机构

• 金属研究所 [2]
• 长春光学精密机械与物... [1]
• 软件研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 会议论文 [2]
• 期刊论文 [2]

发表日期

• 2022 [1]
• 2014 [1]
• 2012 [1]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4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 Crystal plasticity model of induction heating-assisted incremental sheet forming with recrystallisation simulation in cellular automata 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 页码: 23 作者:  Li, Weining;  Li, Sheng;  Li, Xuexiong;  Xu, Dongsheng;  Shao, Yinghui   |  收藏  |  浏览/下载：22/0  |  提交时间：2023/05/09 Ti-6Al-4 V thin sheet  Incremental sheet forming  Multi-scale modelling  Crystal plasticity  Cellular automata  Grain size  Crystallographic texture   Efficient texture synthesis of aggregate solid material 会议论文  OAI收割 Shu, Yue (1); Qian, Yinling (3); Sun, Hanqiu (3); Chen, Yanyun (1)   |  收藏  |  浏览/下载：27/0  |  提交时间：2014/12/16 Aggregate material  Solid texture  Cellular texture  Stereology   Progress in mesoscopic modeling of microstructure evolution in steels 期刊论文  OAI收割 Science China-Technological Sciences, 2012, 卷号: 55, 期号: 2, 页码: 341-356 N. M. Xiao; Y. Chen; D. Z. Li; Y. Y. Li 收藏  |  浏览/下载：33/0  |  提交时间：2013/02/05 microstructure evolution  steel  phase field  cellular automaton  Monte  Carlo  phase-field model  low-carbon steel  austenite-ferrite transformation  cellular-automaton method  monte-carlo-simulation  fe-c alloys  goss  texture development  state wetting analysis  abnormal grain-growth  fe-3-percent si steel   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：25/0  |  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.