skip to main content


Search for: All records

Creators/Authors contains: "Kalyanam, Rajesh"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

     
    more » « less
  2. Joseph Paris, Jackie Milhans (Ed.)
    The Cyber Human Ecosystem for Engaged Security Education (CHEESEHub) is an open web platform that hosts communitycontributed containerized demonstrations of cybersecurity concepts. In order to maximize flexibility, scalability, and utilization, CHEESEHub is currently hosted in a Kubernetes cluster on the Jetstream academic cloud. In this short paper, we describe the security model of CHEESEHub and specifically the various Kubernetes security features that have been leveraged to secure CHEESEHub. This ensures that the various cybersecurity exploits hosted in the containers cannot be misused, and that potential malicious users of the platform are cordoned off from impacting not just other legitimate users, but also the underlying hosting cloud. More generally, we hope that this article will provide useful information to the research computing community on a less discussed aspect of cloud deployment: the various security features of Kubernetes and their application in practice. 
    more » « less
  3. null (Ed.)