Search Results for author: Majdi I. Radaideh

Found 6 papers, 1 papers with code

Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions

no code implementations5 Jul 2023 Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave

Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems.

Dimensionality Reduction

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

no code implementations20 Apr 2023 Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau

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).

Vocal Bursts Type Prediction

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

no code implementations30 Sep 2022 Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau

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).

Ensemble Learning Fault Detection

Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions

no code implementations18 Feb 2022 Majdi I. Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew Winder, Sarma Gorti, Guannan Zhang, Justin Mach, Sarah Cousineau

Given that some of the calibrated parameters that show a good agreement with the experimental data can be nonphysical mercury properties, we need a more advanced two-phase flow model to capture bubble dynamics and mercury cavitation.

NEORL: NeuroEvolution Optimization with Reinforcement Learning

1 code implementation1 Dec 2021 Majdi I. Radaideh, Katelin Du, Paul Seurin, Devin Seyler, Xubo Gu, Haijia Wang, Koroush Shirvan

NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforcement learning, and hybrid neuroevolution algorithms.

Benchmarking reinforcement-learning +1

Improving Intelligence of Evolutionary Algorithms Using Experience Share and Replay

no code implementations10 Aug 2020 Majdi I. Radaideh, Koroush Shirvan

We propose PESA, a novel approach combining Particle Swarm Optimisation (PSO), Evolution Strategy (ES), and Simulated Annealing (SA) in a hybrid Algorithm, inspired from reinforcement learning.

Evolutionary Algorithms

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