对某自动驾驶仪可靠性增长的试验研究

对某自动驾驶仪可靠性增长工作进行研究,提出了对现役机载电子设备可靠性增长的一些成功、可借鉴的经验。对增长试验过程中发现的问题和管理方面的不足之处,提出一些建议。     

手势数据驱动的头部运动合成方法

非手部手势是手语表达中不可缺少的一部分,头部运动的实现并与手势进行协同表达是其重要研究内容。对真人手语表演数据中的手势与头部动作之间的关系进行了深入研究,提取二者的动作特征,利用核典型相关分析方法(kernel canonical correlation analysis,KCCA)建立起手势与头部动作之间的预测关系模型。动画合成结果以及评价实验表明,KCCA方法能更好地刻画手势与头部动作的协调性,实现虚拟人行为动作合成的逼真性。     

Thermal and mechanical properties of polyhedral oligomeric silsesquioxane (POSS)/polycarbonate composites

A series of composite materials were produced incorporating polyhedral oligomeric silsesquioxane (POSS) derivatives into polycarbonate (PC), by melt blending. Significant differences in compatibility were observed depending on the nano-scale filler's specific structure: trisilanol POSS molecules generally provided better compatibility with PC than fully-saturated cage structures, and phenyl-substituted POSS grades were shown to be more compatible with PC than fillers with other functional groups. Trisilanolphenyl-POSS/PC composites possess the best overall performance among the POSS materials tested. The high compatibility between the trisilanolphenyl-POSS and polycarbonate matrix results in generation of transparent samples up to 5 wt% POSS content. Slightly enhanced mechanical properties including tensile and dynamic mechanical modulus are observed with the increase of trisilanolphenyl-POSS loading at the cost of decreasing ductility of the nanocomposites. Importantly, upon orientation of the PC/POSS nanocomposite, crystallization of POSS within the oriented material results—this observation is consistent with a growing number of observations which suggest that ‘bottom-up’ formation of structures incorporating multiple POSS cages result from orientation of these nanocomposites, and that the hybrid organic-inorganic inclusions may be at the heart of observed nano-scale reinforcement.     

水平管内气液两相流界面湍流各向异性分析

为探讨水平管内气液两相流湍流模型如何选择,提出水平管内气液两相流界面湍流为各向异性的猜想,分析了该界面湍流各向异性产生和发展的机理,指出界面湍流各向异性产生的湍流惯性力是水平管环状流形成的重要原因.通过比较二方程模型和雷诺应力模型(RSM)模拟同一个水平管环状流工况的结果来验证分析的正确性,在此基础上探讨了气液两相流湍流模型的选择依据.结果表明当水平管内气液剪切力较大时适合选用RSM模型,其他情况二方程模型和RSM模型均适用.     

LNG冷能用于冷库制冷性能模拟研究

为使LNG冷能在冷库系统中得到有效利用,从LNG冷能梯级利用的角度出发,设计一种新型LNG冷能制冷的冷库系统,在LNG换热器端和冷库末端实现对LNG冷能的梯级利用,并通过HYSYS模拟对其进行热力学分析和经济性分析.结果表明:该冷库系统COP为1.82,火用效率为80.2%;LNG换热器火用损最大,约占系统总火用损的78.9%,且随LNG进口温度升高而减小;系统COP和火用效率均随LNG进口温度及气化压力升高而增大;该系统投资回收期为4.77 a,具有较好的经济效益.LNG冷能为冷库系统冷量来源提供新的选择,在LNG进口端增设蓄冷设备和加压泵均能提高系统COP和火用效率.     

竖直地埋管换热器多个钻孔地层热阻计算方法

为解决国家规范中关于多个钻孔热干扰下的地层热阻计算问题,针对竖直地埋管换热器系统,提出了矩形阵列钻孔布置中不同参考钻孔下的地层热阻数值计算方法,并通过算例模拟分析研究了不同钻孔布置形式以及不同钻孔间距情况下的地层热阻分布.结果表明:规则矩形阵列钻孔布置的中心位置地层热阻最大,且布置形式越趋近正方形,钻孔间距越小,地层热阻越大;而不规则矩形阵列钻孔布置的最大地层热阻位置会根据未布置钻孔点位发生相对偏移,但偏移量可忽略.研究成果可为竖直地埋管换热器设计提供理论参考.     

Evaluate mobile video quality in hybrid spatial and temporal domain

Mobile video quality assessment plays an essential role in multimedia systems and services. In the case of scalable video coding, which enables dynamic adaptation based on terminal capabilities and heterogeneous network, variable resolution is one of the most prominent types of video distortions. In this paper, we propose a new hybrid spatial and temporal distortion metric for evaluating video streaming quality with variable spatio-temporal resolution. The key idea is to project video sequence into feature domain and calculate the distortion of content information from the projected principal component matrix and its eigenvectors. This metric can measures the degree of content information degradation especially in spatio-temporal resolution scalable video. The performance of the proposed metric is evaluated and compared to some state-of-the-art quality evaluation metrics in the literature. Our results show that the proposed metric achieves good correlations with the subjective evaluations of the EPFL scale video database.     

A semi-supervised privacy-preserving clustering algorithm for healthcare

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.     

非线性自适应滤波在磁致伸缩导波传感器信号处理中的应用

针对传统滤波算法对磁致伸缩导波传感器信号滤波,存在降噪效果不理想、滤波后信号失真等问题,提出一种基于小波分解的非线性自适应滤波算法。在利用小波分解提取参考噪声的基础上,将神经网络和粒子群优化算法引入传统自适应滤波中,实现了对信号的非线性自适应滤波。以纵向导波磁致伸缩传感器对钢杆的检测信号为例,应用该算法进行滤波处理,结果表明:这种算法能够有效滤除检测信号中的噪声,具有滤波后信号信噪比高、信号不失真、容易实现实时处理等优点,可以实际应用在磁致伸缩导波传感器信号处理中。     

Building Sparse Multiple-Kernel SVM Classifiers

The support vector machines (SVMs) have been very successful in many machine learning problems. However, they can be slow during testing because of the possibly large number of support vectors obtained. Recently, Wu (2005) proposed a sparse formulation that restricts the SVM to use a small number of expansion vectors. In this paper, we further extend this idea by integrating with techniques from multiple-kernel learning (MKL). The kernel function in this sparse SVM formulation no longer needs to be fixed but can be automatically learned as a linear combination of kernels. Two formulations of such sparse multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called ldquoequivalentrdquo kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of toy and real-world data sets show that the resultant classifier is compact and accurate, and can also be easily trained by simply alternating linear program and standard SVM solver.