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	Massive Data Management and Sharing Module for Connectome Reconstruction 期刊论文 OAI收割 BRAIN SCIENCES, 2020, 卷号: 10, 期号: 5, 页码: 15 作者: Yuan, Jingbin; Zhang, Jing; Shen, Lijun; Zhang, Dandan; Yu, Wenhuan \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2020/08/03 connectome massive data management distributed storage and retrieval electron microscope image segmentation result image cache
	A New Set of Parameters of High-Mass X-ray Binaries Found with their Cyclotron Lines 期刊论文 OAI收割 arXiv e-prints, 2018 作者: Zhang, Chengmin \| 收藏 \| 浏览/下载：76/0 \| 提交时间：2019/07/12 Astrophysics - High Energy Astrophysical Phenomena Abstract: We have derived new physical quantities for several High-Mass X-ray Binaries (HMXBs) with supergiant (SG) companions through their cyclotron lines. The parameters are: the terminal velocity of the wind, the mass loss rate of the donor, the effective temperature and the magnetic fields. These parameters influence significantly the improvement of the model of accretion. In spite of the variety of their observational properties, the corresponding magnetic field is around B ~ 10^12 G. This result can be constrained by the effects on stellar evolution. In addition, we have performed a segmentation in the parameter space of donors intended for several SG-HMXB listed in our sample set. The parameter space can be categorized into five regimes depending on the possibility of disk formation associated with accretion from the stellar wind. This can give a quantitative clarification of the observed variability and the properties of these objects. We show that, when these systems come into the direct accretion region, systems with corresponding parameters can emit X-rays.
	An edge extracting method of fuzzy thresholding value (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Gu R.; Zhu M. 收藏 \| 浏览/下载：15/0 \| 提交时间：2013/03/25 This paper discusses image's object edge extracting of threshold value. First this paper introduces fundamental principle of gray image object edge extracting after discussing the influence of object edge image's gray scale distributing characteristic and choice of threshold value to character extracting and target identification we bring forward a sort of method which using object edge fuzzy taxonomy and maximum fuzzy entropy theory to auto choose threshold value in the case that membership function is given then we have a test of threshold value segmentation to edge gray image in different contrast the result indicates this method has preferable scene adaptive faculty.
	A new segmentation method of CR images based on discrete wavelet transform and mathematics morphology (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Z.; Li Z. 收藏 \| 浏览/下载：68/0 \| 提交时间：2013/03/25 In this paper we propose a segmentation method of CR(computed radiography) images with being rid of under-segmentation and over-segmentation. An under-segmentation occurs when pixels belonging to different objects are grouped into a single region. Such errors are the most dangerous because they can invalidate the whole segmentation process. The phenomenon always takes place when we segment CR images because of the principle of CR. In order to depressed under-segmentation we enhance the CR images using DWT (discrete wavelet transform) to get more detail of CR image features. As we enhance the CR image the over-segmentation maybe occurs. Compared with under-segmentation the over-segmentation occurs when a single objects is subdivided by segmentation into several region. For the purpose of preventing from the over-segmentation we present a scheme for enhanced CR images based on watershed algorithm that solves over-segmentation problem. We use marker-based watershed algorithm. Together with gradient image and marker image watershed segmentation can make sure to partition CR image into meaningful object and avoid further segmentation of homogeneous regions. The result of the proposed algorithm are compared with those of other standard methods. Experiments have shown a better result and indeed solved under-segmentation and over-segmentation problems.