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长春光学精密机械与物... [1]
数学与系统科学研究院 [1]
遥感与数字地球研究所 [1]
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OAI收割 [6]
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会议论文 [3]
期刊论文 [3]
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2024 [1]
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2011 [1]
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Determination of the stress tensor of a triaxial strain cell in a three-layer model using the genetic algorithm and support vector machine
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 2024, 卷号: 175, 页码: 16
作者:
Zheng, Minzong
;
Li, Shaojun
;
Feng, Zejie
;
Liu, Liu
;
Jia, Wei
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2025/06/27
Stress measurement
Three -layer model
Hollow inclusion cell
Genetic algorithm
Support vector machine
A Minimax Probability Machine for Nondecomposable Performance Measures
期刊论文
OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:
Luo, Junru
;
Qiao, Hong
;
Zhang, Bo
  |  
收藏
  |  
浏览/下载:33/0
  |  
提交时间:2022/01/27
Measurement
Task analysis
Covariance matrices
Support vector machines
Prediction algorithms
Minimization
Kernel
Imbalanced classification
minimax probability machine
nondecomposable performance measures
A Minimax Probability Machine for Nondecomposable Performance Measures
期刊论文
OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:
Luo, Junru
;
Qiao, Hong
;
Zhang, Bo
  |  
收藏
  |  
浏览/下载:45/0
  |  
提交时间:2022/04/02
Measurement
Task analysis
Covariance matrices
Support vector machines
Prediction algorithms
Minimization
Kernel
Imbalanced classification
minimax probability machine
nondecomposable performance measures
A comparative study of city environment in Tianjin Area, China and the greater Toronto Area, Canada based on multi-factors of the urban ecosystem
会议论文
OAI收割
American Society for Photogrammetry and Remote Sensing Annual Conference 2011, ASPRS 2011,, Milwaukee, WI, United states, May 1, 2011 - May 5,2011
Huang, Qingni
;
Guo, Huadong
;
Li, Xinwu
;
Sun, Zhongchang
;
Ding, Yixing
收藏
  |  
浏览/下载:27/0
  |  
提交时间:2014/12/07
Remote sensing
Developing countries
Ecosystems
Photogrammetry
Support vector machines
Surface measurement
Urban planning
Multiwavelet based multispectral image fusion for corona detection (EI CONFERENCE)
会议论文
OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者:
Wang X.
;
Yang H.-J.
;
Sui Y.-X.
;
Yan F.
;
Yan F.
收藏
  |  
浏览/下载:34/0
  |  
提交时间:2013/03/25
Image fusion refers to the integration of complementary information provided by various sensors such that the new images are more useful for human or machine perception. Multiwavelet transform has simultaneous orthogonality
symmetry
compact support
and vanishing moment
which are not possible with scalar wavelet transform. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. In this paper
a new image fusion algorithm based on discrete multiwavelet transform (DMWT) to fuse the dual-spectral images generated from the corona detection system is presented. The dual-spectrum detection system is used to detect the corona and indicate its exact location. The system combines a solar-blind UV ICCD with a visible camera
where the UV image is useful for detecting UV emission from corona and the visible image shows the position of the corona. The developed fusion algorithm is proposed considering the feature of the UV and visible images adequately. The source images are performed at the pixel level. First
a decomposition step is taken with the DMWT. After the decomposition step
a pyramid for each source image in each level can be obtained. Then
an optimized coefficient fusion rule consisting of activity level measurement
coefficient combining and consistency verification is used to acquire the fused coefficients. This process reduces the impulse noise of UV image. Finally
a new fused image is obtained by reconstructing the fused coefficients using inverse DMWT. This image fusion algorithm has been applied to process the multispectral UV/visible images. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach.
mining ratio rules via principal sparse non-negative matrix factorization
会议论文
OAI收割
4th IEEE International Conference on Data Mining, Brighton, ENGLAND, NOV 01-04,
Hu CY
;
Zhang BY
;
Yan SC
;
Yang Q
;
Yan J
;
Chen Z
;
Ma WY
  |  
收藏
  |  
浏览/下载:46/0
  |  
提交时间:2011/07/29
association rules
principal sparse nonnegative matrix factorization
principle component analysis
quantifiable data mining
quantitative association knowledge
ratio rules mining
support measurement
data mining
matrix decomposition
principal compone