151-160 of 201 results
-
Machine Learning for Dynamic Airspace Configuration towards Optimized Mobility in Emergency Situations
PI Houbing Song
Categories: Faculty-Staff
-
NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning
PI Houbing Song
CO-I Richard Stansbury
Embry‑Riddle Aeronautical University establishes a new Research Experiences for Undergraduates (REU) Site to engage participants in research in drone swarms. The emerging concept of drone swarms, which is defined as the ability of drones to autonomously make decisions based on shared information, creates new opportunities with major societal implications. However, future drone swarm applications and services pose new networking challenges. A resurgence of Artificial Intelligence and machine learning research presents a tremendous opportunity for addressing these networking challenges. There is an overwhelming need to foster a robust workforce with competencies to enable future drone swarm applications and services in the age of AI/machine learning.
The project establishes a new Research Experiences for Undergraduates (REU) Site with a focus on networking research for drone swarms in the age of AI/machine learning at Embry‑Riddle Aeronautical University. The goals of the REU Site are: (1) attract undergraduate students to state-of-the-art drone swarm research, especially those from underrepresented groups, and from institutions with limited opportunities; (2) develop the research capacity of participants by guiding them to perform research on drone swarms; (3) grow the participants’ technical skills to enable a wide variety of beneficial applications of drone swarms; (4) promote the participants’ integrated AI/machine learning and drone swarm competencies; and (5) prepare participants with professional skills for careers. The focus of the REU Site is on the design, analysis and evaluation of innovative computing and networking technologies for future drone swarm applications and services. To be specific, research activities will be conducted in three focus areas, notably dynamic network management, network protocol design, and operationalizing AI/machine learning for drone swarms. Each year eight undergraduate students will participate in a ten-week summer REU program to perform networking research for drone swarms under the guidance of research mentors with rich experiences in AI/machine learning and drone swarms. This REU site is expected to foster workforce knowledge and skills about developing new computing and networking technologies for future drone swarm applications and services. This site is supported by the Department of Defense ASSURE program in partnership with the NSF REU program.
Categories: Faculty-Staff
-
Unmanned Aircraft Systems (UAS) Application to Support Aircraft Rescue and Fire Fighting (ARFF)
PI Brent Terwilliger
CO-I David Ison
CO-I Dennis Vincenzi
CO-I Dahai Liu
This continuing research project features refinement of UAS application methods to support of ARFF responses. Previously, modeling and simulation, in combination with UAS attribute performance models, was implemented to better understand challenges, limitations, and potential benefits of UAS support. However, based on the findings and recommendations of the original inquiry, the research will be expanded to include examination of operator knowledge, skills, and abilities (KSAs), performance rating standards, and appropriate training requirements and delivery approaches.
Our team of researchers from Embry‑Riddle Aeronautical University-Worldwide has been actively compiling published performance data associated with commercially-off-the-shelf (COTS) group 1 to 3 fixed-wing and vertical takeoff and landing (VTOL) unmanned aircraft systems (UAS) in an effort to develop statistical models of each category. The captured data, which includes maximum speed, cruise speed, endurance, weights, wind limitations, and costs, is used to calculate capabilities including range (one-way and return), time to objective, station keeping duration, and maneuver requirements. The benefit from assembling such a unified collection of information and the calculation of associated derived capabilities is that these models are anticipated to accurately reflect the capabilities, limitations, and considerations necessary in the assessment of such platforms for various applications and operating environments. These models will be available for combination with simulation or analysis frameworks to better assess end usability of these categories of aircraft for a significant number of applications including, emergency response, disaster relief, precision agriculture, security, tactical, communications, environmental study, infrastructure inspection, cargo delivery, and mapping/surveying.
Publications:
Terwilliger, B., Vincenzi, D., Ison, D., & Smith, T. (2015). Assessment of unmanned aircraft platform performance using modeling and simulation (paper no. 15006). In Volume 2015: Proceedings of the 2015 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Arlington, VA: National Training and Simulation Association.
Terwilliger, B., Vincenzi, D., Ison, D., Herron, R., & Smith, T. (2015). UAS capabilities and performance modeling for application analysis. In Proceedings of the Association for Unmanned Vehicle Systems International 42nd Annual Symposium. Arlington, VA: Association of Unmanned Vehicle Systems International.
Ison, D., Terwilliger, B., Vincenzi, D., & Kleinke, S. (2015). Airport bird activity - monitoring and mitigation: The unmanned aerial system (UAS) approach.Presented at the 2015 North American Bird Strike Conference, Montreal, QC.
Categories: Faculty-Staff
-
A Curriculum Wide Software Development Case Study
PI Massood Towhidnejad
CO-I Thomas Hilburn
This NSF funded research develops case studies of software development for use in software engineering and computing instruction.
Products include realistic projects, complete artifacts throughout the software development life cycle, case studies decoupled from a particular textbook, and case modules designed with varying complexity allowing for use in multiple classes throughout undergraduate and graduate curricula.Categories: Faculty-Staff
-
Encouraging Students to Pursue an Engineering Education and Career
PI Massood Towhidnejad
This NSF-sponsored project provides scholarship for engineering students pursuing degrees in computer science, computer engineering, electrical engineering, mechanical engineering and software engineering.
Working closely with faculty and student mentors, scholarship recipients are involved in multi-disciplinary projects involving unmanned and autonomous systems throughout their four years of undergraduate study.Categories: Faculty-Staff
-
From Middle School to Industry Vertical Integration to Inspire Interest in Computational Thinking
PI Massood Towhidnejad
CO-I Thomas Hilburn
While students typically do not see immediate advantages of the topics being studies, top down integration exposes students to larger, more complex projects, giving them better appreciation for topics as they realize the “big picture.”
Funded by the National Science Foundation, this research seeks to vertically integrate software development best practices from industry to graduate, undergraduate, high school, and middle school academic programs, with the intention of increasing student interest in computing and computational thinking.Categories: Faculty-Staff
-
Big Data Analytics for Injury Data
PI Dothang Truong
This project leverages big data analytics tools for the exploration and transformation of injury data for a major Part 121 carrier with the goal of predictive modeling. This project offers graduate students an opportunity to work with a substantial airline dataset under the supervision of a faculty member. The outcomes have the potential to lead to more extensive future projects in the realm of big data analytics. (This project is under strict NDA).
Categories: Faculty-Staff
151-160 of 201 results