Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Radaideh, Majdi I."'
Reducing operation and maintenance costs is a key objective for advanced reactors in general and microreactors in particular. To achieve this reduction, developing robust autonomous control algorithms is essential to ensure safe and autonomous reacto
Externí odkaz:
http://arxiv.org/abs/2406.15931
Autor:
Joynt, Veda, Cooper, Jacob, Bhargava, Naman, Vu, Katie, Kwon, O Hwang, Allen, Todd R., Verma, Aditi, Radaideh, Majdi I.
In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an application could increase energy literacy -- an awareness of low-carbon energy
Externí odkaz:
http://arxiv.org/abs/2312.01180
Autor:
Goldenberg, Steven, Schram, Malachi, Rajput, Kishansingh, Britton, Thomas, Pappas, Chris, Lu, Dan, Walden, Jared, Radaideh, Majdi I., Cousineau, Sarah, Harave, Sudarshan
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard method
Externí odkaz:
http://arxiv.org/abs/2307.02367
Autor:
Alanazi, Yasir, Schram, Malachi, Rajput, Kishansingh, Goldenberg, Steven, Vidyaratne, Lasitha, Pappas, Chris, Radaideh, Majdi I., Lu, Dan, Ramuhalli, Pradeep, Cousineau, Sarah
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:
http://arxiv.org/abs/2304.10639
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advan
Externí odkaz:
http://arxiv.org/abs/2209.15570
Autor:
Radaideh, Majdi I., Tran, Hoang, Lin, Lianshan, Jiang, Hao, Winder, Drew, Gorti, Sarma, Zhang, Guannan, Mach, Justin, Cousineau, Sarah
Publikováno v:
Nucl. Instrum. Methods Phys. Res. B 525 (2022) 41-54
The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source (SNS). We leverage the ex
Externí odkaz:
http://arxiv.org/abs/2202.09353
Publikováno v:
Results in Physics 36 (2022) 105414
The reliability of the mercury spallation target is mission-critical for the neutron science program of the spallation neutron source at the Oak Ridge National Laboratory. We present an inverse uncertainty quantification (UQ) study using the Bayesian
Externí odkaz:
http://arxiv.org/abs/2202.03959
Autor:
Radaideh, Majdi I., Du, Katelin, Seurin, Paul, Seyler, Devin, Gu, Xubo, Wang, Haijia, Shirvan, Koroush
We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the
Externí odkaz:
http://arxiv.org/abs/2112.07057
Autor:
Kwon, O. Hwang, Vu, Katie, Bhargava, Naman, Radaideh, Mohammed I., Cooper, Jacob, Joynt, Veda, Radaideh, Majdi I.
Publikováno v:
In Renewable and Sustainable Energy Reviews August 2024 200
Publikováno v:
In Applied Mathematical Modelling July 2024 131:134-158