Spatio-Temporal AI Inference Engines for System-Level Reliability

High-dimensional Data-driven Energy optimization for Multi-Modal transit Agencies (HD-EMMA)

Transportation accounts for 28% of the total energy use in the United States and as such, it is responsible for immense environmental impact, including urban air pollution and greenhouse gas emissions, and may pose a severe threat to energy security. As we encourage mode shift from personal vehicles to public transit, it is important to consider that public transit systems still require substantial amounts of energy; for example, public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO2 emission annually.

SCC-IRG Track 1: Mobility for all - Harnessing Emerging Transit Solutions for Underserved Communities

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service.

NeTS: JUNO2: STEAM: Secure and Trustworthy Framework for Integrated Energy and Mobility in Smart Connected Communities

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data.

EdgeNet: An online Edge Computing Based Generative Anomaly Detection and Prognostics Solution for Networked Equipment at Customer Premises

Anomaly detection, prognostication and automated mitigation are very critical for data center management. Most of these approaches can be divided into two categories - model-based and data-driven. While model-based techniques rely on physics guided models that can explain and predict the expected progression of parameters such as temperature and voltage in electronics, the data-driven approach is suitable for complex scenarios where a suitable physics based model is unavailable. The data-driven approaches can be further divided into supervised and unsupervised methods.

AI-Engine for Optimizing Integrated Service Mixed Fleet Transit Operations

In every public transit system, a trade-off has to be made between concentrating service into very useful routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. Improving the efficiency of an existing system while enhancing service in terms of both usefulness and coverage presents considerable challenges.

Rapid Scenario-Driven Integrated Simulation Experimentation Framework

Cyber-Physical Systems (CPS) are composed of a wide range of networked physical, computational, and human/organization components. These systems are highly complex as they have many different heterogeneous components, such as physical, computational, and human. Simulation-based evaluation of the behavior of CPS is complex, as it involves multiple, heterogeneous, interacting domains. Each simulation domain has sophisticated tools, but their integration into a coherent framework is a difficult, time-consuming, labor-intensive, and error-prone task.

Air Taxi (Hybrid or Electric) aero Nautical Simulation (ATHENS)

Automated design processes, especially using Machine Learning/AI techniques, require proposed systems to be evaluated across all relevant attributes, requirements, and concerns.  Traditionally, teams create models in a set of engineering tools for design evaluation data. 

Integrated Microgrid Control Platform (IMCP)

This project aims at developing and demonstrating a highly reusable ‘software platform’ that can be easily adapted to and used in various microgrid configurations.
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