IOCAS-IR

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Application of artificial neural networks throughout the entire life cycle of coatings: A comprehensive review 期刊论文
PROGRESS IN ORGANIC COATINGS, 2024, 卷号: 189, 页码: 22
作者:  Ning, Zenglei;  Zhao, Xia;  Fan, Liang;  Peng, Zhongbo;  Ma, Fubin;  Jin, Zuquan;  Deng, Junying;  Duan, Jizhou;  Hou, Baorong
收藏  |  浏览/下载:47/0  |  提交时间:2024/06/04
Artificial neural networks  Formulation design  Preparation process  micro defects  Service life  
Prediction of 3-D Ocean Temperature Based on Self-Attention and Predictive RNN 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 卷号: 21, 页码: 5
作者:  Yue, Weihao;  Xu, Yongsheng;  Xiang, Liang;  Zhu, Shanliang;  Huang, Chao;  Zhang, Qingjun;  Zhang, Liqiang;  Zhan, Xiangguang
收藏  |  浏览/下载:68/0  |  提交时间:2024/06/04
Climate change  Recurrent neural networks  Temperature measurement  Ocean temperature  Predictive models  Global gridded temperature data with Barnes objective analysis (BOA-ARGO)  predictive recurrent neural network (PredRNN)  self-attention  
Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 8043-8057
作者:  Chen, Wantai;  Li, Xiaofeng
收藏  |  浏览/下载:2/0  |  提交时间:2024/08/29
Aquaculture  Radar polarimetry  Imaging  Tides  Monitoring  Task analysis  Radar imaging  Deep convolution neural networks (DCNNs)  imaging characteristics  marine aquaculture zones (MAZs)  Sentinel-1  synthetic aperture radar (SAR)  
Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model 期刊论文
DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS, 2023, 卷号: 202, 页码: 14
作者:  Zhao, Yizhi;  Qi, Jifeng;  Zhu, Shanliang;  Jia, Wentao;  Gong, Xiang;  Yin, Wenming;  Yin, Baoshu
Adobe PDF(7659Kb)  |  收藏  |  浏览/下载:74/0  |  提交时间:2024/04/07
Barrier layer thickness  Particle swarm optimization  Artificial neural networks  Hybrid models  
Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 14
作者:  Zhou, Yuan;  Feng, Yanjie;  Huo, Shuwei;  Li, Xiaofeng
收藏  |  浏览/下载:260/0  |  提交时间:2022/12/09
Feature extraction  Frequency-domain analysis  Remote sensing  Optical sensors  Optical imaging  Frequency domain analysis  Optical fiber networks  Change detection  deep learning  frequency domain  neural network  optical image  
Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 18
作者:  Liu, Guangyang;  Liu, Bin;  Zheng, Gang;  Li, Xiaofeng
收藏  |  浏览/下载:165/0  |  提交时间:2023/01/12
Deep convolutional neural networks (DCNNs)  deep learning  environment monitoring  intertidal zone  MB-U-2-ACNet  synthetic aperture radar (SAR) imagery  
Underwater Image Enhancement via Physical-Feedback Adversarial Transfer Learning 期刊论文
IEEE JOURNAL OF OCEANIC ENGINEERING, 2021, 页码: 12
作者:  Zhou, Yuan;  Yan, Kangming;  Li, Xiaofeng
Adobe PDF(8751Kb)  |  收藏  |  浏览/下载:208/0  |  提交时间:2022/02/18
Degradation  Adaptation models  Image restoration  Image color analysis  Image enhancement  Convolutional neural networks  Data models  Degradation model  domain adaptation  generative adversarial networks  underwater image enhancement  
Global Drag-Coefficient Estimates From Scatterometer Wind and Wave Steepness 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 卷号: 49, 期号: 5, 页码: 1499-1503
作者:  Liu, Guoqiang;  He, Yijun;  Shen, Hui;  Guo, Jie
Adobe PDF(1033Kb)  |  收藏  |  浏览/下载:195/1  |  提交时间:2012/07/03
Air-sea Interaction  Neural Networks (Nns)  Remote Sensing  
A new model to estimate significant wave heights with ERS-1/2 scatterometer data 期刊论文
CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2009, 卷号: 27, 期号: 1, 页码: 112-116
作者:  Guo Jie;  He Yijun;  Perrie, William;  Shen Hui;  Chu Xiaoqing
Adobe PDF(347Kb)  |  收藏  |  浏览/下载:332/4  |  提交时间:2010/12/22
Scatterometer  Significant Wave Height  Neural Networks  Wind Waves  Swell