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Multiscale mechanical properties of shales: grid nanoindentation and statistical analytics 期刊论文  OAI收割
ACTA GEOTECHNICA, 2021, 期号: -, 页码: 16
作者:  
Du, Jianting;  Luo, Shengmin;  Hu, Liming;  Guo, Brandon;  Guo, Dongdong
  |  收藏  |  浏览/下载:91/0  |  提交时间:2021/09/01
GRB 200415A: A Short Gamma-Ray Burst from a Magnetar Giant Flare? 期刊论文  OAI收割
The Astrophysical Journal, 2020, 卷号: 899, 页码: 106
作者:  
HXMT
  |  收藏  |  浏览/下载:41/0  |  提交时间:2022/02/08
Gamma-ray bursts  Soft gamma-ray repeaters  Magnetars  Gamma-ray  transient sources  629  1441  992  1853  Astrophysics - High Energy  Astrophysical Phenomena  Abstract: The giant flares of soft gamma-ray repeaters (SGRs) have long been proposed to contribute to at least a subsample of the observed short gamma-ray bursts (GRBs). In this paper, we perform a comprehensive analysis of the high-energy data of the recent bright short GRB 200415A, which was located close to the Sculptor galaxy. Our results suggest that a magnetar giant flare provides the most natural explanation for most observational properties of GRB 200415A, including its location, temporal and spectral features, energy, statistical correlations, and high-energy emissions. On the other hand, the compact star merger GRB model is found to have difficulty reproducing such an event in a nearby distance. Future detections and follow-up observations of similar events are essential to firmly establish the connection between SGR giant flares and a subsample of nearby short GRBs.  
Status and perspectives of the CRAFTS extra-galactic HI survey 期刊论文  OAI收割
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY, 2019, 卷号: 62, 期号: 5, 页码: 9
作者:  
Zhang, Kai;  Wu, JingWen;  Li, Di;  Krco, Marko;  Staveley-Smith, Lister
  |  收藏  |  浏览/下载:29/0  |  提交时间:2020/03/10
Surface properties of neutron-rich exotic nuclei within relativistic mean field formalisms 期刊论文  OAI收割
PHYSICAL REVIEW C, 2018, 卷号: 97, 期号: 2, 页码: 24322
作者:  
Patra, SK;  Bhuyan, M;  Carlson, BV;  Zhou, SG
  |  收藏  |  浏览/下载:51/0  |  提交时间:2018/12/27
On random nonsingular Hermite Normal Form 期刊论文  OAI收割
JOURNAL OF NUMBER THEORY, 2016, 卷号: 164, 页码: 66-86
作者:  
Hu, Gengran;  Pan, Yanbin;  Liu, Renzhang;  Chen, Yuyun
  |  收藏  |  浏览/下载:26/0  |  提交时间:2018/07/30
On the systematic bias in the estimation of black hole masses in active galactic nuclei 期刊论文  OAI收割
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY, 2014, 卷号: 57, 期号: 3, 页码: 584-588
作者:  
Wang JG(王建国);  Dong XB
收藏  |  浏览/下载:45/0  |  提交时间:2016/04/08
Ion Solvation in Polymer Blends and Block Copolymer Melts: Effects of Chain Length and Connectivity on the Reorganization of Dipoles 期刊论文  OAI收割
journal of physical chemistry b, 2014, 卷号: 118, 期号: 21, 页码: 5787-5796
Nakamura, Issei
收藏  |  浏览/下载:35/0  |  提交时间:2015/10/20
Stochastic and dynamic modeling of MEMS gyroscopes (EI CONFERENCE) 会议论文  OAI收割
2012 9th IEEE International Conference on Mechatronics and Automation, ICMA 2012, August 5, 2012 - August 8, 2012, Chengdu, China
作者:  
Wang Y.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y.
收藏  |  浏览/下载:32/0  |  提交时间:2013/03/25
TextureGrow: Object recognition and segmentation with limit prior knowledge (EI CONFERENCE) 会议论文  OAI收割
2011 International Conference on Network Computing and Information Security, NCIS 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China
Yao Z.; Han Q.
收藏  |  浏览/下载:30/0  |  提交时间:2013/03/25
In this paper we present a new method for automatically visual recognition and semantic segmentation of photographs. Our automatically and rapidly approach based on Cellular Automation. Most of the analysis and description of recognition and segmentation are based on statistical or structural properties of this attribute  most of them need plenty of samples and prior Knowledge. In this paper  within a few evident samples (not too many)  we can first get the texture feature of each component and the structures  then select the approximately location randomly of the objects or patches of them  then we use the Cellular Automata algorithm to "grow" based on texture of different objects. The grow progress will stop When texture grow to the boundary. By this steps a new method is found which allow us use very few samples and low lever experience and get a rapidly and accuracy way to recognize and segment objects. We found that this new propose gives competitive results with limited experience and samples. 2011 IEEE.  
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