296 research outputs found
Dynamic Stress Analysis of Viscoelastic Rotor
The present work deals with the study of stresses in viscoelastic rotor which are dynamic in nature. Due to internal damping of the rotor material, dynamic characteristics get affected and hence it is studied to understand the behavior in terms of Campbell plot, mode shapes etc. This study starts with modelling of beam, where viscoelastic material was considered. The solution for time domain was obtained through state space approach. For discretizing the continuum finite element method is used based on Euler Bernoulli beam theory. Then stresses were found for cantilever beam. This modelling was further used to model viscoelastic rotor. Stable limit speed as a function for different torque is plotted, which is found to remain constant for varying torques. The bending as well as shear stresses were calculated. For designing of rotor here non-ferrous material was considered as they do not exhibit endurance limit. Then the rotor was analyzed based on dynamic shear and dynamic bending stress, equivalent stresses were obtained and the location which was subjected to maximum stresses was focused and using design equations, life of rotor before failure was found
Task2Sim: towards effective pre-training and transfer from synthetic data
Department of Defense/ARO; CCF-2007350 - National Science Foundation; CCF-1955981 - National Science Foundationhttps://openaccess.thecvf.com/content/CVPR2022/papers/Mishra_Task2Sim_Towards_Effective_Pre-Training_and_Transfer_From_Synthetic_Data_CVPR_2022_paper.pdfFirst author draf
Fine-grained Few-shot Recognition by Deep Object Parsing
In our framework, an object is made up of K distinct parts or units, and we
parse a test instance by inferring the K parts, where each part occupies a
distinct location in the feature space, and the instance features at this
location, manifest as an active subset of part templates shared across all
instances. We recognize test instances by comparing its active templates and
the relative geometry of its part locations against those of the presented
few-shot instances. We propose an end-to-end training method to learn part
templates on-top of a convolutional backbone. To combat visual distortions such
as orientation, pose and size, we learn multi-scale templates, and at test-time
parse and match instances across these scales. We show that our method is
competitive with the state-of-the-art, and by virtue of parsing enjoys
interpretability as well
How transferable are video representations based on synthetic data?
Army Research Office; CCF-2007350 - National Science Foundation; CCF-1955981 - National Science Foundationhttps://openreview.net/pdf?id=lRUCfzs5Hz
Surprisingly simple semi-supervised domain adaptation with pretraining and consistency
Visual domain adaptation involves learning to classify
images from a target visual domain using labels available
in a different source domain. A range of prior work uses
adversarial domain alignment to try and learn a domain invariant
feature space, where a good source classifier can
perform well on target data. This however, can lead to errors
where class A features in the target domain get aligned
to class B features in source. We show that in the presence
of a few target labels, simple techniques like selfsupervision
(via rotation prediction) and consistency regularization
can be effective without any adversarial alignment
to learn a good target classifier. Our Pretraining and
Consistency (PAC) approach, can achieve state of the art
accuracy on this semi-supervised domain adaptation task,
surpassing multiple adversarial domain alignment methods,
across multiple datasets. Notably, it outperforms all
recent approaches by 3-5% on the large and challenging
DomainNet benchmark, showing the strength of these simple
techniques in fixing errors made by adversarial alignmentPublished versio
Surprisingly simple semi-supervised domain adaptation with pretraining and consistency
Most modern unsupervised domain adaptation (UDA) approaches are rooted in domain
alignment, i.e., learning to align source and target features to learn a target domain
classifier using source labels. In semi-supervised domain adaptation (SSDA), when the
learner can access few target domain labels, prior approaches have followed UDA theory
to use domain alignment for learning. We show that the case of SSDA is different
and a good target classifier can be learned without needing alignment. We use self-supervised
pretraining (via rotation prediction) and consistency regularization to achieve
well separated target clusters, aiding in learning a low error target classifier. With our
Pretraining and Consistency (PAC) approach, we achieve state of the art target accuracy
on this semi-supervised domain adaptation task, surpassing multiple adversarial domain
alignment methods, across multiple datasets. PAC, while using simple techniques, performs
remarkably well on large and challenging SSDA benchmarks like DomainNet and
Visda-17, often outperforming recent state of the art by sizeable margins. Code for our
experiments can be found at https://github.com/venkatesh-saligrama/PAC.https://www.bmvc2021-virtualconference.com/assets/papers/0764.pdfPublished versio
Learning Human Action Recognition Representations Without Real Humans
Pre-training on massive video datasets has become essential to achieve high
action recognition performance on smaller downstream datasets. However, most
large-scale video datasets contain images of people and hence are accompanied
with issues related to privacy, ethics, and data protection, often preventing
them from being publicly shared for reproducible research. Existing work has
attempted to alleviate these problems by blurring faces, downsampling videos,
or training on synthetic data. On the other hand, analysis on the
transferability of privacy-preserving pre-trained models to downstream tasks
has been limited. In this work, we study this problem by first asking the
question: can we pre-train models for human action recognition with data that
does not include real humans? To this end, we present, for the first time, a
benchmark that leverages real-world videos with humans removed and synthetic
data containing virtual humans to pre-train a model. We then evaluate the
transferability of the representation learned on this data to a diverse set of
downstream action recognition benchmarks. Furthermore, we propose a novel
pre-training strategy, called Privacy-Preserving MAE-Align, to effectively
combine synthetic data and human-removed real data. Our approach outperforms
previous baselines by up to 5% and closes the performance gap between human and
no-human action recognition representations on downstream tasks, for both
linear probing and fine-tuning. Our benchmark, code, and models are available
at https://github.com/howardzh01/PPMA .Comment: 19 pages, 7 figures, 2023 NeurIPS Datasets and Benchmarks Trac
A novel hybrid approach for automated detection of retinal detachment using ultrasound images
Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis
- …