中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页
机构
成果
学者
登录
注册
登陆
×
验证码:
换一张
忘记密码?
记住我
×
校外用户登录
CAS IR Grid
机构
长春光学精密机械与物... [2]
遥感与数字地球研究所 [2]
西安光学精密机械研究... [2]
南海海洋研究所 [1]
地理科学与资源研究所 [1]
昆明植物研究所 [1]
更多
采集方式
OAI收割 [10]
内容类型
期刊论文 [6]
会议论文 [4]
发表日期
2021 [2]
2020 [2]
2017 [1]
2016 [1]
2012 [1]
2010 [1]
更多
学科主题
筛选
浏览/检索结果:
共10条,第1-10条
帮助
条数/页:
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
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
Temporal and spatial variations in the terrestrial water storage across Central Asia based on multiple satellite datasets and global hydrological models
期刊论文
OAI收割
JOURNAL OF HYDROLOGY, 2021, 卷号: 596, 页码: 16
作者:
Hu, Zengyun
;
Zhang, Zizhan
;
Sang, Yan-Fang
;
Qian, Jing
;
Feng, Wei
  |  
收藏
  |  
浏览/下载:48/0
  |  
提交时间:2021/08/19
Terrestrial water storage
Global hydrological model
GRACE satellite dataset
Partial least square regression
Central Asia
The powdery mildew disease of rubber (Oidium heveae) is jointly controlled by the winter temperature and host phenology
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2021, 卷号: 65, 期号: 10, 页码: 1707-1718
作者:
Zhai,De-Li
;
Thaler,Philippe
;
Luo,Yiqi
;
Xu,Jianchu
  |  
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2022/04/02
Rubber plantation
Oidium heveae
Partial least square (PLS) regression
Winter warming
Phenology
Measurement of moisture content in lubricating oils of high-speed rail gearbox by Vis-NIR spectroscopy
期刊论文
OAI收割
Optik, 2020, 卷号: 224
作者:
Liu, Chenyang
;
Tang, Xingjia
;
Yu, Tao
;
Wang, Taisheng
;
Lu, Zhenwu
  |  
收藏
  |  
浏览/下载:83/0
  |  
提交时间:2020/11/03
Vis-NIR spectroscopy
Lubricating oils
Moisture content
The reflection probe
Successive projections algorithm
Partial least square regression
Back propagation neural network
Phytoplankton community, structure and succession delineated by partial least square regression in Daya Bay, South China Sea
期刊论文
OAI收割
ECOTOXICOLOGY, 2020, 卷号: 29, 期号: 6, 页码: 751, 761
作者:
Wu, Mei-Lin
;
Wang, Yu-Tu
;
Cheng, Hao
;
Sun, Fu-lin
;
Fei, Jiao
  |  
收藏
  |  
浏览/下载:31/0
  |  
提交时间:2020/09/22
Nutrients
Phytoplankton
Diatoms
Dinoflagellates
Southeast Asian Monsoon
Partial least square regression
Learning Instance Correlation Functions for Multilabel Classification
期刊论文
OAI收割
ieee transactions on cybernetics, 2017, 卷号: 47, 期号: 2, 页码: 499-510
作者:
Liu, Huawen
;
Li, Xuelong
;
Zhang, Shichao
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2017/04/06
l(1)-norm
instance-based learning
k-nearest neighbors (kNNs)
multilabel classification
partial least square (PLS) regression
Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula
期刊论文
OAI收割
CATENA, 2016, 卷号: 137, 页码: 340-349
作者:
Yu, X
;
Liu, Q
;
Wang, YB
;
Liu, XY
;
Liu, X
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2016/04/24
Multiple linear stepwise regression
Partial least square regression
Estimation
Soil element contents
Visible/near-infrared spectroscopy
An experimental study on paddy soil moisture inversion based on emissive hyperspectra
会议论文
OAI收割
2012 First International Conference on Agro-Geoinformatics, New York
Huang Qi-ting
;
Luo Jian-cheng
;
Shi Zhou
;
Li Jun-li
收藏
  |  
浏览/下载:30/0
  |  
提交时间:2014/12/07
component
Soil moisture
Emissive hyper-spectra
Partial least square
regression
ASTER
TEMPERATURE
Band Selection Method for Retrieving Soil Lead Content with Hyperspectral Remote Sensing Data
会议论文
OAI收割
Earth Resources and Environmental Remote Sensing-Gis Applications, Bellingham
Zhang, Xia
;
Wen, Jianting
;
Zhao, Dong
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2014/12/07
heavey metal inversion
partial least square regression
least square
regression
genetic algorithm
band selection
REFLECTANCE SPECTROSCOPY
RIVER FLOODPLAINS
REGRESSION
The study on the near infrared spectrum technology of sauce component analysis (EI CONFERENCE)
会议论文
OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者:
Li S.
;
Wang C.
;
Chen X.
;
Chen X.
;
Chen X.
收藏
  |  
浏览/下载:32/0
  |  
提交时间:2013/03/25
The author
Shangyu Li
engages in supervising and inspecting the quality of products. In soy sauce manufacturing
quality control of intermediate and final products by many components such as total nitrogen
saltless soluble solids
nitrogen of amino acids and total acid is demanded. Wet chemistry analytical methods need much labor and time for these analyses. In order to compensate for this problem
we used near infrared spectroscopy technology to measure the chemical-composition of soy sauce. In the course of the work
a certain amount of soy sauce was collected and was analyzed by wet chemistry analytical methods. The soy sauce was scanned by two kinds of the spectrometer
the Fourier Transform near infrared spectrometer (FT-NIR spectrometer) and the filter near infrared spectroscopy analyzer. The near infrared spectroscopy of soy sauce was calibrated with the components of wet chemistry methods by partial least squares regression and stepwise multiple linear regression. The contents of saltless soluble solids
total nitrogen
total acid and nitrogen of amino acids were predicted by cross validation. The results are compared with the wet chemistry analytical methods. The correlation coefficient and root-mean-square error of prediction (RMSEP) in the better prediction run were found to be 0.961 and 0.206 for total nitrogen
0.913 and 1.215 for saltless soluble solids
0.855 and 0.199 nitrogen of amino acids
0.966 and 0.231 for total acid
respectively. The results presented here demonstrate that the NIR spectroscopy technology is promising for fast and reliable determination of major components of soy sauce.
Fast determination of total ginsenosides content in Ginseng powder by near infrared reflectance spectroscopy (EI CONFERENCE)
会议论文
OAI收割
ICO20: Biomedical Optics, August 21, 2005 - August 26, 2005, Changchun, China
作者:
Chen X.-D.
;
Chen X.-D.
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2013/03/25
Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm1880 nm and 2230nm-2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR)
principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm-2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.