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SUMMARY:NSF Workshop on Predictive Intelligence for Pandemic Prevention
DESCRIPTION:NSF Predictive Intelligence for Pandemic Prevention (PIPP) Workshop – Pandemic Readiness for Emerging Pathogens (PREP)  \nThe goal is to foster scientific discussion and catalyze innovation and partnerships to significantly enhance our understanding of the challenges and potential solutions to rapid detection and assessment of emerging pathogens and infectious disease dynamics from the molecular to the ecological scale. We invite participants from academia\, government\, industry and non-governmental organizations from varied disciplines: Engineering (ENG)\, Biological Sciences (BIO)\, Computer and Information Science and Engineering (CISE)\, and Social\, Behavioral and Economic Sciences (SBE). \nPREP has four topical thrusts: \n1) Rapid and Accurate Detection and Assessment of Emerging Pathogens \n2) Monitoring Environmental Change\, Animal Movements\, and High-Risk Interfaces for Disease Transmission \n3) Monitoring Human Movements and At-risk Communities for Disease Transmission and Spread \n4) Data-Intensive Machine Learning and Modeling for Pandemic Preparedness. \nEach topical thrust has vision talks\, panels\, and breakout sessions.\nThe goal of the workshop is a roadmap for research investments to address key technical and scientific challenges for pandemic prevention. \nFor more information\, agenda\, list of invited speakers\, and registration: https://thepipp.org \nDirect registration \n\nOrganized by ECE Professor Nian X. Sun with support from NSF grant.
URL:https://ece.northeastern.edu/event/nsf-workshop-on-predictive-intelligence-for-pandemic-prevention/
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DTSTART;TZID=America/New_York:20210217T110000
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UID:4718-1613559600-1613563200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Andre Langner
DESCRIPTION:PhD Proposal Review: Multi-area Distribution State Estimation Using a Virtual Reference \nAndre Langner \nLocation: Teams Link \nAbstract: State Estimation (SE) is the backbone of modern Energy Management System due to its capability of processing real-time measurements and provide reliable information to system operators. Since its introduction to power systems in the ’70s\, SE has been widely used in transmission networks. Distribution grids\, on the other hand\, lack sufficient number of real-time measurements\, and for that reason\, SE has not been widely implemented on these systems. The recent increase in the number of renewable energy sources connected to the grid at lower voltage levels\, the advent of Distribution Automation\, and Smart Grids necessitate closer monitoring of distribution networks. Thus\, forcing utilities to upgrade their operations and deploy Advanced Distribution Management Systems. Therefore\, Distribution System State Estimation (DSSE) is paramount to provide real-time monitoring of active distribution grids. In the first part of this proposal\, a three-phase distribution system state estimator is presented\, especially for highly unbalanced networks. In the second part\, the Multi-area State Estimation (MASE) approach is proposed to distribution systems\, by a partition into non-overlapping areas\, aiming at reducing the overall execution time. Furthermore\, it is also proposed to combine MASE along with the so-called Generalized State Estimation to identify topology errors causing divergence in the state estimation process.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-andre-langner/
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