no code implementations • 15 Jun 2022 • Ryan Nguyen, Rahul Rai
The framework is composed of three components: (1) a reinforcement learning algorithm for data collection to develop a training dataset, (2) a deep learning algorithm for diagnosing faults, and (3) a handheld augmented reality application for data collection for testing data.
no code implementations • 15 Jun 2022 • Ryan Nguyen, Shubhendu Kumar Singh, Rahul Rai
Results on a bearing problem showcases the efficacy of adding a physics-based aggregation in a fuzzy logic model to improve GAN's ability to model health and give a more accurate system prognosis.
no code implementations • 3 Dec 2021 • Balaram Singh Kshatriya, Shiv Ram Dubey, Himangshu Sarma, Kunal Chaudhary, Meva Ram Gurjar, Rahul Rai, Sunny Manchanda
Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map.
Generative Adversarial Network Image-to-Image Translation +2
no code implementations • 27 Oct 2021 • Ryan Nguyen, Shubhendu Kumar Singh, Rahul Rai
This paper shows that adding a fuzzy logic layer can enhance GAN's ability to perform regression; the most desirable injection location is problem-specific, and we show this through experiments over various datasets.
1 code implementation • 3 Mar 2021 • Jun Wang, Wei Wayne Chen, Daicong Da, Mark Fuge, Rahul Rai
Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy ($R^2$-scores between target properties and properties of generated unit cells $>98\%$) and 2) improve the optimized structural performance over the conventional variable-density single-type structure.
no code implementations • 8 Dec 2020 • Zhibo Zhang, Chen Zeng, Maulikkumar Dhameliya, Souma Chowdhury, Rahul Rai
The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box.
no code implementations • 22 Mar 2020 • Ion Matei, Johan de Kleer, Christoforos Somarakis, Rahul Rai, John S. Baras
We describe how we can build models out of the p-H constructs and how we can train them.
no code implementations • 4 Mar 2020 • Ion Matei, Johan de Kleer, Alexander Feldman, Rahul Rai, Souma Chowdhury
In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models.