Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Lasitha Vidyaratne"'
Autor:
Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau
Publikováno v:
Machine Learning with Applications, Vol 13, Iss , Pp 100484- (2023)
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type t
Externí odkaz:
https://doaj.org/article/249bc77939d846c5abae8ea3e340255d
Autor:
Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md Monibor Rahman, Anna S. Shabalina
Publikováno v:
Frontiers in Artificial Intelligence, Vol 4 (2022)
This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous
Externí odkaz:
https://doaj.org/article/4281285e32e14f208616587bbc69ea25
Autor:
Zeina A. Shboul, Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, Mohamed I. Elbakary, Khan M. Iftekharuddin
Publikováno v:
Frontiers in Neuroscience, Vol 13 (2019)
Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing.
Externí odkaz:
https://doaj.org/article/0b07560866bb422a86599769b29ca5d0
Autor:
Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin
Publikováno v:
Physical Review Accelerators and Beams, Vol 23, Iss 11, p 114601 (2020)
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating l
Externí odkaz:
https://doaj.org/article/e6285c40191e41b884fdc27111bdfabd
Autor:
Majdi I. Radaideh, Chris Pappas, Jared Walden, Dan Lu, Lasitha Vidyaratne, Thomas Britton, Kishansingh Rajput, Malachi Schram, Sarah Cousineau
Publikováno v:
SSRN Electronic Journal.
Autor:
Adam Carpenter, Riad Suleiman, Christopher Tennant, Dennison Turner, Lasitha Vidyaratne, Khan Iftekharuddin, Md Rahman
Publikováno v:
ICALEPCS2021, 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Shanghai, China, 14-22 October 2021.
Publikováno v:
Neural Networks. 107:12-22
Representation learning plays an important role for building effective deep neural network models. Deep generative probabilistic models have shown to be efficient in the data representation learning task which is usually carried out in an unsupervise
Autor:
Christopher Tennant, Alexander Glandon, Lasitha Vidyaratne, Mahbubul Alam, Khan M. Iftekharuddin, Anna Shabalina
Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::231f804cd40baa9dfa6104d6c4a0a2e7
http://arxiv.org/abs/2101.05608
http://arxiv.org/abs/2101.05608
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor s