中国科学院机构知识库网格系统: 检索

Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes 期刊论文 OAI收割
ENTROPY, 2023, 卷号: 25, 期号: 10, 页码: 16
作者: Dai, Rui; Wang, Yibo; Zhang, Da; Ji, Hu

|

收藏 | 浏览/下载：24/0 | 提交时间：2023/12/27

microseisms P-phase onset time picking STA/LTA method AIC method skew and kurtosis method wavelet coefficient threshold denoising in time-frequency domain

Simultaneous Extraction of Planetary Boundary-Layer Height and Aerosol Optical Properties from Coherent Doppler Wind Lidar 期刊论文 OAI收割
SENSORS, 2022, 卷号: 22
作者: Chen, Yehui; Jin, Xiaomei; Weng, Ningquan; Zhu, Wenyue; Liu, Qing

|

收藏 | 浏览/下载：43/0 | 提交时间：2022/12/23

planetary boundary layer (PBL) aerosol extinction coefficient (AEC) aerosol optical depth (AOD) wavelet covariance transform (WCT) dilation operation

NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition 期刊论文 OAI收割
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 卷号: 70, 期号: 3, 页码: 2138-2152
作者: Qin, Yanjun; Luo, Haiyong; Zhao, Fang; Wang, Chenxing

|

收藏 | 浏览/下载：42/0 | 提交时间：2021/12/01

Transportation Smart phones Feature extraction Discrete wavelet transforms Convolution Acceleration Correlation coefficient Mobile sensing transportation mode NIN Dilate Convolution GCN

Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations 期刊论文 OAI收割
IEEE ACCESS, 2020, 卷号: 8, 页码: 167998-168007
作者: Zhang, Liying; Pei, Tao; Meng, Bin; Lian, Yuanfeng; Jin, Zhou

|

收藏 | 浏览/下载：28/0 | 提交时间：2021/03/16

Time series analysis Correlation Shape Correlation coefficient Discrete wavelet transforms Clustering algorithms Multivariate time series cluster maximum overlap discrete wavelet transform symbolic aggregate approximation (SAX) urban rail transit stations

Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations 期刊论文 OAI收割
IEEE ACCESS, 2020, 卷号: 8, 页码: 167998-168007
作者: Zhang, Liying; Pei, Tao; Meng, Bin; Lian, Yuanfeng; Jin, Zhou

|

收藏 | 浏览/下载：19/0 | 提交时间：2021/03/16

Time series analysis Correlation Shape Correlation coefficient Discrete wavelet transforms Clustering algorithms Multivariate time series cluster maximum overlap discrete wavelet transform symbolic aggregate approximation (SAX) urban rail transit stations

The influence of denoising on the subgrade settlement fitting curve 会议论文 OAI收割
Guangzhou, PEOPLES R CHINA, JAN 30-31, 2016
作者: Dai, Zhangjun; Chen, Shanxiong

|

收藏 | 浏览/下载：27/0 | 提交时间：2018/06/05

wavelet denoising subgrade settlement correlation coefficient

Wavelet transformed Gaussian network model 期刊论文 OAI收割
JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY, 2014, 卷号: 13, 期号: 6
作者: Zhan, Meng; Li, Suhong; Li, Fan

收藏 | 浏览/下载：20/0 | 提交时间：2015/06/24

Protein modeling Gaussian network model wavelet transform B-factors correlation coefficient

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：77/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility 期刊论文 OAI收割
JOURNAL OF ECONOMETRICS, 2008, 卷号: 143, 期号: 2, 页码: 227-262
作者: Chen, Gongmeng; Choi, Yoon K.; Zhou, Yong

|

收藏 | 浏览/下载：17/0 | 提交时间：2018/07/30

nonparametric regression wavelet coefficient change points kernel estimation local polynomial smoother conditional heteroscedastic variance alpha-mixing

Research of multiple elimination method in inverse wavelet domain 期刊论文 OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2008, 卷号: 51, 期号: 1, 页码: 250-259
作者: Jin De-Gang; Chang Xu; Liu Yi-Ke

|

收藏 | 浏览/下载：14/0 | 提交时间：2018/09/26

SRMA inverse wavelet domain Gauss distribution correlation coefficient orthogonality