turnout of Hispanic voters in the southern sector of Florida. The Florida Democratic Party teamed up with students at Embry-Riddle Aeronautical University on a research project that could help to analyze trends by examining Democratic Hispanic performance level based on sub ethnicity. First step towards it aims to develop an algorithm to analyze and predict possible Hispanic sub-ethnicity. Dealing with over 11 million records on all voters registered in Florida, students was able to combine public information on population data for all zip codes and data on 1000 most popular first names and 1000 most popular last names for all 16 Hispanic subethnicities to build algorithm to estimate the probability of each subethnicity for each Hispanic origin voter. With given prediction the further analysis on each subethnicity activity and party affiliation is in progress. Major outcomes:
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| Fall 2019 | Characterizing Seismic Events through Data Analytics
Industrial Sponsor: Nevada National Security Site, Las Vegas, NV
Description and resultsResearch Project in Industrial Mathematics - Capstone Students: Kasey Friedenreich ('19) DB BS Computational Mathematic/Daytona Elect Comp Eng Minor Joseph Uy ('21) DB BS Computational Mathematic/Global Conflict Studies/Daytona Military Kade Mahoney ('20) DB BS Computational Mathematic/Daytona Physics Minor Description: The Signal Processing and Applied Mathematics research group at the Nevada National Security Site, is seeking to collaborate with students at Embry-Riddle Aeronautical University to develop advanced data analytics methods for characterizing seismic events in the southwest United States as near-field earthquakes, far-field earthquakes, or non-natural seismic events, using publicly available seismic data. Students performed independent research on theory of seismic activities and data available. Learned how to read seismic data, determine location, distances, and original magnitude. Collected additional data of known events for comparison and testing purposes. With the given data for 5 stations and 30 days history with interval of one minute, there was a large amount of data to be processed, filtered, and visualized. Students have located and characterized manually all events, approximately 90 events in the given 30 days period. Major outcomes:
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| Fall 2018 - Spring 2019 |
Predictive Analytics in Child Welfare
Industrial Sponsor: Partnership for Strong Families, Gainesville, FL
This project was supported by ERAU IGNITE grant
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Daniel Oldham ('19) DB BS Computational Mathematic/Daytona Appl Meteor Minor Maegan Revak ('19) DB BS Computational Mathematic Orobosa Ero ('19) DB BS Computational Mathematic/Daytona Business Admin Minor Research Project in Industrial Mathematics Daniel Oldham ('19) DB BS Computational Mathematic/Daytona Appl Meteor Minor (Fall 2018) Betiay Babacan ('20) DB BS Astronomy & Astrophysics/2nd Major Computational Math Geoffrey Mount ('19) DB BS Computer Engineering/Daytona Applied Math Minor Nathan Foster ('21) DB BD Computer Science Description: The Partnership for Strong Families (PSF) is a child welfare organization headquartered in Gainesville, Florida helping to serve 13 counties in northern Florida. DCF estimates that, within PSF's area, approximately 45 children are removed every month from their parents' (or guardians') care. There is a clear need to identify, as early as possible, children who are at risk. The purpose of this project is to examine removals within PSF's area over the last 7 years and to potentially identify factors that lead to a child's re-entry into the state shelter system. Students successfully used statistical analysis, data mining, and machine learning techniques to determine multiple factors in child welfare service records that could lead to a child entering the foster care system multiple times. The factors included ages, removal types, disabilities, demographics, case details, relatives, and caregivers involved. Students also built multiple machine learning models and prediction schemes that facilitated further understanding of statistically significant insights about the cases. Major outcomes: Results were presented by: Daniel Oldham at ERAU STUDENT RESEARCH SYMPOSIUM (SRS), November 19, 2018, Daytona Beach, FL Daniel Oldham at Florida Undergraduate Research Conference, February 22-23, 2019, Jacksonville, FL Daniel Oldham at National Conference on Undergraduate Research, April 10-13, 2019, Atlanta, GA Nathan Foster and Daniel Oldham at 2019 ERAU Discovery Day Results were published: Oldham, D., Foster, N., & Berezovski, M. (2019). Data Mining and Machine Learning to Improve Northern Florida’s Foster Care System. Beyond: Undergraduate Research Journal, 3(1), 3. News article about the project: Data Science Offers New Tools for Understanding Foster Care Outcomes
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| Spring 2019 |
