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  • Additive Solution-7 Reduces the Red Blood Cell Cold Storage Lesion

    Article thumbnail: Additive Solution-7 Reduces the Red Blood Cell Cold Storage Lesion
    Type:
    Article
    Description/Abstract:
    Not Available
    Creator/Author:
    Maes, L. A.; Rugg. N; Dumont, L. J.; Cancelas, Jose A.; Zia, M.; Hess, J. R.; Whitley, P. H.; Siegel, A. H.; Szczepiokowski, Z. M., and Herschel, L.
    Submitter:
    Jose Cancelas
    Date Uploaded:
    02/08/2017
    Date Modified:
    04/10/2017
    Date Created:
    2014-10
    License:
    All rights reserved

  • SQL Server scripts to create the database, add the schema, and populate the tables

    Dataset thumbnail: SQL Server scripts to create the database, add the schema, and populate the tables
    Type:
    Dataset
    Description/Abstract:
    SQL Server scripts to create the database, add the schema, and populate the tables.
    Creator/Author:
    Nicholson, Bill
    Submitter:
    Bill Nicholson
    Date Uploaded:
    02/06/2017
    Date Modified:
    02/06/2017
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • The Visual Studio Project that simulates transactions and adds records to the database

    Document thumbnail: The Visual Studio Project that simulates transactions and adds records to the database
    Type:
    Document
    Description/Abstract:
    A VS Project written in C#. Includes data manipulation, OOP, Database connections, and simulation logic.
    Creator/Author:
    Nicholson, Bill
    Submitter:
    Bill Nicholson
    Date Uploaded:
    02/06/2017
    Date Modified:
    02/06/2017
    License:
    Attribution-NonCommercial-ShareAlike 4.0 International

  • Grocery Store Simulator for IT and Computer Science Education

    Generic Work thumbnail: Grocery Store Simulator for IT and Computer Science Education
    Type:
    Generic Work
    Description/Abstract:
    This project includes a SQL Server database and a Visual Studio project that simulate the transactions at a chain of grocery stores. It has stores, employees, products, ingredients in the products, manufacturers, and brands. When the simulation runs it generates customer transactions, employee work history, store history (open/closed/on fire/etc.), coupons, coupon usage, and product price history. As this data accumulates you can use it to teach SQL programming and query design. The source code for the simulation project is included and can be used as an object lesson for software development courses, particularly C# and OOP.
    Creator/Author:
    Nicholson, Bill
    Submitter:
    Bill Nicholson
    Date Uploaded:
    10/27/2017
    Date Modified:
    10/27/2017
    License:
    All rights reserved

  • Plasma Protein Timings: Relative Contributions of Storage Time, Donor Age and Donation Season

    Article thumbnail: Plasma Protein Timings: Relative Contributions of Storage Time, Donor Age and Donation Season
    Type:
    Article
    Description/Abstract:
    Not Available
    Creator/Author:
    Rugg, N. and Cancelas, Jose A.
    Submitter:
    Jose Cancelas
    Date Uploaded:
    02/08/2017
    Date Modified:
    04/10/2017
    Date Created:
    2016-10-04
    License:
    All rights reserved

  • Comparison of Approximate Approaches to Solving the Travelling Salesman Problem and its Application to UAV Swarming

    Article thumbnail: Comparison of Approximate Approaches to Solving the Travelling Salesman Problem and its Application to UAV Swarming
    Type:
    Article
    Description/Abstract:
    Comparison of approximate approaches to solving the Travelling Salesman Problem and its application to UAV swarming. International Journal of Unmanned Systems Engineering. 3(1): 1-16. The Travelling Salesman Problem (TSP) is a widely researched Non-deterministic Polynomial-time hard optimization problem with a range of important applications in a wide spectrum of disciplines including aerospace engineering. In this paper, a comparison of different approaches to solve the TSP and also its application towards swarming of UAVs is considered. The objective of the TSP is to determine the optimal route associated with the shortest tour connecting all targets just once. Genetic Algorithms (GA) are one of the most widely applied techniques for solving this class of optimization problems. Two other techniques, 2-opt and Particle Swarm Optimization, are used and the results are compared with those obtained using GA. The comparison is made for different numbers of targets, using salient figures of merit such as computational time required and the cost function which is the minimum solution (distance) obtained. Results show that the 2-opt approach with the closest neighbour as initial starting point for the search yields superior performance. In the Multiple Travelling Salesman Problem, we propose a cluster-first approach which allocates each specific UAV to a subset of targets. The 200 targets are divided into four clusters corresponding to the four UAVs and then TSP algorithms like 2-opt and GA are employed to solve each cluster. This approach drastically reduces the computational time and also gives much better results than the conventional technique of directly applying GA over the 200 targets.
    Creator/Author:
    Cohen, Kelly; Boone, Nathan, and Sathyan, Anoop
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2015-01
    License:
    All rights reserved

