Search for: All records

For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future pre- diction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores. 

This paper proposes an AR-based real-time mobile system for assistive indoor navigation with target segmentation (ARMSAINTS) for both sighted and blind or low-vision (BLV) users to safely explore and navigate in an indoor environment. The solution comprises four major components: graph construction, hybrid modeling, real-time navigation and target segmentation. The system utilizes an automatic graph construction method to generate a graph from a 2D floorplan and the Delaunay triangulation-based localization method to provide precise localization with negligible error. The 3D obstacle detection method integrates the existing capability of AR with a 2D object detector and a semantic target segmentation model to detect and track 3D bounding boxes of obstacles and people to increase BLV safety and understanding when traveling in the indoor environment. The entire system does not require the installation and maintenance of expensive infrastructure, run in real-time on a smartphone, and can easily adapt to environmental changes.