In this study, we focus on investigating a nonsmooth convex optimization problem involving the l 1-norm under a non-negative constraint, with the goal of developing an inverse-problem solver for image ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...

Abstract: To improve the estimation accuracy of the state of charge (SOC) in power batteries for electric vehicles, this study proposes a novel modeling and online SOC estimation method using Back ...

Early site preparation works were required for a big capital project site located close to the Arabian Gulf Coast in the Eastern Province of Saudi Arabia. The ground conditions at the delineated area ...

Build your own backpropagation algorithm from scratch using Python — perfect for hands-on learners! Parkinson’s Isn’t Just Bad Luck. Scientists Reveal It’s Largely Preventable—and the Culprit Is All ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

A next-gen Lagrange-Newton solver for nonconvex optimization. It unifies barrier and SQP methods in a modern and generic way, and implements different globalization flavors (line search/trust region ...

Abstract: Neural Networks are indispensable tools in adaptive signal processing. Multi-layer perceptron (MLP) neural network is one of the most widely used neural network architecture. The performance ...