基于可见光源与独立成分分析法的实时人眼检测系统

提出一种基于特征与外貌混合检测确定人眼区域的实时人眼检测方法. 首先,依据可见光源在人眼角膜上反射形成耀点特性,通过图像处理算法提取潜在耀点位置,利用人眼几何特征的确定可能人眼候补区域;然后,提取人眼数据库中具有不同外貌特征的200幅人眼图像,采用FastICA算法估计出提取人眼图像的有效成分分析(ICA)基向量;最后,通过计算人眼候选区域在基向量上投影角度判断出左、右人眼区域准确位置. 实验结果表明,在人脸面部旋转、佩戴眼镜、大范围头部运动和不同光照强度下,实时人眼检测具有较高的检测正确率和较好的鲁棒性.      

气－固流动系统无干扰测量设备的新进展

为了在气－固流动系统过程中确定运输特性，证实理论预报和仪设计程序公式化，必须对气－固两相流的各相质量流量、密度和局部地区的流速进行测量，对于某种相（诸如一群固体颗粒）的密度进行直接测量时需要有一种基本测量标准，激光多普勤相测定仪在一定条件下可以测定局部地区的瞬时密度，因而可用作基本测量标准，其它光学方法及中子束方法由于需要用质量流量进行标定，因而只能用作间接的测量标准，本文对无干扰测试设备的新进展进行了综述，对激光多普勒测速仪（ＬＤＶ）、相多普勤颗粒分析仪（ＰＤＰＡ）直至核磁共振测速仪的性能、限制因素及应用前景作了概述和讨论。     

Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data

The growing interest in energy-efficient buildings is driving changes in investment, design, and occupant behavior. To better focus cost and resource conservation efforts, electricity consumption feedback can be used to provide motivation, guidance, and verification. Disaggregating by end-use helps both consumers and producers to identify targets for conservation. While hardware-based sub-metering is costly and labor-intensive, non-intrusive load monitoring (NILM) is capable of gathering detailed energy-use data with minimal equipment cost and installation time. However, variations in measurements between metering devices complicate the process of compiling the necessary appliance profiles. Future work involves the devel-opment of NILM algorithms using sensor fusion and detailed appliance-level data gathered from a highly-sensed house currently being constructed near Pittsburgh, Pennsylvania.     

Heat Transfer Evaluation Method in Complex Rotating Environments Employing IR Thermography and CFD

This article presents a measuring method for determination of convective heat transfer in rotating environments. The method is used to obtain temperature and Nusselt number distributions on optically accessible objects inside real operating environments, without the need for implementation of complex surface heat flux measurements. Temperature maps of the observed surfaces are acquired non-intrusively by infrared (IR) thermography, whereas heat flux data is computed numerically by computational fluid dynamics (CFD) tools. The method was employed on a rotating hollow blade, heated internally by secondary air flow. Experimental and numerical results for the observed blade side are compared in terms of surface temperature 2D distributions. Temperature distributions are further statistically evaluated and show good agreement, which is the basic precondition for combining experimental and numerical data by the method. Results are presented in terms of combined experimental-numerical Nusselt number shown as two-dimensional distribution on the blade pressure side.     

基于双通道多特征融合的电力负荷智能感知方法

负荷识别是分析用户用电行为的主要工具之一.提高负荷识别的精度对于开展用能监测服务、实现节能降损具有重要意义.提出了一种基于双通道多特征融合的电力负荷智能感知方法.首先,从电器设备的基本属性出发,分析了电流、谐波、功率等数值特征以及电压-电流(V-I)轨迹图像特征对负荷识别的影响;其次,考虑了特征之间的互补性,分别采用主成分分析-神经网络(principal component analysis-back propagation,PCA-BP)算法和卷积神经网络算法将数值特征和图像特征以不同通道在高维空间进行深度融合;最后,采用Softmax分类算法对融合后的高级特征和设备标签进行有监督的学习,从而实现了不同类别电器设备的有效辨识.算例测试结果显示,所提出方法的负荷识别准确率高达94.55％.该结果充分说明了将多种特征进行高维空间融合,可以更全面、立体地反映设备的本质属性,提高算法的识别精度.     

