Results 71 to 80 of about 203,239 (310)
Quantum Dilated Convolutional Neural Networks
IEEE Access, 2022 In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements ...
Yixiong Chen
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Advanced Engineering Materials, EarlyView.Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Petrographic analysis with deep convolutional neural networks [PDF]
, 2019 Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies.
Pires de Lima, Rafael
core
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
, 2023 Spiking neural networks (SNNs) can be used in low-power and embedded systems e.g. neuromorphic chips due to their event-based nature. They preserve conventional artificial neural networks (ANNs) properties with lower computation and memory costs.
Fatemeh Sadat Tabatabaei Far +14 more
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A new approach to seasonal energy consumption forecasting using temporal convolutional networks
Results in Engineering, 2023 There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh +4 more
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Self-grouping convolutional neural networks [PDF]
Neural Networks, 2020 Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups
Qingbei Guo +3 more
openaire +4 more sources
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Analysis and training of a traffic sign recognition neural network model
Вестник Дагестанского государственного технического университета: Технические науки, 2023 Objective. The purpose of the research is to develop and train a neural network model based on convolutional neural networks for effective recognition of road signs in images.Method.
A. U. Mentsiev +2 more
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Hyperbolic Convolutional Neural Networks
CoRR, 2023 Deep Learning is mostly responsible for the surge of interest in Artificial Intelligence in the last decade. So far, deep learning researchers have been particularly successful in the domain of image processing, where Convolutional Neural Networks are used.
Andrii Skliar, Maurice Weiler
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Advanced Functional Materials, EarlyView.ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak +19 more
wiley +1 more source