中国科学院机构知识库网格系统: 检索

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	A new multi-scalar framework for quantifying the impacts of climate change and human activities on streamflow variation 期刊论文 OAI收割 WATER SUPPLY, 2022, 页码: 18 作者: Ma, Mingwei; Wang, Zhaohang; Cui, Huijuan; Wang, Wenchuan; Jiang, Liuyuwei \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2023/01/30 attribution analysis budyko-based decomposition method monthly ABCD model multi-scalar framework streamflow
	A new multi-scalar framework for quantifying the impacts of climate change and human activities on streamflow variation 期刊论文 OAI收割 WATER SUPPLY, 2022, 页码: 18 作者: Ma, Mingwei; Wang, Zhaohang; Cui, Huijuan; Wang, Wenchuan; Jiang, Liuyuwei \| 收藏 \| 浏览/下载：52/0 \| 提交时间：2023/01/30 attribution analysis budyko-based decomposition method monthly ABCD model multi-scalar framework streamflow
	Model-based target decomposition with the pi/4 mode compact polarimetry data 期刊论文 iSwitch采集 Science china-information sciences, 2016, 卷号: 59, 期号: 6, 页码: 10 作者: Guo, Shenglong; Li, Yang; Hong, Wen; Wang, Jianfeng; Guo, Xiaoyang 收藏 \| 浏览/下载：34/0 \| 提交时间：2019/05/09 Pi/4 mode compact polarimetry Mode-based target decomposition Stokes vector Rvog model Radar polarimetry
	An Extension of a Complete Model-Based Decomposition of Polarimetric SAR Data 期刊论文 OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 卷号: 13, 期号: 2, 页码: 287-291 作者: Zhu, Feiya; Zhang, Yunhua; Li, Dong 收藏 \| 浏览/下载：37/0 \| 提交时间：2017/01/18 Helix angle (HA) variation model-based decomposition nonnegative eigenvalue decomposition (NNED) orientation angle (OA) variation polarimetric synthetic aperture radar (PolSAR)
	A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting 期刊论文 OAI收割 ANNALS OF OPERATIONS RESEARCH, 2015, 卷号: 234, 期号: 1, 页码: 111-132 作者: Tang, Ling; Wang, Shuai; He, Kaijian; Wang, Shouyang \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2018/07/30 Decomposition ensemble model Data-characteristic-based modeling Nuclear energy consumption forecasting Time series analysis Intelligent knowledge management
	Image quality assessment based on gradient complex matrix (EI CONFERENCE) 会议论文 OAI收割 2012 International Conference on Systems and Informatics, ICSAI 2012, May 19, 2012 - May 20, 2012, Yantai, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 An image quality assessment model based on gradient complex matrix is proposed. The vertical and horizontal gradient information of grayscale image is calculated. Complex number is used to construct the measuring matrix. Singular value decomposition is performed in order to obtain the main structure information of the image. The singular value feature vectors of the image gradient complex matrices corresponding to the reference image and the distorted image are used to measure the structural similarity of the two images. PSNR is taken as a tool to evaluate the gradient distribution similarity. Their properties are analyzed by using LIVE database and nonlinearity regression function. 2012 IEEE.
	Study of the neural network constitutive models for turfy soil with different decomposition degree (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Mechanic Automation and Control Engineering, MACE 2011, July 15, 2011 - July 17, 2011, Inner Mongolia, China 作者: Nie L. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 The turfy soil is of a special humus soil. The decomposition degree is the main factor on the physical and mechanical properties of turfy soil. To build the turfy soil constitutive model there are a few shortages such as the calculation cumbersome and low accuracy for parameter value with the method of traditional models. Furthermore those methods did not reflect the influence of strength that effected by decomposition degree of the turfy soil. In this paper the relationship of stress-strain with different decomposition degrees of turfy soil was carried out through indoor tests. Based on above experimental results an improved method which divided into different zones according to different decomposition degrees of turfy soil and calculated combining with neural network constitutive model is put forward. The result shows that the neural network of turfy soil has good fitting precision and good generalization ability. It can fully describe the influence of the turfy soil. 2011 IEEE.