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