Satellite-Image Detection of Ships with Convolutional Neural Network
Description and resultsResearch Project in Industrial Mathematics - Capstone Clayton Birchenough ('19) DB BS Aerospace Engineering/2nd Major Computational Math Description: A convolutional neural network was built to provide automatic ship detection and localization from satellite images. The challenge of solving for the parameters of the neural network is a non-convex optimization problem with acceptable solutions and many deceptively acceptable solutions. To avoid deceptively acceptable solutions, different learning rate parameters, data augmentation methods, and neuron dropout rates were explored when training the network. Additionally, the effects of the number and order of convolutional, max pooling, and fully connected layers were varied to investigate the impacts on training and results. Major outcomes:
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| Spring 2019 | Characterizing Seismic Events through Data Analytics
Industrial Sponsor: Nevada National Security Site, Las Vegas, NV
Description and resultsResearch Project in Industrial Mathematics Students: Jessica Haselwood ('21) DB BS Aerospace Engineering/Daytona Applied Math Minor Roger Acchione ('20) DB BS Aerospace Engineering/2nd Major Computational Math Nikolaus Rentzke ('21) DB BS Meteorology/2nd Major Computational Math Nathaniel Arzola Lilly ('21) DB BS Mechanical Engineering/Daytona Applied Math Minor Dhairya Chokshi ('21) DB BS Astronomy & Astrophysics/Daytona Applied Math Minor Paige Rasmussen ('21) DB BS Computational Mathematic Description: The Signal Processing and Applied Mathematics research group at the Nevada National Security Site, is seeking to collaborate with students at Embry-Riddle Aeronautical University to develop advanced data analytics methods for characterizing seismic events in the southwest United States as near-field earthquakes, far-field earthquakes, or non-natural seismic events, using publicly available seismic data. Students performed independent research on theory of seismic activities and data available. Learned how to read seismic data, determine location, distances, and original magnitude. Collected additional data of known events for comparison and testing purposes. With the given data for 5 stations and 30 days history with interval of one minute, there was a large amount of data to be processed, filtered, and visualized. Students have located and characterized manually all events, approximately 90 events in the given 30 days period. Major outcomes:
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| Spring 2019 |
Unmanned Aerial Vehicle (UAV) DOF Dynamic Simulation Environment
Industrial Sponsor: Embedded Control Designs, MicaPlex, Daytona Beach, FL
Description and resultsResearch Project in Industrial Mathematics Students: Philip Giuliano ('21) DB BS Aerospace Engineering Christopher Gutierrez ('20) DB BS Aerospace Engineering/Applied Math Minor/ CAD/CAM Minor Eric Osorio ('19) DB BS Mechanical Engineering/Applied Math Minor Eugenie Fontaine ('21) DB BS Aerospace Engineering Applied Math Minor/ Astronomy Minor Nicolas Prulhiere ('20) DB BS Aerospace Engineering Description: The goal of this project is the development of a 6 DOF dynamic simulation environment. This simulation environment will play an integral role in the development of flight software and mission planning for standard operations as well as R&D. Students was able to create a completely software-based UAV simulation by utilizing a digital, on-board computer called ArduCopter and a 3D game engine, Unity, to test the success rate of a mission for various UAV models. The goal is to display the “thought processes” of ArduCopter and the actual behavior of the simulated UAV model. This data is analyzed to see the percent error between both data sets to determine a mission’s success rate. The data collected from the 3D simulation then used by ArduCopter to make necessary corrections and increase the chance of a successful mission. Major outcomes:
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| Spring 2019 | Flying Routes Optimization
Industrial Sponsor: OneSky, MicaPlex, Daytona Beach, FL
Description and resultsStudents: Research Project in Industrial Mathematics Ada Chika ('19) DB BS Aerospace Engineering/2nd Major Computational Math/Flight Minor Nicholas Parga ('21) DB BS Aerospace Engineering/2nd Major Computational Math Timothy Mitchell ('19) DB BS Aerospace Engineering/2nd Major Computational Math Ian Mungovan ('20) DB BS Aerospace Engineering/ Astronomy Minor Ian Young ('21) DB BS Aerospace Engineering/ Space Studies Minor Description: The ultimate goal for the OneSky Optimization project was to create a new fleet optimizer that works across all of the OneSky Flight five established private jet brands: Flexjet, Sentient Jet, SkyJet, PrivateFly, and Sirio. There are industry-specific rules and business-specific requirements that ERAU team working on this project will discuss with members of the OneSky Innovation Center in the MicaPlex. Students successfully developed and built the new optimization algorithm using, by their choice, a modified version of Dijkstra’s algorithm to identify the most profitable routes for any requested trip. Algorithm includes connection to OneSky SQL server retrieving all the possible flight connections for each possible aircraft, identification of all viable flights for each leg considering constraints, assignment dollar value to each alternative and operational time constraints. The output is the most profitable route option: optimal trade-off between money and time without compromising quality. Students signed IP release to have access to SQL data base. Major outcomes:
