ORCID Profile
0000-0002-3356-8917
Current Organisations
Indian Institute of Technology Kanpur
,
University of Southampton
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: IEEE
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: IEEE
Date: 08-2016
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 03-2012
Publisher: IOP Publishing
Date: 14-03-2016
Abstract: In this paper, self-consistent three-dimensional (3D) device simulations for exact analysis of thermal transport in FinFETs are performed. We analyze the temperature rise in FinFET devices with the variation in the number of fins ( N fin ), shape of fins and fin pitch ( F pitch ). We investigate that the thermal resistance R th has nonlinear dependency on N fin and F pitch . We formulate a model for thermal resistance behavior correctly with N fin and F pitch variation. The proposed formulation is implemented in industry standard Berkeley short-channel independent gate FET model for common multi-gate transistors (BSIM-CMG) and validated with both experimental data and TCAD simulations.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: IEEE
Date: 10-2015
Publisher: Elsevier
Date: 2015
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: IEEE
Date: 12-2014
Publisher: Elsevier
Date: 2019
Publisher: IEEE
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Elsevier
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Elsevier BV
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 10-2016
Publisher: IEEE
Date: 08-2016
Publisher: American Geophysical Union (AGU)
Date: 09-2021
DOI: 10.1029/2021EA001896
Abstract: Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for ex le, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Publisher: IEEE
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: IEEE
Date: 08-2016
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: IEEE
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: Wiley
Date: 09-2015
DOI: 10.14814/PHY2.12511
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Yogesh Singh Chauhan.