基于典型相关分析的机械故障盲源分离方法研究

提出一种基于典型相关分析的机械故障盲分离方法。与基于独立分量分析的故障盲分离方法相比较,传统的机械故障盲分离方法只考虑样本值的统计分布,不考虑源信号之间的时间和空间关系,而提出的方法克服了此不足,利用源信号的自相关关系来进行盲源分离。仿真结果表明:提出的方法取得了满意的分离效果,并且具有很高的的计算效率,分离速度要快于传统的盲源分离方法。最后,将提出的方法应用到轴承故障诊断中,实验结果进一步验证了该方法的有效性。     

Jatropha curcas L. (Euphorbiaceae) modulates stomatal traits in response to leaf-to-air vapor pressure deficit

Cultivation of Jatropha curcas in arid and semiarid non-cultivated areas could be a sustainable strategy for stimulating biofuel production without competing with food crops for land and water resources. J. curcas is considered a drought-tolerant species; however, the mechanisms that provide tolerance are unknown. Few efforts have been made to understand the connections between stomatal development and environment conditions. Here, we compared changes in stomatal density (SD) and stomatal index (SI) and their influence on gas exchange in J. curcas. Plants were cultivated in both rainy and dry regions. We describe a distinctive distribution of stomata under the adaxial and abaxial leaf epidermises, where higher SD may have caused the increase in stomatal conductance (g_s) with positive effects on net photosynthetic rate (P_N). However, when rain was excluded, the variation in g_s was strongly related to vapour pressures deficit (VPD), and VPD was strongly related to the P_N. Thus, our results suggest that J. curcas may also contribute mitigating the effect of CO₂ deposition in the atmosphere, given that a remarkable change in SD and other leaf traits was observed in response to seasonal variations. Moreover, multivariate analysis highlights the high sensitivity of J. curcas plants to VPD which in turn induces rapid stomatal closure and consequent reduction of P_N for long periods of time which reflect into a change in the pattern of development resulting in higher SI. These results can help us to understand the relationship between stomatal features and gas exchange in response to environment changes.     

Droplet velocity and thermal state from hot gas atomization of steel melt: Impact on the quality of the spray-formed tubular deposit

The velocity and thermal behavior (temperature, enthalpy, solid fraction) of atomized droplets in a metal spray play the most important role in the spray forming process. These properties mainly determine the materials yield and the final product quality (e.g., porosity, microstructure) of the as-sprayed materials. Changing the gas temperature in the atomization process directly influences these droplet properties in the spray. To understand the droplet behavior in the spray at various atomization gas temperatures (i.e., room temperature RT 293 K, 573 K, 873 K), numerical simulations using computational fluid dynamics (CFD) techniques have been performed and validated by experiments. A series of atomization runs (powder production and spray-forming with AISI 52100 steel) has been conducted at different atomization gas temperatures and pressures with a close-coupled atomizer (CCA). The in-situ temperature detection of the deposit surface (pyrometer) and in the substrate (thermocouples) has been performed to observe the effect of particle properties on the deposit. The result shows that hot gas atomization provides smaller droplets with faster velocity in the spray, affecting the droplet impact and deformation time in the deposition zone. A higher solid fraction of the smaller droplets by hot gas atomization also reduces the deposit surface temperature. Increasing the substrate diameter further decreases the deposit surface temperature without compromising the deposit quality (i.e., porosity) and also refines the grain size. Pre-heating of the substrate up to 573 K results in lower porosity in the vicinity of the substrate.     

基于SM9的CCA安全广播加密方案

选择密文安全模型能有效刻画主动攻击,更接近现实环境.现有抵抗选择密文攻击的密码算法以国外算法为主,缺乏我国自主设计且能抵抗选择密文攻击的密码算法.虽然实现选择密文安全存在通用转化方法,代价是同时增加计算开销和通信开销.基于国密SM9标识加密算法,提出一种具有选择密文安全的标识广播加密方案.方案的设计继承了SM9标识加密算法结构,用户密钥和密文的大小都是固定的,其中用户密钥由一个群元素组成,密文由3个元素组成,与实际参与加密的接收者数量无关.借助随机谕言器,基于GDDHE困难问题可证明方案满足CCA安全.加密算法的设计引入虚设标识,通过该标识可成功回复密文解密询问,实现CCA的安全性.分析表明,所提方案与现有高效标识广播加密方案在计算效率和存储效率上相当.     

fMRI analysis on the GPU-possibilities and challenges

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64 × 64 × 22 voxels), all the preprocessing takes about 0.5 s on the GPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10,000 permutations, with smoothing in each permutation, takes about 50 s if three GPUs are used, compared to 0.5-2.5 h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.     

Mo_2C/CCA催化剂制备及环己烷脱氢性能研究

以(NH4)6Mo7O24.4H2O为钼源,以覆炭γ-Al2O3(CCA)为载体,采用浸渍法制备出催化剂前驱物Mo3O/CCA。以正己烷为渗碳剂对前驱物进行碳化得到Mo2C/CCA。对不同碳化终温下制得的催化剂进行了XRD表征。在固定床反应装置上进行了环己烷脱氢反应,考察了催化剂的制备条件及反应条件对催化剂脱氢活性的影响。结果表明:当载体CCA覆炭量为7.82%,碳化终温为650℃,反应条件为温度475℃、压力0.1 MPa、空速2h-1、氢烃体积比200∶1时,环己烷的转化率最高可达91%。     

基于2D-FrFT多阶次特征融合的人脸表情识别方法

提出一种二维分数阶傅里叶域(2D-FrFT)多阶次特征融合分类算法.该方法充分利用分数阶傅里叶域不同阶次下表情特征之间的相关性,选取两个阶次的表情特征,利用典型相关分析法(Canonical Correlation Analysis,CCA)进行特征融合,并通过基于支持向量机(Support Vector Machine,SVM)的多层次分类机制进行人脸表情识别.仿真实验结果表明,采用多阶次特征融合算法后提高了平均识别率,降低了表情特征维数,减小了计算量.     

基于格上带舍入学习问题的抗选择密文攻击的公钥密码体制

基于格的密码体制具有可抵抗目前已知量子算法攻击、并行性好、由格上困难问题确保的可证安全性等优点,具有广泛的应用前景,受到了越来越多的关注.带舍入学习问题（learn-ing with rounding问题,LWR问题）作为格上新兴问题,相关工作较少,研究价值较高.文章提出了两种基于该问题的公钥加密体制,给出了在随机谕示模型下的安全性证明.与现有基于该问题的体制相比,新体制可以达到适应性选择密文攻击下不可区分性（IND-CCA2）的安全性.     

基于Babel的构件程序设计

为了解决高性能科学计算程序设计当中存在的开发难度大,开发周期长以及时开发人员要求高等问题,人们已经开始将软件构件技术引入该领域。由美国能源部、犹他州大学、印弟安那大学等联合提出的CCA便是研究高性能科学计算构件技术的项目之一。本文主要介绍了CCA以及CCA框架下的语言互操作工具－Babel的相关情况,并且通过NPB基准测试程序IS详细描述了Babel的使用,分析了基于Babel的程序设计对程序性能的影响。初步实验表明Babel能够有效解决语言的互操作问题,在面向科学计算的构件程序设计环境中能够发挥关键作用。