Projects

Pirate Detection

Pirate Detection

Pirate DetectionSponsor: Johns Hopkins University Applied Physics LabCadet
Researchers: Stephen Mascioli, Cameron Armstrong, Ryan HarnerFaculty
Advisor: John David

This project was initiated to better understand the capabilities of naval sea and air assets off the Horn of Africa to be used as a tool against piracy in that region. A model was developed to simulate possible plane routes and pirate detection probabilities based off of realistic operating conditions in order to determine the maximum possible area coverage in a particular region over 7 days. Major factors in the model were the density of merchant ships in the region and the availability of patrolling assets.

Meal Delivery

Sponsor: Valley Program for Aging Services (VPAS)
Cadet Researcher: Alex Falcetti
High School Interns: Stevan Hall-Mejia
Advisors: Nate Axvig and John David

One of the services VPAS provides is a program in which hot meals are delivered to homebound senior citizens for their noon meal. Currently, over 400 meals are served each day.

Our task was to find a more efficient way to route delivery drivers given the daily fluctuations in both volunteer aid as well as meal demand. To accomplish this, we designed an easy-to-use piece of software that employs a combination of genetic algorithms, allowing VPAS to deliver their meals in a more efficient manner.

Energy Usage in the City of Lexington

Sponsor: City of Lexington
Cadet Researcher: Robert “Chap” Michie
High School Interns: Kunal Ghandi and Stevan Hall-Mejia
Faculty Mentor: Geoffrey Cox

The City of Lexington is located in Rockbridge County, Virginia. The city owns and operates 27 buildings with 42 separate energy accounts: 13 natural gas and 29 electric. Prior to our project, all records of these accounts were handwritten documents kept within city hall, penciled in by a city employee.

The goals of the project were to first take this information and put it in a digital state and then provide a tool to analyze the data in question. Our tool allows the city to quickly access visual models of each building’s energy consumption so that improvements can be made and inefficiencies addressed. By looking at the visual models, the city can increase its overall sustainability, providing economic, environmental, and social benefits to the community. 

Data Mining in Major League Baseball

Sponsor: VMI Summer Undergraduate Research Initiative
Cadet Researcher: Will Lucas
Faculty Mentor: John David

Data Mining has become an increasingly popular technique in science, economics, business and sports, especially a sport with as rich data as baseball. In this project we mined millions of baseball statistics to predict which teams will win on a day to day basis as well as over entire seasons.

We focused on statistical techniques of regression, artificial neural networks and decision trees.

Real Interest Rate Prediction using Supervised and Unsupervised Learning Techniques

Real Interest Rate Predictionusing Supervised and Unsupervised Learning Techniques
Sponsor: National Science Foundation
Researcher: Robert Michie
Advisor: Atin Basuchoudhary and John David

This project examines factors of political risk and theirability to influence and predict real interest rates. Various data miningtechniques were used on the ICRG researcher dataset for political risk, whichis made up of 12 weighted components.

 The dataset contains data from the years1984 to 2008 for 37 countries. Unsupervised learning techniques were used inorder to find hidden structures within the data itself, while supervisedlearning techniques were used to make future predictions of the real interestrate.  

 The key points of this work arethat money growth is not a good predictor of real interest rates, as well asthe fact that a committee of artificial neural networks proved to best thestrongest model in terms of predictive performance, and finally that there arecertain political risk factors that are indicative of changes in real interestrates.

NBA Data Mining and Predictive Modeling

Sponsor: VMI
Cadet Researcher: James T. Snyder
Advisor: John A. David
Virginia Military Institute
June 20, 2013

 In this project we explore the importance of data in the process of making predictions of outcomes of National Basketball Association (NBA) games.

 We use NBA statistics for the 2004-2005 season up to and including the 2010-2011 season. We utilize unsupervised learning techniques to gain an informed understanding of our data and how it operates. These techniques include Clustering, Principal Component Analysis, and Factor Analysis. We learn predictive capabilities and the different ways in which the statistics impact the outcome of an NBA game. We also use supervised learning techniques to build models in order to make predictions on the score differential between two teams in a given game.

