Results 61 to 70 of about 5,452,386 (223)
Machine Learning‐Driven Variability Analysis of Process Parameters for Semiconductor Manufacturing
Advanced Intelligent Systems, EarlyView.This research presents a machine learning approach that integrates nonlinear variation decomposition (NLVD) with statistical techniques to quantify the contribution of individual unit processes to performance and variance of figure of merit (FoM) at the LOT level.
Sinyeong Kang +6 more
wiley +1 more source
Online Signature for Attendance Verification System using Levenberg-Marquardt Neural Network [PDF]
, 2015 This work focuses on the application of Levenberg-Marquardt based Back-propagation Neural Network for training features extracted from online signature images.
Adefuminiyi, Morakinyo A. +2 more
core +1 more source
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
, 2016 Motion estimation across low-resolution frames and the reconstruction of high-resolution images are two coupled subproblems of multi-frame super-resolution.
Bercea, Cosmin +2 more
core +1 more source
Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
Modeling Leachate Generation Using Artificial Neural Networks [PDF]
آب و فاضلاب, 2011 In this study, a neural network model is proposed for modeling leachate flow-rate in a municipal solid waste landfill site. After training, the neural network model predicts leachate generation based on meteorological data and leachate characteristics ...
Mohammad Javad Zoqi, Mohsen Saeedi
doaj
Enabling Stochastic Dynamic Games for Robotic Swarms
Advanced Intelligent Systems, EarlyView.This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
Do optimization methods in deep learning applications matter? [PDF]
, 2020 With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention.
Kiran, Mariam, Ozyildirim, Buse Melis
core +1 more source
Asia-Pacific Journal of Chemical Engineering, EarlyView.ABSTRACT In this study, the actual route of methylene blue (MB) dye adsorption by using fabricated polyfunctional activated carbon–copper oxide nanowires (AC@CuO‐NWs) from bulky wastewater bodies has been investigated. To better understand the exact pathway of the adsorption process, a prominent statistical physics formalism or grand canonical ...
Abdellatif Sakly +7 more
wiley +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.Abstract Global energy demand and environmental concerns have intensified the search for renewable and sustainable energy sources. This study thus, focuses on optimizing the transesterification process of waste cooking oil (WCO) using thermally activated basic oxygen furnace slag catalyst calcined at 850°C (BOF 850). The optimization and modelling were
Johra S. Ali, Hillary L. Rutto
wiley +1 more source
Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction
Discrete Dynamics in Nature and Society, 2014 For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. When the multilayer perceptron is used to predict the Mackey-Glass chaotic time series, what we should do is to minimize the loss function.
Junsheng Zhao +3 more
doaj +1 more source