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SUMMARY:Towards Future Intelligent Structural Systems via Bio-inspired Sta
 te-sensing and Awareness
DTSTART:20170314T101500
DTEND:20170314T111500
DTSTAMP:20260406T194851Z
UID:5b8d83c778e31cc9aab318c6a286d2c1abfc06643542141a20ab1bf4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Fotis Kopsaftopoulos\, Department of Aeronautics and Astro
 nautics\, Stanford University - USA\nFuture intelligent mechanical\, aeros
 pace and civil structural systems will be able to “feel”\, “think”
 \, and “react” in real time based on high-resolution state-sensing\, a
 wareness\, and self-diagnostic capabilities. They will be able to sense an
 d observe phenomena at unprecedented length and time scales allowing for s
 uperior performance and adaptability\, increased safety and resilience\, o
 ptimal control\, reduced operational and maintenance costs\, and complete 
 life-cycle management. Towards this end\, current research efforts aim at 
 the development of novel technologies that will lead to the next generatio
 n of intelligent self-sensing self-diagnostic structural systems that can 
 sense the surrounding environment\, interpret the sensing information in r
 eal time\, determine their actual operating state and health condition in 
 complex dynamic environments\, and make optimal decisions for control and 
 mitigation in the face of uncertainty.\n\nIn an effort to address these go
 als in a unified way\, this talk will present a novel framework for the de
 velopment of intelligent self-sensing self-diagnostic structural systems i
 nspired by the unprecedented sensing and awareness capabilities of biologi
 cal systems. This framework incorporates (i) bio-inspired distributed mult
 i-modal micro-sensor networks\, (ii) data-driven methods for the global mo
 deling and identification of structural systems under varying operating st
 ates and uncertainty\, and (iii) integrated state awareness and structural
  health monitoring (SHM) approaches for inferring the actual operating and
  structural health state. Prototype intelligent systems with embedded sens
 ing and awareness capabilities will be presented with special emphasis pla
 ced on the novel concept of “fly-by-feel” aerial vehicles. Distributed
  micro-sensors in the form of stretchable sensor networks are used to prov
 ide the sensing capabilities\, while stochastic system identification\, st
 atistical signal processing\, machine learning and SHM diagnostic techniqu
 es are employed for the accurate interpretation of the sensing data and su
 bsequent determination the operating state and structural health condition
 . The ultimate goal of this presentation is to provide a concise overview 
 of the main research developments that constitute a “conceptual leap” 
 to overcome the current limitations towards the development of the next ge
 neration of intelligent structural systems that can “feel”\, “think
 ”\, and “react”.\n\nBio:\nDr. Fotis Kopsaftopoulos is a Postdoctoral
  Research Fellow in the Structures and Composites Laboratory in the Depart
 ment of Aeronautics and Astronautics at Stanford University. He received h
 is Diploma and Ph.D. in Mechanical and Aeronautical Engineering from Unive
 rsity of Patras\, Greece\, on the topic of advanced functional and sequent
 ial time series methods for vibration-based structural health monitoring (
 SHM). His background and interests span the areas of intelligent structura
 l systems with embedded sensing and awareness capabilities\, structural he
 alth monitoring (SHM)\, stochastic modeling and identification\, bio-inspi
 red fly-by-feel aerial vehicles\, and integration of data-based with multi
 -scale physics-based methods for “smart” data analysis. Dr. Kopsaftopo
 ulos has participated in various national\, international and industrially
  supported research projects both in the U.S.A. (AFOSR\, NASA\, NSF\, ARPA
 -e\, Boeing) and Europe (FP6 and FP7\, Airbus\, RUAG Space). He is a membe
 r of the Organizing Committee of the International Workshop on Structural 
 Health Monitoring (IWSHM) and serves as an Associate Editor of the Structu
 ral Health Monitoring international journal.\n 
LOCATION:ME B1 B10 https://plan.epfl.ch/theme/generalite_thm_v2?request_lo
 cale=fr&room=meb10&domain=places&dim_floor=1&lang=fr&dim_lang=fr&tree_grou
 ps=centres_nevralgiques%2Cacces%2Cmobilite_reduite%2Censeignement%2Ccom
STATUS:CONFIRMED
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