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| Spring 2018 |
Quantifying Uncertainties in Image Segmentation
Industrial Sponsor: Nevada National Security Site, Las Vegas, NV
Description and resultsResearch Project in Industrial Mathematics Students: Tori Hoff ('18) DB BS Computational Mathematic/Daytona Business Admin Minor Jean-Lucien Gionet ('20) DB BS Aerospace Engineering/ Applied Math Minor/ Comp Math Minor Description: The Signal Processing and Applied Mathematics research group at the Nevada National Security Site (NNSS) partnered with students at ERAU to characterize the effects of user interaction on supervised image segmentation. Students were provided with a new statistical tool for image segmentation, called Locally Adaptive Discriminant Analysis (LADA) which has three primary user inputs, the training data, the window size (WS), and nearest neighbor (NN) values. The primary research objective was to determine the sensitivity of the tool in regards to the user inputs with regard to all other inputs and provide a set of parameters as a guide to optimize the results of segmentation with regards to the boundary definition between different classes within an acceptable margin of error. Students come up with recommendation on parameters and analysis of inputs mutual effect. Another important result was the identification of the failure in the provided application: for small WS and NN values the program was unable to handle the calculations required to output the appropriate matrices. Major outcomes:
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| Spring 2018 |
Data Analysis for Partnership For Strong Families
Industrial Sponsor: Partnership for Strong Families, Gainesville, FL
Description and resultsStudents: Research Project in Industrial Mathematics - Capstone Tori Hoff ('18) DB BS Computational Mathematic/Daytona Business Admin Minor Research Project in Industrial Mathematics Cynthia Butcher ('20) DB BS Astronomy & Astrophysics/Applied Math Minor Maegan Revak ('19) DB BS Computational Mathematic Description: The Partnership for Strong Families (PSF) is a child welfare organization headquartered in Gainesville, Florida helping to serve 13 counties in northern Florida. DCF estimates that, within PSF's area, approximately 45 children are removed every month from their parents' (or guardians') care. There is a clear need to identify, as early as possible, children who are at risk. The purpose of this project is to study the data provided by Partnership for Strong Families (PSF) and identify any factors that could lead to a child being removed from his/her home multiple times. Major outcomes:
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| Spring 2017 | Mie Scattering Diagnostic
Industrial Sponsor: Nevada National Security Site, Las Vegas, NV
This project was supported by PIC Math program
Description and resultsPICMath course - Research Project in Industrial Mathematics Students: Tilden Roberson ('19) DB BS Aerospace Engineering/ Applied Math Minor ('21) DB MS Aerospace Engineering Clayton Birchenough ('19) DB BS Aerospace Engineering/2nd Major Computational Math Arjunsinh Nakum ('20) DB BS Astronomy & Astrophysics/ Comp Science Minor Joao Rocha Belmonte ('19) DB BS Mechanical Engineering/ Applied Math Minor Sophie Jorgensen ('20) DB BS Aerospace Engineering/ Applied Math Minor/ Comp Math Minor Description: The Signal Processing and Applied Mathematics Research Group at the Nevada National Security Site teamed up with Embry-Riddle Aeronautical University (ERAU) to collaborate on a research project under the framework of PIC math program with challenge to make a recommendation about whether to use a technique, used in the air quality industry, called Mie scattering, and repurpose this method to measure particle sizes that are emitted from a metal surface when it's shocked by explosives. Using simulated data derived from Mie scattering theory and existing codes provided by NNSS students validated the simulated measurement system. The construction data procedure was implemented with an additional choice of discretization technique: randomly distributed particle radii and incrementally discretized particle radii. The critical regions of sensors position were determined. Major outcomes:
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