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SUMMARY:Structures as Sensors: Using Structures to Indirectly Monitor Huma
 ns and Surroundings
DTSTART:20180518T121500
DTEND:20180518T131500
DTSTAMP:20260407T115101Z
UID:ec22333905738b9be9fa194dbb4ea74c0a2ff6126ac78f1a25543ba8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Dr Hae Young Noh\, Assistant Professor Dept. of Civil & 
 Environmental Engineering and courtesy assistant professor Dept. of Electr
 ical & Computer Engineering\, Carnegie Mellon University\, Pittsburgh\, Pe
 nnsylvania\, USA\nThis talk introduces “structures as sensors” for the
  indirect monitoring of cyber-physical-human systems by sensing and analyz
 ing their noisy physical structural responses. Smart structures are design
 ed to sense\, understand\, and respond to various situations involving the
  structure itself\, the humans within\, and the surrounding environment. T
 raditional direct monitoring approaches using dedicated sensors often resu
 lt in dense and heterogeneous sensing systems that are difficult to instal
 l and maintain in large-scale structures. The conditions of the structure 
 itself\, the environment around\, and the activities of users within all h
 ave a direct impact on the physical responses of the structure. For exampl
 e\, human walking induces building floor vibrations\, uneven road surfaces
  and bridge settlement change vehicle motions\, etc.\nThis talk focuses on
  my “structures as sensors” approach that utilizes the structure as a 
 sensing medium to indirectly infer multiple types of hidden information re
 lating to the structure (e.g. the users and the surrounding). By using the
  same set of sensors for multiple types of information\, and because of wa
 ve propagation characteristics\, this approach significantly reduces the n
 umber and type of sensors needed to install and maintain. Challenges lie\,
  however\, in creating robust inference models for analyzing convoluted no
 isy structural response data (e.g.\, a mixture of building responses due t
 o human activities\, outside traffic\, seismic events). To this end\, I de
 veloped physics-guided data analytics approaches that combine statistical 
 signal processing and machine learning with physical principles (e.g.\, wa
 ve propagation\, human motions\, structural dynamics\, etc.). Specifically
 \, I present two projects as examples of this approach\; 1) Vehicles as Se
 nsors: indirect infrastructure health monitoring through vehicle responses
 \; and 2) Buildings as Sensors: occupant tracking and characterization thr
 ough footstep-induced building vibrations. I will also present results fro
 m the real-world experiments\, including our 3-year railway and eldercare 
 center deployments.\n \nBio: Hae Young Noh is an assistant professor in t
 he Dept. of Civil & Environmental Engineering and a courtesy assistant pro
 fessor in the Dept. of Electrical & Computer Engineering at Carnegie Mello
 n University. Her research focuses on indirect sensing and physics-guided 
 data analytics to enable low-cost and non-intrusive monitoring of cyber-ph
 ysical-human systems. The result of her work has been deployed in a number
  of real-world applications from trains\, to the Amish community\, to elde
 rcare centers\, to pig farms. She received her Ph.D. and M.S. degrees in C
 ivil and Environmental Engineering and the second M.S. degree in Electrica
 l Engineering at Stanford University. She earned her B.S. degree in Mechan
 ical and Aerospace Engineering at Cornell University. She received a numbe
 r of awards\, including the Google Faculty Research Awards in 2013 and 201
 6\, the Dean’s Early Career Fellowship in 2018\, and the National Scienc
 e Foundation CAREER award in 2017.
LOCATION:GC B3 30 https://plan.epfl.ch/?room=GCB330
STATUS:CONFIRMED
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