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	High-temperature deformation behavior and processing maps of a novel AlNbTi3VZr1.5 refractory high entropy alloy 期刊论文 OAI收割 JOURNAL OF ALLOYS AND COMPOUNDS, 2022, 卷号: 912, 页码: 11 作者: Yu, Zhenxiang; Xie, Bijun; Zhu, Zhengwang; Xu, Bin; Sun, Mingyue \| 收藏 \| 浏览/下载：45/0 \| 提交时间：2022/07/14 Refractory high entropy alloy High-temperature deformation Stress-strain analysis Dynamic recrystallization Hot processing maps Weight
	Abnormal deformation behavior and particle distribution during hot compression of fine-grained 14 vol% SiCp/2014Al composite 期刊论文 OAI收割 JOURNAL OF ALLOYS AND COMPOUNDS, 2018, 卷号: 743, 页码: 87-98 作者: Huang, ZY; Zhang, XX; Yang, C; Xiao, BL; Ma, ZY \| 收藏 \| 浏览/下载：48/0 \| 提交时间：2018/06/05 Fatigue Crack-growth Mechanical-properties Microstructure Evolution Matrix Microstructure Processing Maps Aluminum-alloy Yield Stress Al Composite Creep Temperature
	Hot deformation characteristics and hot working window of as-cast large-tonnage GH3535 superalloy ingot 期刊论文 OAI收割 JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY, 2018, 卷号: 34, 期号: 12, 页码: 2439-2446 作者: Wang, Y; Wang, JS; Dong, JS; Li, AM; Li, ZJ \| 收藏 \| 浏览/下载：44/0 \| 提交时间：2019/12/17 NI-BASE SUPERALLOY PROCESSING MAPS DYNAMIC RECRYSTALLIZATION BEHAVIOR MICROSTRUCTURE COMPRESSION ALLOY
	Study of the processing map and hot deformation behavior of a Cu-bearing 317LN austenitic stainless steel 期刊论文 OAI收割 MATERIALS & DESIGN, 2015, 卷号: 87, 页码: 303-312 Xi, Tong; Yang, Chunguang; Shahzad, M. Babar; Yang, Ke 收藏 \| 浏览/下载：17/0 \| 提交时间：2016/04/21 Cu-bearing stainless steel Hot compression Processing maps Constitutive equation Dynamic recystallization
	Perception-motivated multiresolution rendering on sole-cube maps 期刊论文 OAI收割 MULTIMEDIA TOOLS AND APPLICATIONS, 2014, 卷号: 72, 期号: 1, 页码: 231-252 作者: Sheng, Bin; Meng, Weiliang; Sun, Hanqiu; Wu, Wen; Wu, Enhua 收藏 \| 浏览/下载：43/0 \| 提交时间：2015/08/12 Multiresolution rendering Sole-cube maps GPU processing Mesh refinement
	Hot deformation map and its application of GH4706 alloy 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF MINERALS METALLURGY AND MATERIALS, 2014, 卷号: 21, 期号: 5, 页码: 462-468 作者: Huang Shuo; Wang Lei; Lian Xintong; Zhao Guangpu; Li Fangfei \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2021/02/02 DYNAMIC RECRYSTALLIZATION MICROSTRUCTURAL CONTROL WORKING CHARACTERISTICS PROCESSING MAPS SUPERALLOY BEHAVIOR superalloys nickel alloys deformation microstructure dynamic recrystallization
	Hot deformation map and its application of GH4706 alloy 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF MINERALS METALLURGY AND MATERIALS, 2014, 卷号: 21, 期号: 5, 页码: 462-468 作者: Huang Shuo; Wang Lei; Lian Xintong; Zhao Guangpu; Li Fangfei \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2021/02/02 DYNAMIC RECRYSTALLIZATION MICROSTRUCTURAL CONTROL WORKING CHARACTERISTICS PROCESSING MAPS SUPERALLOY BEHAVIOR superalloys nickel alloys deformation microstructure dynamic recrystallization
	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：72/0 \| 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.
	Constitutive flow behavior and hot workability of powder metallurgy processed 20 vol.%SiCP/2024Al composite 期刊论文 OAI收割 MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2010, 卷号: 527, 期号: 29-30, 页码: 7865-7872 作者: Shao, J. C.; Xiao, B. L.; Wang, Q. Z.; Ma, Z. Y.; Liu, Y. \| 收藏 \| 浏览/下载：59/0 \| 提交时间：2021/02/02 Metal matrix composites Constitutive equations Processing maps Aluminum alloys Powder metallurgy