Taking electrification on the road: Exploring the impact of the Electric Farm Equipment roadshow

Project Summary

A team of students led by researchers in the Energy Initiative and the Energy Access Project will explore historical data on the U.S. Electric Farm Equipment (EFE) demonstration show that ran between 1939 and 1941, which aimed to increase usage of electricity in rural areas. Students will compile data collected by the Rural Electrification Agency into a machine-readable form, and then use that data to explore and visualize the EFE’s impact. If time allows, they will then compare data from the EFE and a related, smaller-scale project from 1923 (“Red Wing Project”) to current data on appliance promotion programs in villages in East Africa that have recently gained access to electricity. The outcomes of this analysis would offer evidence on the successes and limitations of these types of programs, and the relevance of the historical U.S. case to countries that are currently facing similar challenges.

Project Leads: Victoria Plutshack, Jonathon Free, Robert Fetter

Year
2020
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Related Projects

A team of students that worked together for a semester in the Mission Driven Startups class will obtain and analyze data to create a predictive maintenance model for F15-E Fighter Jets from Seymour Johnson Air Base. Using data provided by the Base, the Data+ team will evaluate the relationship between unscheduled maintenance and external factors such as weather, sortie hours between repairs, and failure frequency of aircraft components. These findings will then feed into a predictive maintenance model to enhance the Air Force Crew’s ability to anticipate maintenance needs, helping to minimize unscheduled aircraft downtime. 

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

A team of students, led by Electrical and Computer Engineering professor Vahid Tarokh, will develop methods to improve the efficiency of information processing with adaptive decisions according to the structure of new incoming data. Students will have the opportunity to explore data-driven adaptive strategies based on neural networks and statistical learning models, investigate trade-offs between error threshold and computational complexity for various fundamental operations, and implement software prototypes. The outcome of this project can potentially speed up many systems and networks involving data sensing, acquisition, and computation.

Project Leads: Yi Feng, Vahid Tarokh

A team of students will explore new ways of reading pre-modern maps and perspectival views through image tagging, annotation and 3D modeling. Each student will build a typology of icons found in these early maps (for example, houses, churches, roads, rivers, etc.). By extracting, modeling, and cataloging these features, the team will create a library of 2D and 3D objects that will be used to (a) identify patterns in how space and power are represented across these maps, and (b) to create a model for “experiencing” these maps in 3D, using the Unity game engine platform. This is a combined Data+ / Bass Connections project that will instruct students in qualitative and quantitative mapping techniques, basic 3D modeling and the history of cartography.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood