PSG actively participates in Small Business Innovation Research (SBIR) projects dealing with complex system-of-systems engineering and data science. Our focus is on modeling and simulation of complex system performance, artificial intelligence and machine learning applied to sensor data, and innovative analytics for creating or exploiting sensor products. PSG also engages with academia at universities through collaboration with the SenSIP research consortium, as well as research projects with engineering students at Arizona State University.
This 2019 Scientific Technology Transfer (STTR) topic investigated the development of a new high-fidelity modeling and simulation (M&S) framework that addresses the need for voluminous and high-quality Multi-INT training data for deep learning networks. The rationale for this work is that operationally collecting large quantities of training data is too expensive and infeasible based on costly field experiments. The focus of this effort is on multiphysics-based modeling of radio frequency (RF) signals in realistic physical and contested environments.
PSG designed and developed an innovative SAR simulation workflow called SARsim
Research and development project tasked with investigating techniques for the collection, archival, management, and analysis of data – specifically, large data volumes collected from modeling and simulation applications on the electric grid.
Department of Energy project investigated the use large volumes of data on the electric grid.
Exciting developments in the area of additive manufacturing (also known as 3D printing) enables complex and precise design of production parts. This project explores the computational aspects throughout the lifecycle of additive manufacturing, from modeling technologies that generate manufacturing requirements, to the production of advanced materials.
(Spring 2019-Fall 2020)
Exciting developments in additive manufacturing, also known as 3D printing.
(Fall 2018 - Spring 2019)
Using machine learning to predict residential energy usage.
We can perform studies on electric grids through technical modeling and simulation, but what are the financial implications of these results? The objective of this project is to implement a technique for studying the projected cash flow of simulations and analysis by using a tool from National Renewable Energy Laboratory (NREL) called System Advisor Model (SAM).
(Fall 2018-Spring 2019)
Looking at financial implications of technical modeling & simulation studies on the electric grid.
Two senior capstone teams at ASU CIDSE, from both Computer Science and Industrial Engineering curriculum, were drawn together in this project. The students were tasked with investigating the requirements of an Air Force Pilot Training program through a modeling and simulation activity, and then visualizing the simulation through an augmented reality platform. The students crafted a logical scenario in the AnyLogic simulation software, output the results into a neo4j graph database, and created a visualization of the scenario through the Microsoft HoloLens.
(Spring 2017-Fall 2018)
Looking at an Air Force pilot training program using modeling, visualization and augmented reality.
This project developed an agile lifecycle approach for collecting, manipulating, and evaluating data results of a Convolutional Neural Network (CNN) applied to image segmentation and classification. Students successfully completed objectives including:
(Fall 2017-Spring 2018)
Agile lifecycle aproach to collect, manipulate and evaluate data results of CNN network.