no code implementations • 18 Feb 2024 • J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, XiaoLi Li
During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.
no code implementations • 14 Feb 2024 • J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou, Min Wu, Jón Atli Benediktsson, XiaoLi Li
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
1 code implementation • 21 Dec 2023 • Mahindra Rautela, S. Gopalakrishnan, J. Senthilnath
The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities.
no code implementations • 13 Mar 2023 • Bangjian Zhou, Pan Jieming, Maheswari Sivan, Aaron Voon-Yew Thean, J. Senthilnath
Our proposed method achieved an overall accuracy of 86. 66% and compared with the second-best existing method it improves 15. 50% on the GAA-FET dislocation defect dataset.
1 code implementation • 13 Dec 2022 • Mahindra Rautela, J. Senthilnath, Armin Huber, S. Gopalakrishnan
The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material.
no code implementations • 14 Jun 2022 • Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning.
no code implementations • 13 May 2022 • J. Senthilnath, Nagaraj G, Sumanth Simha C, Sushant Kulkarni, Meenakumari Thapa, Indiramma M, Jón Atli Benediktsson
A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed.
no code implementations • 22 Apr 2022 • Mahindra Rautela, Armin Huber, J. Senthilnath, S. Gopalakrishnan
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i. e., finding layup sequence type and identifying material properties.
no code implementations • 20 Apr 2022 • Mahindra Rautela, J. Senthilnath, Ernesto Monaco, S. Gopalakrishnan
In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals.
no code implementations • 13 Feb 2022 • Jinraj V Pushpangathan, Harikumar Kandath, Rajdeep Dutta, Rajarshi Bardhan, J. Senthilnath
To solve this RAI consensus problem, first, the sufficient condition for the existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based simultaneous stabilization approach.
no code implementations • 14 Jan 2022 • Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J. Senthilnath, Chi Xu, Chee-Keong Kwoh
We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.