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浏览/检索结果: 共16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 Depression Identification from Gait Spectrum Features Based on Hilbert-Huang Transform 会议论文  OAI收割 Merida, Mexico, December 5, 2018 - December 7, 2018 作者:  Yuan, YaHui;  Li, Baobin;  Wang, Ning;  Ye, Qing;  Liu, Yan   |  收藏  |  浏览/下载：62/0  |  提交时间：2019/11/08 Classification models - Depression - Frequency domains - Frequency features - Gait - Hilbert Huang transforms - Kinect - Verification method   Timing analysis of Swift J1658.2-4242's outburst in 2018 with Insight-HXMT, NICER and AstroSat 期刊论文  OAI收割 Journal of High Energy Astrophysics, 2019, 卷号: 24, 页码: 30-40 作者:  HXMT   |  收藏  |  浏览/下载：57/0  |  提交时间：2022/02/08 Accretion  accretion disks  Black hole physics  X-rays: binaries:  Swift J1658.2-4242  Astrophysics - High Energy Astrophysical Phenomena  Abstract: We present the observational results from a detailed timing analysis of the black hole candidate Swift J1658.2-4242 during its 2018 outburst with the observations of Hard X-ray Modulation Telescope (Insight-HXMT), Neutron Star Interior Composition Explorer (NICER) and AstroSat in 0.1-250 keV. The evolution of intensity, hardness and integrated fractional root mean square (rms) observed by Insight-HXMT and NICER are presented in this paper. Type-C quasi-periodic oscillations (QPOs) observed by NICER (0.8-3.5 Hz) and Insight-HXMT (1-1.6 Hz) are also reported in this work. The features of the QPOs are analyzed with an energy range of 0.5-50 keV. The relations between QPO frequency and other characteristics such as intensity, hardness and QPO rms are carefully studied. The timing and spectral properties indicate that Swift J1658.2-4242 is a black hole binary system. Besides, the rms spectra of the source calculated from the simultaneous observation of Insight-HXMT, NICER and AstroSat support the Lense-Thirring origin of the QPOs. The relation between QPO phase lag and the centroid frequency of Swift J1658.2-4242 reveals a near zero constant when < 4Hz and a soft phase lag at 6.68 Hz. This independence follows the same trend as the high inclination galactic black hole binaries such as MAXI J1659-152.   Lg-wave attenuation in the Australian crust 期刊论文  OAI收割 TECTONOPHYSICS, 2017, 卷号: 717, 页码: 413-424 作者:  Wei, Zhi;  Kennett, Brian L. N.;  Zhao, Lian-Feng   |  收藏  |  浏览/下载：18/0  |  提交时间：2018/09/26 Australian Continent  Lg Attenuation Model  Geological Features  Frequency-dependent q Of Lg Waves  Ground Motion   Lg-wave attenuation in the Australian crust 期刊论文  OAI收割 TECTONOPHYSICS, 2017, 卷号: 717, 页码: 413-424 作者:  Wei, Zhi;  Kennett, Brian L. N.;  Zhao, Lian-Feng   |  收藏  |  浏览/下载：29/0  |  提交时间：2018/09/26 Australian Continent  Lg Attenuation Model  Geological Features  Frequency-dependent q Of Lg Waves  Ground Motion   Application of a sea surface temperature front composite algorithm in the Bohai, Yellow, and East China Seas SCI/SSCI论文  OAI收割 2016 作者:  Ping B.;  Su, F. Z.;  Meng, Y. S.;  Du, Y. Y.;  Fang, S. H.   |  收藏  |  浏览/下载：26/0  |  提交时间：2017/11/09 oceanic fronts  Sobel algorithm  frontal frequency  frontal average  gradient  edge-detection  sst images  oceanic fronts  satellite  wintertime  features   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：78/0  |  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.   The Comparative Numerical Experiments of Frequency Features for Earthquake Responds in Liquefiable Field and Elastic Field 会议论文  OAI收割 Jinan, PEOPLES R CHINA, OCT 14-16, 2011 作者:  He, Jianping;  Chen, Weizhong   |  收藏  |  浏览/下载：28/0  |  提交时间：2018/06/05 Liquefaction field  Frequency features  Excess static pore pressure ratio  Acceleration enlargement   Analysis of the effects of slope geometry on the dynamic response of a near-field mountain from the Wenchuan Earthquake 期刊论文  OAI收割 JOURNAL OF MOUNTAIN SCIENCE, 2010, 卷号: 7, 期号: 4, 页码: 353–360 Xiao Shiguo; Feng Wenkai; Zhang Jianjing 收藏  |  浏览/下载：52/0  |  提交时间：2013/08/12 Wenchuan Earthquake  Slope  Geometric features  Dynamic response  Natural frequency   The research on the automatic measurement of frequency characteristics of opto-electronic platform (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  Liu H.;  Liu H.;  Chen J.;  Liu H. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 A new frequency characteristic test method is proposed based on the digitization characteristics of optoelectronic platform servo system. First a digital processing controller is used to automatically test the entire process and tested data are uploaded to a PC through CAN-bus  and then the tested data are transferred into accurate transfer function with the Levy method. Here we discuss the testing steps  the data processing  the extraction  the Bode diagram plotting and the model identification in detail. The corresponding experiments are carried out  and the results show that the method is simple with the features of high efficiency  high precision and engineering feasibility. 2010 IEEE.   Detection of low contrast targets based on lifting scheme wavelet transform (EI CONFERENCE) 会议论文  OAI收割 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, August 9, 2009 - August 12, 2009, Changchun, China 作者:  Chen X.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：16/0  |  提交时间：2013/03/25 This paper present a fast algorithm for detection of low contrast objects by using wavelet filters based on lifting scheme. The advantage is robust to noise. According Swelden's  lifting wavelet filters are biorthogonal wavelet filters containing free parameters. We use reference image of targets to train the lifting terms  so that the learnt wavelet filters have the features of targets. Then applying such filters to the images including targets taken from camera system. We can detect the locations where the high frequency components are almost the same as those of the target image. 2009 IEEE.