Results 91 to 100 of about 1,718,101 (355)

An Improved New Convolutional Neural Network Method for Inverting the Pore Pressure in Oil Reservoir by Surface Vertical Deformation

Lithosphere, 2021
Average pore pressure in oil formation is an important parameter to measure energy in the formation and the capacity of injection–production. In past studies, average pore pressure mainly depends on pressure build-up test results, which have a high cost ...
Chaoyang Hu, Fengjiao Wang, Chi Ai, Xu Wang, Yongping Wang   +4 more
doaj   +1 more source

Homological Convolutional Neural Networks

CoRR, 2023
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still pose a challenge, with classic machine learning approaches being often computationally cheaper and equally effective than increasingly complex deep learning ...
Antonio Briola, Yuanrong Wang, Silvia Bartolucci, Tomaso Aste   +3 more
openaire   +3 more sources

Advancements in English listening education: Chat GPT and convolutional neural network integration [PDF]

, 2023
In today's globalized world, strong English listening skills have become more essential than ever before. Whether you're a language learner, a professional conducting business internationally, or a traveler exploring new cultures, the ability to ...
Runmei Xing
core   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Research onconvolutional neural network for reservoir parameter prediction

Tongxin xuebao, 2016
As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep ...
You-xiang DUAN, Gen-tian LI, Qi-feng SUN
doaj   +2 more sources

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

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, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Forest fire image recognition based on convolutional neural network

Journal of Algorithms & Computational Technology, 2019
In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms.
Yuanbin Wang, Langfei Dang, Jieying Ren
doaj   +1 more source

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
doaj   +1 more source

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Sensors, 2023
Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual ...
Guoliang Luo, Bingqin He, Yanbo Xiong, Luqi Wang, Hui Wang, Zhiliang Zhu, Xiangren Shi   +6 more
doaj   +1 more source

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, Xiao-Jun Wu 0001, Josef Kittler, Zhiquan Feng   +3 more
openaire   +4 more sources