Efficiency of randomised dynamic mode decomposition for reduced order modelling

ABSTRACT

The purpose of this paper is the identification of a reduced order model (ROM) from numerical code output by non-intrusive techniques (i.e. not requiring projecting of the governing equations onto the reduced basis modes). In this paper, we perform a comparison between two methods of model order reduction based on dynamic mode decomposition (DMD). The first method is a deterministic (classic) DMD technique endowed with a dynamic filtering criterion of selection of modes used in the ROM model. The second method is an adaptive randomised DMD algorithm (ARDMD) based on a randomised singular value decomposition. This produced an accelerating algorithm, which is endowed with a few additional advantages. In addition, the reduced order model is guaranteed to satisfy the boundary conditions of the full model, which is crucial for surrogate modelling. For numerical illustration, we use the shallow water equations model.     

爆炸波中的混合不确定度量化方法

将认知不确定作为外层,偶然不确定作为内层,利用混合不确定理论处理爆炸波中的不确定度问题.分别使用非嵌入多项式混沌方法（NIPC）和Dempster-Shafer理论处理偶然不确定和认知不确定,用迎风格式求解确定系统.结果表明：NIPC和Dempster-Shafer结构为模型输入参数和初边值不确定性对输出响应量的影响提供一种有效方法,对各种建模与模拟不确定性评估和确认建模与模拟具有很好的参考价值.     

Numerical prediction of the influence of uncertain inflow conditions in pipes by polynomial chaos

Nearly all types of flow measurement devices installed in pipes are affected by the flow conditions at their inlet section, which can lead to measurement errors of several per cent. To evaluate the influence of uncertain inflow profiles on the flow field at different positions of the flow meter, a non-intrusive polynomial chaos approach is applied to simulations of turbulent pipe flow. This allows us to estimate the expected variations of the flow profiles as a function of the distance to the inlet of the pipe in an efficient way. The polynomial chaos approach shows reasonable convergence already for a small number of function evaluations. The results are validated by comparison with a quasi-Monte Carlo method and an exact solution, where available. The approximation error of the polynomial chaos method with 10 function evaluations is smaller than the one for the quasi-Monte Carlo method with 100 runs.     

Uncertainty quantification in the catalytic partial oxidation of methane

This work focuses on uncertainty quantification of eight random parameters required as input for 1D modelling of methane catalytic partial oxidation within a highly dense foam reactor. Parameters related to geometrical properties, reactor thermophysics and catalyst loading are taken as uncertain. A widely applied 1D heterogeneous mathematical model that accounts for proper transport and surface chemistry steps is considered for the evaluation of deterministic samples. The non-intrusive spectral projection approach based on polynomial chaos expansion is applied to determine the stochastic temperature and species profiles along the reactor axial direction as well as their ensemble mean and error bars with a confidence interval of 95%. Probability density functions of relevant variables in specific reactor sections are also analysed. A different contribution is noticed from each random input to the total uncertainty range. Porosity, specific surface area and catalyst loading appear as the major sources of uncertainty to bulk gas and surface temperature and species molar profiles. Porosity and the mean pore diameter have an important impact on the pressure drop along the whole reactor as expected. It is also concluded that any trace of uncertainty in the eight input random variables can be almost dissipated near the catalyst outlet section for a long-enough catalyst, mainly due to the approximation to thermodynamic equilibrium.     

非嵌入式多项式混沌法在爆轰产物JWL参数评估中的应用

介绍了非嵌入多项式混沌法的数学模型,给出了非嵌入式多项式混沌法进行不确定度量化的主要步骤。采用此方法研究了平面、散心爆轰问题数值模拟中, JWL模型参数R₁、R₂服从均匀分布的随机变量时所引起的爆轰过程计算结果的不确度性,着重分析了爆轰传播过程中压力与密度的统计特性。研究结果表明,非嵌入式多项式混沌法可以为模型输入参数不确定性的传播对输出结果响应量的影响建立一种有效不确定度评估方法,为使用JWL模型时选取参数提供参考。