oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 31 )

2018 ( 534 )

2017 ( 525 )

2016 ( 575 )

Custom range...

Search Results: 1 - 10 of 25289 matches for " 熊邦书 "
All listed articles are free for downloading (OA Articles)
Page 1 /25289
Display every page Item
从表面重构的体数据实现3维网格剖分
刘君,陈伟强,
中国图象图形学报 , 2014, DOI: 10.11834/jig.20140516
Abstract: 目的针对有限元分析中网格最优化问题,提出一种改进的生成四面体网格的自组织算法。方法该算法首先应用几何方法将三角形表面模型重新构造成规定大小的分类体数据,同时由该表面模型建立平衡八叉树,计算用以控制网格尺寸的3维数组;然后将体数据转换成邻域内不同等值面的形态一致的边界指示数组;结合改进的自组织算法和相关3维数据的插值函数,达到生成四面体网格的目的。结果实验结果对比表明,该方法能够生成更高比例的优质四面体,增强了对扁平面体的抑制能力,同时很好地保证了边界的一致。结论在对封闭的3维表面网格进行有限元建模时,本文算法为其提供了一种有效、可靠的途径。
周期能量与优化LMD结合的轴承故障诊断方法
Fault Diagnosis Method of the Rolling Bearing Combining Period-Energy Feature with LMD Feature of Optimization

,,,李新民,,
- , 2016, DOI: 10.16450/j.cnki.issn.1004-6801.2016.02.027
Abstract: 为了提高轴承故障诊断准确率,缩短神经网络训练时间,将周期能量特征和优化的局域均值分解(local mean decomposition,简称LMD)特征结合,提出了一种新的轴承故障诊断方法。首先,采用形态滤波法对振动信号去噪;其次,以轴承一个旋转周期采样点数为标准,对振动信号进行截取,提取周期能量特征和LMD特征;然后,对提取的特征进行u律压扩和滑动平均优化处理;最后,设计两个同精度神经网络,采用经优化和未优化的特征对设计好的RBF神经网络进行训练,用训练好的神经网络进行故障诊断。实验结果表明,神经网络收敛迭代次数减少了50次,诊断正确率提高了10%,提高了轴承故障诊断正确率,缩短了神经网络训练时间。
In order to improve the accuracy of fault diagnosis and shorten the training time of the neural network, a new method of bearing fault diagnosis is presented that combines the period-energy feature with the LMD optimization feature. First, morphological filtering was used to remove noise from signals. Second, as the standard by one rotation period sampling points, all kinds of fault signals were intercepted. The period-energy feature and LMD feature were extracted from the intercepted signal. Third, the features were processed by u-law compression expansion and moving-average processing. Finally, two Rolling bearing fault (RBFs) were designed with the same precision. The first RBF was trained with the period-energy feature and LMD feature, and the second was trained with period-energy of optimization and LMD feature of optimization. Then, the bearing fault was diagnosed with the well-trained neural network. The experimental results showed that the diagnostic accuracy improved by 10%, and the convergence iteration times of the training neural network were reduced by 50%, thus indicating improved diagnostic accuracy of bearing fault diagnosis and shortened convergence training time of the neural network.
基于CUDA的蛋白质点检测快速实现方法
Fast Implementation Method of Protein Spots Detection Based on CUDA

,叶毅嘉,欧巧凤,张郝东
- , 2016, DOI: 10.7507/1001-5515.20160016
Abstract: 为提高蛋白质点检测的效率,利用图像处理单元(GPU)在并行计算和内存管理方面的优势,提出一种基于CUDA的蛋白质点检测快速实现方法。首先,对蛋白质点检测算法中最耗时的图像预处理、蛋白质点粗检测和重叠蛋白质点分割三部分进行并行化设计;然后,根据CUDA单指令多线程的执行方式对数据空间进行二维分块,利用共享寄存器和二维纹理内存的内存管理措施实现了蛋白质点快速检测。通过本文方法与中央处理器(CPU)串行方法进行真实凝胶图像的检测对比实验,结果表明,本文方法的执行效率明显高于CPU串行方法,并且随着图像大小的增加,效率也随之提高,对于2 048×2 048大小的图像数据,CPU串行实现时间为52 641 ms,GPU则为4 384 ms,效率提高了11倍。
In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2 048×2 048, the method of CPU needs 5 2641 ms, but the GPU needs only 4 384 ms.
Image Definition Evaluation Method Based on Wavelet Scale Correlation
基于小波尺度相关的图像清晰度判别方法①

WANG Min-Jun,XIONG Bang-Shu,HUANG Li-Zhen,YU Liang,
王敏君
,,黄丽贞,余亮

计算机系统应用 , 2010,
Abstract: A novel definition measurement based on correlation of the wavelet transform at adjacent scales is presented, which improves the sharpness function`s performance of unimodality and noise-immunity. It takes advantage of the property that wavelet coefficients magnitude of signals increase as scale increases and wavelet coefficients magnitude of noise decrease with increasing scale and calculates the multiplication of wavelet coefficients at the adjacent scales to construct the evaluation method. It is capable to amplify signals and suppress noise. Compared with traditional algorithms using Image Data acquired by CCD, the proposed method is found to be more robust and accurate to estimate the focusing extent.
松滋县发展生态农业的调查与思考

生态学杂志 , 1996,
Abstract: ?
微卫星标记在鱼类遗传育种研究及种质资源管理上的应用
张小谷,,
农业生物技术学报 , 2006,
Abstract:
我国鲂属鱼类的研究进展
徐薇,
水生态学杂志 , 2008,
Abstract:
转录因子snail与肿瘤关系的研究进展
,
癌变·畸变·突变 , 2012,
Abstract: ?转录因子snail在个体发育、器官纤维化和肿瘤发生发展中发挥着重要作用。在肿瘤发生发展中,snail不仅是上皮细胞间充质转分化现象中关键的诱导子,而且在干细胞特性产生和维持、细胞生存和凋亡、以及免疫调节中起着重要作用。本文就转录因子snail的结构和功能、snail的调节途径以及snail在肿瘤发生发展中的相关研究进展作一综述。
建立生态经济型橡胶园橡胶咖啡间作模式
黄克新,
生态学杂志 , 1991,
Abstract: ?
定向选育米曲霉提取香菇鲜味成分
,许学
食品科学 , 2005,
Abstract: ?对a.oryzae进行了定向选育,利用定向选育出来的菌株水解香菇,提取鲜味成分。分别以产蛋白酶、纤维素酶、果胶酶为目标建立了选育模型。讨论了不同的筛选模型对菌株产酶分布的影响,以及其酶系分布特点对香菇固体发酵的影响。实验结果表明,在水解香菇制备鲜味剂的复合酶解过程中纤维素酶是关键酶。确定了用于该过程菌株的选育方法。
Page 1 /25289
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.