机构

• 数学与系统科学研究院 [3]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2021 [1]
• 2020 [2]
• 2010 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method 期刊论文  OAI收割 APPLIED SOFT COMPUTING, 2021, 卷号: 113, 页码: 15 作者:  Jiang, Fuxin;  Zhang, Chengyuan;  Sun, Shaolong;  Sun, Jingyun   |  收藏  |  浏览/下载：41/0  |  提交时间：2022/04/29 PM2.5 concentration forecasting  Complete ensemble empirical mode  decomposition with adaptive noise  Temporal convolutional  Data patterns  Deep learning   Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm 期刊论文  OAI收割 RENEWABLE ENERGY, 2020, 卷号: 162, 页码: 1208-1226 作者:  Hu, Jianming;  Heng, Jiani;  Wen, Jiemei;  Zhao, Weigang   |  收藏  |  浏览/下载：23/0  |  提交时间：2021/04/26 Renewable energy  Complete empirical mode decomposition with adaptive noise  Quantile regression neural network  Wind speed forecasting  Distance correlation   A decomposition-ensemble approach for tourism forecasting 期刊论文  OAI收割 ANNALS OF TOURISM RESEARCH, 2020, 卷号: 81, 页码: 16 作者:  Xie, Gang;  Qian, Yatong;  Wang, Shouyang   |  收藏  |  浏览/下载：19/0  |  提交时间：2020/06/30 Tourism demand  Complete ensemble empirical mode decomposition with adaptive noise  Data characteristic analysis  Time series forecasting   Destriping method using lifting wavelet transform of remote sensing image (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  He B. 收藏  |  浏览/下载：30/0  |  提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images  a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm  the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.