机构

• 长春光学精密机械与物... [9]
• 地理科学与资源研究所 [2]
• 烟台海岸带研究所 [2]
• 力学研究所 [1]
• 金属研究所 [1]
• 新疆生态与地理研究所 [1]
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采集方式

• OAI收割 [18]
• iSwitch采集 [1]

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• 会议论文 [9]
• 期刊论文 [8]
• SCI/SSCI论文 [1]
• 学位论文 [1]

发表日期

• 2023 [3]
• 2020 [3]
• 2016 [3]
• 2012 [3]
• 2011 [1]
• 2010 [2]
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学科主题

• Environmen... [1]
• 地图学与地理信息系统 [1]

筛选

浏览/检索结果: 共19条，第1-10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 A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures 期刊论文  OAI收割 SMART MATERIALS AND STRUCTURES, 2023, 卷号: 32, 期号: 7, 页码: 75008 作者:  Du WQ(杜文琪);  Yang LK(杨乐凯);  Lu LL(路玲玲);  Le J(乐杰);  Yu MK(于明凯)   |  收藏  |  浏览/下载：17/0  |  提交时间：2023/06/15 thermal load identification  physics-guided neural network  physics-guided loss function  thermal feature parameters  laser irradiation   基于微波散射实验的油种识别研究 期刊论文  OAI收割 海洋与湖沼, 2023, 卷号: 54, 期号: 1, 页码: 30-43 作者:  马靖;  过杰   |  收藏  |  浏览/下载：24/0  |  提交时间：2023/08/16 oil type identification  synthetic aperture radar (SAR)  C-band full-polarization scatterometer  sensitive feature parameters  油种识别  合成孔径雷达(SAR)  C波段全极化散射计  敏感特征参数   基于微波散射实验的油种识别研究 期刊论文  OAI收割 海洋与湖沼, 2023, 卷号: 54, 期号: 1, 页码: 30-43 作者:  马靖;  过杰   |  收藏  |  浏览/下载：6/0  |  提交时间：2024/10/28 oil type identification  synthetic aperture radar (SAR)  C-band full-polarization scatterometer  sensitive feature parameters  油种识别  合成孔径雷达(SAR)  C波段全极化散射计  敏感特征参数   天山区域典型灌区生长季土壤盐分含量时空差异分析 学位论文  OAI收割 北京: 中国科学院大学, 2020 作者:  徐红涛   |  收藏  |  浏览/下载：76/0  |  提交时间：2021/12/10 机器学习  支持向量回归  土壤盐渍化  建模变量和模型参数同步优选  时空变化  Machine learning  Support vector regression  Soil salinization  Simultaneous optimization of feature subset and model parameters  temporal and spatial changes   Automatic process parameters tuning and surface roughness estimation for laser cleaning 期刊论文  OAI收割 IEEE Access, 2020, 卷号: 8, 页码: 20904-20919 作者:  Liu, Haoting;  Li, Jiacheng;  Yang, Yong;  Lan, Jinhui;  Xue, Yafei   |  收藏  |  浏览/下载：38/0  |  提交时间：2020/03/12 Laser cleaning  process parameters  surface roughness  image feature  thermophysical property   Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image 期刊论文  OAI收割 GEOMATICS NATURAL HAZARDS & RISK, 2020, 卷号: 11, 期号: 1, 页码: 288-300 作者:  Guo, Bing;  Zang, Wenqian;  Luo, Wei;  Wen, Ye;  Yang, Fei   |  收藏  |  浏览/下载：86/0  |  提交时间：2020/05/19 Soil salinization  feature space  surface parameters  Landsat 8 OLI  Yellow River Delta   Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain SCI/SSCI论文  OAI收割 2016 作者:  Li Y.;  Zhu, A. X.;  Shi, Z.;  Liu, J.;  Du, F.   |  收藏  |  浏览/下载：30/0  |  提交时间：2017/11/09 Prediction uncertainty  Supplemental sampling  Feature domain  Spatial  domain  Digital soil mapping  optimal interpolation  constrained optimization  design  variables  classification  attributes  parameters  variogram  schemes   Improved bore-sight calibration for airborne light detection and ranging using planar patches 期刊论文  OAI收割 Journal of Applied Remote Sensing, 2016, 卷号: 10, 期号: 2 作者:  Li, Dong;  Guo, Huadong;  Wang, Cheng;  Dong, Pinliang;  Zuo, Zhengli 收藏  |  浏览/下载：28/0  |  提交时间：2017/04/24 SPECIES CLASSIFICATION  FEATURE PARAMETERS  LIDAR INTENSITY  CANOPY  FOREST  TERRESTRIAL   The consistency between na content distribution at the subsurface and the lake body's movement in lop nur 期刊论文  iSwitch采集 International journal of digital earth, 2016, 卷号: 9, 期号: 7, 页码: 662-675 作者:  Li, Bingyan;  Gong, Huaze;  Shao, Yun;  Li, Lin;  Wang, Longfei 收藏  |  浏览/下载：40/0  |  提交时间：2019/05/09 Lop nur  `ear' feature  Polsar  Polarimetric decomposition parameters  Ga-pls   Moving target detection and classification using spiking neural networks (EI CONFERENCE) 会议论文  OAI收割 2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011, October 23, 2011 - October 25, 2011, Xi'an, China 作者:  Sun H.;  Wang Z.;  Wang Z.;  Wang P.;  Sun H. 收藏  |  浏览/下载：41/0  |  提交时间：2013/03/25 We proposed a spiking neural network (SNN) to detect moving target in video streams and classify them into real categorization in this paper. The proposed SNN uses spike trains to encoding information such as the gray value of pixels or feature parameters of the target  detects moving target by simulating the visual cortex for motion detection in biological system with axonal delays and classify them into different categorizations according to their distance to categorization's centers found by Hebb learning rule. The experimental results show that the proposed SNN is promising in intelligence computation and applicable in general visual surveillance system. 2012 Springer-Verlag.