  • Fuzzy Logic Based Intelligent Agents for Unmanned Combat Aerial Vehicle Control

    Article thumbnail: Fuzzy Logic Based Intelligent Agents for Unmanned Combat Aerial Vehicle Control
    Type:
    Article
    Description/Abstract:
    Not Available
    Creator/Author:
    Cohen, Kelly and Ernest, Nick
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2015-12
    License:
    All rights reserved

  • Genetic Fuzzy Trees and their Application Towards Autonomous Training and Control of a Squadron of Unmanned Combat Aerial Vehicles

    Article thumbnail: Genetic Fuzzy Trees and their Application Towards Autonomous Training and Control of a Squadron of Unmanned Combat Aerial Vehicles
    Type:
    Article
    Description/Abstract:
    This study introduces the technique of Genetic Fuzzy Trees (GFTs) through novel application to an air combat control problem of an autonomous squadron of Unmanned Combat Aerial Vehicles (UCAVs) equipped with next-generation defensive systems. GFTs are a natural evolution to Genetic Fuzzy Systems, in which multiple cascading fuzzy systems are optimized by genetic methods. In this problem a team of UCAV's must traverse through a battle space and counter enemy threats, utilize imperfect systems, cope with uncertainty, and successfully destroy critical targets. Enemy threats take the form of Air Interceptors (AIs), Surface to Air Missile (SAM) sites, and Electronic WARfare (EWAR) stations. Simultaneous training and tuning a multitude of Fuzzy Inference Systems (FISs), with varying degrees of connectivity, is performed through the use of an optimized Genetic Algorithm (GA). The GFT presented in this study, the Learning Enhanced Tactical Handling Algorithm (LETHA), is able to create controllers with the presence of deep learning, resilience to uncertainties, and adaptability to changing scenarios. These resulting deterministic fuzzy controllers are easily understandable by operators, are of very high performance and efficiency, and are consistently capable of completing new and different missions not trained for.
    Creator/Author:
    Schumacher, Corey; Cohen, Kelly; Ernest, Nicholas; Casbeer, David, and Kivelevitch, Elad
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2015-05
    License:
    All rights reserved

  • Spatio-temporal estimation of wildfire growth

    Article thumbnail: Spatio-temporal estimation of wildfire growth
    Type:
    Article
    Description/Abstract:
    This work presents a methodology for real-time estimation of wildland fire growth, utilizing a fire growth model based on a set of partial differential equations for prediction, and harnessing concepts of space-time Kalman filtering and Proper Orthogonal Decomposition techniques towards low dimensional estimation of potentially large spatio-temporal states. The estimation framework is discussed in its criticality towards potential applications such as forest fire surveillance with unmanned systems equipped with onboard sensor suites. The effectiveness of the estimation process is evaluated numerically over fire growth data simulated using a well-established fire growth model described by coupled partial differential equations. The methodology is shown to be fairly accurate in estimating spatio-temporal process states through noise-ridden measurements for real-time deployability.
    Creator/Author:
    Sharma, Balaji R.; Cohen, Kelly, and Kumar, Manish
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2013-10
    License:
    All rights reserved

  • Optimal Fireline Generation for Wildfire Fighting in Uncertain and Heterogeneous Environment

    Article thumbnail: Optimal Fireline Generation for Wildfire Fighting in Uncertain and Heterogeneous Environment
    Type:
    Article
    Description/Abstract:
    Fire is a natural component of many ecosystems but wildland fires often do pose serious threats to public safety, properties and natural resources. Forest fire acts as a dominant factor in reshaping of terrain and change of the ecosystem of a particular area. The total damage due to wildland fire shows an increasing trend over the past decade. Forest Fire Decision Support Systems (FFDSS) have been developed for the last thirty years all over the world that supplies valuable information on forest fire detection, fire behavior and other aspects of forest fires but lacks in developing intelligent fire suppression strategies. In this paper, an effort has been made to generate intelligent fire suppression strategies with efficient resource allocation using the Genetic Algorithm based optimization tool in a heterogeneous and uncertain scenario. The goal of this research is to perform intelligent resource allocation along with the generation of optimal firelines that minimizes the total burned area due to wildland fire. The solutions generated at each generations of the Genetic Algorithm (GA) are used to build the firelines in a heterogeneous terrain where advanced forest fire propagation model is used to evaluate the fitness values of each generated solutions. The optimal firelines thus obtained through the Simulation-Optimization technique minimizes the total damage due to wildland fire and eliminates the chance of any fire escape i.e., firefront reaching the fireline positions before they are built. Such techniques integrated with the existing FFDSS hold promise in effectively controlling forest fires.
    Creator/Author:
    Manish, Kumar; Cohen, Kelly, and HomChaudhuri, Baisravan
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2010-07
    License:
    All rights reserved