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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221111T150000
DTEND;TZID=Asia/Kolkata:20221111T160000
DTSTAMP:20260618T160824
CREATED:20221106T232123Z
LAST-MODIFIED:20221106T233337Z
UID:240104-1668178800-1668182400@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Sayantan Das
DESCRIPTION:Thesis Title:  Modeling of lightning attachment to aircraft and  quantification of the influencing parameters \nGuide: Prof. Udaya Kumar \nDegree registered:          Ph.D. \nDate and Time:              11th November 2022\, 9:30 AM\nMeeting link:                  https://teams.microsoft.com/l/meetup-join/19%3ameeting_MDg5YzdhYWUtMTc3Zi00Yjg0LWE1ZTktYjgyY2I5Y2MyNDI4%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227ef4df52-6005-46aa-bff3-d96db1a85b71%22%7d \nAbstract: According to Air Transport Action Group (ATAG)\, 45 million aircraft took off worldwide in 2020\, which translates to 1.5 lakh per day. Statistically\, the aviation industry is found to double its fleet size every fifteen years. Lightning is considered one of the dreadful environmental threats to aircraft. Past incidents show that lightning strikes can lead to structural damage\, operational interruption\, and loss of lives. Field data suggest that\, on average\, an aircraft gets struck by lightning once or twice a year. Therefore\, the threat due to lightning is considered a crucial safety aspect of an aircraft. \nDesign of suitable lightning protection for aircraft involves Zoning of its skin. It is intended to differentiate lightning attachment points\, channel slipping regions\, and regions that carry just the stroke current. The first step of Aircraft Zoning is to identify the initial attachment points. For the same\, different methods like Laboratory experiments\, similarity principle\, Rolling Sphere Method (RSM)\, and Field-based approach are suggested in the standard ARP5414. In reality\, the lightning strikes to aircraft can be of two modes\, Aircraft-initiated and Aircraft-intercepted. In the former one\, under the influence of a thundercloud or descending lightning leader\, the aircraft initiates stable bipolar connecting leaders\, upward and downward leader toward the ground. These leaders are deemed to propagate hundreds of meters to complete the lightning strike. In Aircraft-intercepted strikes\, the aircraft intercepts a descending lightning leader and hence gets struck. The laboratory experiments on scaled aircraft models or isolated aircraft parts are inadequate to assess the initial attachment points. The similarity principle suggested in the standard is qualitative and can’t be extended to aircraft of any size and shape. The 25m Rolling Sphere Method (RSM) is routinely employed to determine the attachment points. This method doesn’t consider the connecting leader discharges from aircraft and therefore overestimates the possible attachment points. Most (90%) of the lightning strikes to aircraft are attributed to aircraft-initiated mode\, which involves significant connecting leader activities. Therefore\, it has to be traced accurately to assess attachment points. \nIn literature\, it is difficult to find a model for bipolar leader discharges from aircraft. However\, work on either negative or positive leader inception and propagation from laboratory gaps and their extension can be relatively found. Based on them\, the present work aims to develop a suitable model for simulating bipolar leader discharges from aircraft. Additionally\, the aircraft-intercepted mode of lightning strikes is also included. In summary\, a novel model adapting the pertinent physical aspects of the leader discharges has been developed to accurately assess initial lightning attachment points to aircraft.  \nUsing the model developed\, the following practically important questions are addressed:  \n\nDependency of the frequency of lightning strikes to aircraft on its shape and size.\nRate of lightning strikes to aircraft at different altitudes\nFor a given aircraft and its route\, the number of times it gets struck by lightning\nThe average number of strikes to an aircraft per year\n\nTo present a quantitative assessment\, two different aircraft models\, McDonnell Douglas DC-10 and Standard Dynamic Model are considered. \nIn summary\, a novel model based on physical grounds has been developed to assess the initial lightning attachment points on aircraft. Using the same\, further methodologies are constructed to quantify the dependency of the strike rate on aircraft size\, altitude\, and possible average strike rate. \nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-sayantan-das/
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DTSTART;TZID=Asia/Kolkata:20221111T170000
DTEND;TZID=Asia/Kolkata:20221111T170000
DTSTAMP:20260618T160824
CREATED:20221107T014902Z
LAST-MODIFIED:20221107T025832Z
UID:240109-1668186000-1668186000@ee.iisc.ac.in
SUMMARY:Lecture by Dr. Ganesh Sivararaman
DESCRIPTION:Indian Institute of Science and\nThe IEEE Signal Processing Society\, Bangalore Chapter\nCordially invites you to the following talk on\n“Unsupervised adaptation in speech technologies”\n(Click here for the poster.) \nSpeaker: Dr. Ganesh Sivaraman\, Pindrop\, Atlanta\, USA\nDate and Time: 11th November 2022 at 11:30am to 12:30pm\nVenue: MMCR (Room No. C241)\, 1st Floor\, Dept. of Electrical Engineering \nAbstract: Unsupervised learning and adaptation techniques have taken center stage due to the exponential growth of unlabeled data. For many practical applications unsupervised learning helps in the adaptation of machine learning systems to mismatched train and test domains. Unsupervised adaptation can be performed by three broad approaches – 1) feature transformations in the test domain\, 2) model adaptation to test domain\, and 3) generation of synthetic test domain samples. This talk will outline these methods by showing three specific examples from speech processing. Unsupervised speaker adaptation for acoustic-to-articulatory speech inversion serves as an example of feature transformation-based adaptation. Adaptation of end-to-end ASR systems without manual transcriptions will be presented as an example of model adaptation. Finally\, children’s speech simulation for zero-shot child speech classification using X-vectors will be presented as an example of synthetic data generation for the test domain. \nBiography:  Ganesh Sivaraman is a Senior Research Scientist at Pindrop\, in Atlanta\, USA. He received his M.S. (2013) and Ph.D. (2017) in Electrical Engineering from the University of Maryland College Park. His research experience and publications span several speech technologies like acoustic-to-articulatory inversion\, ASR\, speaker recognition\, deepfake detection\, and speech enhancement. During his PhD at Maryland\, he was awarded the Future Faculty Fellowship\, and the International Graduate Research Fellowship by the A. James Clark School of Engineering. Along with his official work\, he is actively involved in teaching\, mentoring\, and collaborating with doctoral students at Maryland. Apart from research work\, Ganesh is a fluent speaker of Sanskrit actively learning and teaching the language as a volunteer of Samskrita Bharati USA. He is passionate about creating computational tools for learning Sanskrit pronunciation.
URL:https://ee.iisc.ac.in/event/lecture-by-dr-ganesh-sivararaman/
LOCATION:EE\, MMCR
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