艾立许混捏机在炭阳极生产中的应用

在预焙阳极生产中，卧式连续混捏机曾经大量应用，使用效果良好。目前艾立许混捏机以其简化机械设计、降低维修及操作成本等许多独特的优点，在铝电解炭阳极生产中也得到大量应用。通过对原有卧式混捏机设备增加艾立许冷却系统，可以使两种设备得到最佳组合，可提高阳极质量，取得良好效益。     

二郎山滑坡坡脚挡墙破坏及修复技术研究

二郎山1号滑坡位于龙胆溪左岸,滑坡前缘采用了坡脚挡墙防冲刷工程,10 a之后位于龙胆溪凹岸部分坡脚挡墙破坏。针对坡脚挡墙破坏形成的原因,对比分析了目前常用冲刷防护结构的局限性,提出了采用台坎式挡墙新组合结构进行修复,组合结构主要由挡土墙、墙底抗滑键、墙脚台坎以及耐磨肋组成。介绍了组合结构设计计算方法,并通过造价分析说明台坎式挡墙结构与其它防冲刷相比在经济上具有明显优越性,可为其它同类地质灾害防治提供参考。     

Order-k α-hulls and α-shapes

We introduce order-k α-hulls and α-shapes – generalizations of α-hulls and α-shapes. Being also a generalization of k-hull (known in statistics as “k-depth contour”), order-k α-hull provides a link between shape reconstruction and statistical depth. As a generalization of α-hull, order-k α-hull gives a robust shape estimation by ignoring locally up to k outliers in a point set. Order-k α-shape produces an “inner” shape of the set, with the amount of “digging” into the points controlled by k. As a generalization of k-hull, order-k α-hull is capable of determining “deep” points amidst samples from a multimodal distribution: it correctly identifies points which lie outside clusters of samples.The order-k α-hulls and α-shapes are related to order-k Voronoi diagrams in the same way in which α-hulls and α-shapes are related to Voronoi diagrams. This implies that order-k α-hull and α-shape can be readily built from order-k Voronoi diagram, and that the number of different order-k α-shapes for all possible values of α is proportional to the complexity of order-k Voronoi diagram.     

Linear reconstruction measure steered nearest neighbor classification framework

The linear reconstruction measure (LRM), which determines the nearest neighbors of the query sample in all known training samples by sorting the minimum L₂-norm error linear reconstruction coefficients, is introduced in this paper. The intuitive interpretation and mathematical proofs are presented to reveal the efficient working mechanism of LRM. Through analyzing the physical meaning of coefficients and regularization items, we find that LRM provides more useful information and advantages than the conventional similarity measure model which calculates the distance between two entities (i.e. conventional point-to-point, C-PtP). Inspired by the advantages of LRM, the linear reconstruction measure steered nearest neighbor classification framework (LRM-NNCF) is designed with eight classifiers according to different decision rules and models of LRM. Evaluation on several face databases and the experimental results demonstrate that these proposed classifiers can achieve greater performance than the C-PtP based 1-NNs and competitive recognition accuracy and robustness compared with the state-of-the-art classifiers.     

3D fingerprint reconstruction system using feature correspondences and prior estimated finger model

The paper studies a 3D fingerprint reconstruction technique based on multi-view touchless fingerprint images. This technique offers a solution for 3D fingerprint image generation and application when only multi-view 2D images are available. However, the difficulties and stresses of 3D fingerprint reconstruction are the establishment of feature correspondences based on 2D touchless fingerprint images and the estimation of the finger shape model. In this paper, several popular used features, such as scale invariant feature transformation (SIFT) feature, ridge feature and minutiae, are employed for correspondences establishment. To extract these fingerprint features accurately, an improved fingerprint enhancement method has been proposed by polishing orientation and ridge frequency maps according to the characteristics of 2D touchless fingerprint images. Therefore, correspondences can be established by adopting hierarchical fingerprint matching approaches. Through an analysis of 440 3D point cloud finger data (220 fingers, 2 pictures each) collected by a 3D scanning technique, i.e., the structured light illumination (SLI) method, the finger shape model is estimated. It is found that the binary quadratic function is more suitable for the finger shape model than the other mixed model tested in this paper. In our experiments, the reconstruction accuracy is illustrated by constructing a cylinder. Furthermore, results obtained from different fingerprint feature correspondences are analyzed and compared to show which features are more suitable for 3D fingerprint images generation.     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

基于灰度形态重构的颗粒图像分割方法

提出了一种基于距离变换、形态重构和分水岭算法的图像分割算法。将一幅图像通过距离变换得到距离灰度图,与形态重构算法结合,得到颗粒图像的标识点图,用标识点图对距离灰度图进行分割,再用分水岭变换对分割后的距离灰度图进行变换。试验表明,该算法能有效合理地解决粘连或者重叠颗粒等物体的分割。     

用于确定双吸离心泵Suter曲线的三维内特性法研究

水泵Suter曲线是泵站水锤分析的必备条件之一。现有用于获取双吸离心泵Suter曲线的实测法具有难度大、成本高的特点,而间接插值估算法的精度又比较低,本文提出了一种通过水泵流场三维数值计算确定双吸离心泵Suter曲线的新方法,简称三维内特性法。该方法采用计算流体力学(CFD)技术,对双吸离心泵的水力模型进行三维造型、工况离散、流场计算及结果转化,以快速、准确地获取双吸离心泵Suter曲线,在正水泵工况、正水轮机工况、反水轮机工况、反水泵工况等工况下,计算出相应流量、转速、扬程和转矩关系。定量分析表明,相比于间接插值估算法,采用三维内特性法获得的Suter曲线中,Wh(x)的计算精度平均提高了39.2%,Wb(x)的计算精度平均提高了26.3%;在将采用该方法获得的双吸离心泵Suter曲线用于事故停泵工况水锤分析时,计算得到的系统最大压力降低了15.0%,管线发生汽化压力的范围缩减了81.4%,双吸离心泵的最大倒流量增大了11.9%,最大倒转速降低了6.1%,与实际测试结果更为接近。该方法为提高双吸离心泵站水锤分析精度提供了一种新技术途径。