The goal is to research and develop a Quality of Information (QOI) Maximizing Information Acquisition Framework (QMAF) for event prediction that goes beyond the limitations of traditional approaches of heuristic/closed form optimization formulations (and its inherent computational complexities) for selection of sensor/data selection. The concept for deriving the QOI optimizing sensor orchestration strategy is looked at as two sub problems a) for a set of random sensor selections, build separate event prediction models and compute the performance accuracies, and b) have a second predictive model learn the relationship between sensor selections and prediction accuracy. The first step calibrates multiple event prediction models for a diverse set of sensor selections, and also generates accuracy data that is used by the second model. The second model can answer the question of the expected accuracy for a given sensor selection, and can point to sensor selection strategies that maximizes the accuracy at minimal acquisition costs. The effort leverages deep learning architectures for complex feature preprocessing, intelligent sampling strategies, as well as active learning algorithms that help supply informative samples for the accuracy prediction problem.
SBIR Phase I
QOI Maximizing Information Acquisition Framework
Agency: Department of Defense
Branch: Air Force
Department of Transportation Selects KBSI to Develop Mobile Application to Help Inventory and Assess Sidewalks
March 7, 2016 – KBSI has been selected by Volpe, The National Transportation Systems Center of the U.S. Department of Transportation to develop a mobile application for the collection and assessment of the U.S. sidewalk inventory.
Under the terms of the Phase II Small Business Innovation Research (SBIR) contract, KBSI will utilize recent advances in social networks, crowd sourcing, mobile data collection methods, and data mining techniques to provide integrated sidewalk datasets for analysis. The software solution, a mobile application called MySidewalk™, will facilitate the collection of sidewalk inventories and support directed insight decision making.
Built with open architectures and component technologies, MySidewalk™ will be developed with significant inputs from our partners, City of College Station, TX and Texas Transportation Institute making the solution amenable to a wide variety of public works crowd source collection efforts as well as other public entities such as healthcare, entertainment, education management work areas.
January 21, 2015 – KBSI received notification that its proposal for the MySidewalk™ application has been selected for award by the Department of Transportation Small Business Innovative Research program. “MySidewalk™” is a mobile application that will facilitate the crowd-sourced collection of sidewalk inventory and condition assessment data. The tool will utilize the recent advances in social networks, mobile data collection, and data mining to provide integrated sidewalk data-sets. The tool will be developed with significant inputs from our partners, City of College Station, TX and Texas Transportation Institute.