Results 71 to 80 of about 2,625,571 (293)

Convolutional Gated Recurrent Unit–Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

IEEE Access, 2019
For most deep learning practitioners, recurrent networks are often used for sequence modeling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks.
Zhelin Huang, Fangfang Yang, Fan Xu, Xiangbao Song, Kwok-Leung Tsui   +4 more
doaj   +1 more source

Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural Network

한국해양공학회지, 2021
This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training
Seongpil Cho, Jongseo Park, Minjoo Choi
doaj   +1 more source

4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome

, 2023
4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae ...
Mohammed Nasir Uddin (14855899), Md Naimul Pathan (17391367), Abida Sultana (8147670), Md Ashraf Uddin (14681605), Sadia Jannat Mitu (17391364), Sunil Aryal (13096509)   +5 more
core  

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Attentive Gated Recurrent Unit

, 2020
Recurrent Neural Network (RNN) can train time sequence data of complex information, and has also been successfully applied to issues such as handwriting recognition, image description, natural language processing, and video motion analysis.
Chi, Wei-Chih
core  

Peramalan IHSG Berdasarkan Faktor Indeks Global Menggunakan Metode Long Short-Term Memory dan Gated Recurrent Unit [PDF]

Investasi merupakan kegiatan menanam modal di masa sekarang dengan mengharapkan mendapatkan pengembalian di masa mendatang. Investasi dapat dilakukan dalam berbagai bentuk, salah satunya adalah saham.
Lammoreno, Sultan Tanri; Departemen Aktuaria, Insititut Teknologi Sepuluh Nopember (ITS), Siswono, Galuh Oktavia; Departemen Aktuaria, Insititut Teknologi Sepuluh Nopember (ITS)   +1 more
core   +4 more sources

Pouring Dynamics Estimation Using Gated Recurrent Units

CoRR, 2021
7 pages, 8 figures, 4 ...
openaire   +2 more sources

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Uncertainty quantification-based framework for predicting degradation trends of proton exchange membrane fuel cell

Green Energy and Intelligent Transportation
Accurately predicting the degradation trends of proton exchange membrane fuel cells (PEMFCs) can provide a solid basis for optimizing the control of vehicles and stations based on PEMFCs.
Bingxin Guo, Changjun Xie, Wenchao Zhu, Yang Yang, Hao Li, Yang Li, Hangyu Wu   +6 more
doaj   +1 more source

Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network

IEEE Access, 2021
To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper,
Hanhong Shi, Lei Wang, Rafal Scherer, Marcin Wozniak, Pengchao Zhang, Wei Wei   +5 more
doaj   +1 more source