 Committees of Artificial Neural Networks (ANN’s) and Forests of Decision Trees are built based on data leading up to seasons we wish to predict. In this study the most accurate models are built using data from all seasons, 2004-2009, and then evaluated using 2010 data to make predictions. Our most accurate model predicted 2010-2011 NBA game winners with an average accuracy of 72.45% and a mean error of 8.32 points.

Reduction of Shipping Errors Using Data Mining and Data Analysis
Cadet Researchers: William Lucas and Steven Geyer
Advisor: Nate Axvig

Different data mining techniques were used to assess the data that we received from the client. The two main techniques used to analyze the data were artificial neural networks and multiple regression analysis.

The goal of the project was to be able to predict errors on a given order. The two techniques yielded comparable results but due to the complexity of the neural networks we decided to use the multiple regression model to present to our client because it is much simpler to for them to use.
Data Management for the United Way of Greater Augusta
Sponsor: United Way of Greater Augusta
Cadet Researcher: John Zippel
Advisor: Meagan Herald

The United Way of Greater Augusta strove to build a database containing facts and figures specific to greater Augusta County, as well as the Virginia comparisons, with emphasis on making it easily updatable for United Way employees and quickly navigable for any member of the community interested in browsing local data.

Another goal of this project is to pinpoint not only the main problems in the community, but connecting the factors that are causing them. In that way, support will be used to resolve the cause of the problem, not a symptom of it.

The database, which included data analysis such as multiple linear regressions and statistical hypothesis tests, could identify what the symptoms are and begin to link some of the factors in order to discover the causes.
Process Reengineering and Optimization for Vector Industries
Sponsor: Vector Industries
Cadet Researchers: Alexander Lin
Advisor: Geoffrey Cox

Vector Industries is a non-profit employment oriented organization that works to provide individuals with disabilities financial and social independence. Their services include providing complex product assembly, packaging, product testing, and much more.
Recently, Vector has decided to make efforts in transforming specific business operations and processes to ultimately improve their organization's performance. These processes involve the analysis of large amounts of financial data, employee wages, and transportation routes for picking up employees.

To assist in their efforts, a MATLAB transportation routing program was utilized for reducing transportation expenses. Additionally, Microsoft Excel automation tools were developed that conserves time, decreases human error, and generates organized data sets.
Modeling Food Prices and Minimizing Food Cost for Young Life
Sponsor: VMI and Young Life
Advisor: John David

This project was initiated to better the food buying practices of the Young Life Organization as well as to develop tools for them to utilize in this process. One tool analyzed past food invoices to determine when certain products will historically be the cheapest.

In addition tools were developed to help Young Life build a larger database allowing for their model to become better overtime and to normalize the price lists.
Valuation of Selected Asset Classes Using Artificial Neural Networks

Cadet: Jack Zippel
Faculty Advisor: Maj. John David
Sponsor: Davidson & Garrard, Inc., David Hansen

The goal of this project was to value a set of exchange traded funds (ETFs) that Davidson & Garrard may use as indicators of market potential. Predicting the prices of the ETFs allows for an effective valuation technique: if the price will increase, the ETF is currently undervalued and vice versa.

Predictions spanning two years provide optimal results given that short term market oscillations distort greater trends and larger economic events drastically change long term market direction.

Data Analysis for American Shakespeare Center

Cadet: Ryan Poffenbarger
Faculty Advisor: Maj. Geoff Cox
Sponsor: American Shakespeare Center

This projects analyzed 13 years of patron and donor data with the primary interest being to understand seating preferences, customer retention and demographics, and donation patterns.

Through summarizing hundreds of thousands of ticket sales in simple statistics and graphical representation of these we were to give the Center clear quantitative evidence for how to better market and grow donations and ticket sales.

Tool for Quantitative Understanding of Manufacturing Capability

Cadets: Austin States and Josh Grant
Faculty Advisor: Maj. Randy Cone

This project worked for a local manufacturing firm to help them develop a quantitative tool to better understand their production process and capability.

This tool gives guidance on how to flex production to produce orders in an efficient and timely manner while considering variation in product demand, multiple machines in the production process with varying capacity and reliability and variation in product production time.