ANALYSIS OF THREE-DIMENSIONAL UPSETTING PROCESS BY THE RIGID-PLASTIC REPRODUCING KERNEL PARTICLE METHOD

A meshless approach, called the rigid-plastic reproducing kernel particle method （RKPM）, is presented for three-dimensional （3D） bulk metal forming simulation. The approach is a combination of RKPM with the flow theory of 3D rigid-plastic mechanics. For the treatments of essential boundary conditions and incompressibility constraint, the boundary singular kernel method and the modified penalty method are utilized, respectively. The arc-tangential friction model is employed to treat the contact conditions. The compression of rectangular blocks, a typical 3D upsetting operation, is analyzed for different friction conditions and the numerical results are compared with those obtained using commercial rigid-plastic FEM （finite element method） software Deform^3D. As results show, when handling 3D plastic deformations, the proposed approach eliminates the need of expensive meshing and remeshing procedures which are unavoidable in conventional FEM and can provide results that are in good agreement with finite element predictions.

Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods

Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.

TURBULENT SEPARATED REATTACHED FLOW IN A TWO-DIMENSIONAL CURVED-WALL DIFFUSER

A turbulent separation-rcattachment flow in a two-dimensional asymmetrical curved-wall diffuser is studied by a two-dimensional laser doppler velocimeter.The turbulent boundary layer separates on the lower curved wall under strong pressure gradient and then reattaches on a parallel channel.At the inlet of the diffuser,Reynolds number based on the diffuser height is 1.2×10~5 and the velocity is 25.2m/s.The re- sults of experiments are presented and analyzed in new defined streamline-aligned coordinates.The experiment shows that after Transitory Detachment Reynolds shear stress is negative in the near-wall backflow region. Their characteristics are approximately the same as in simple turbulent shear layers near the maximum Reynolds shear stress.A scale is formed using the maximum Reynolds shear stresses.It is found that a Reynolds shear stress similarity exists from separation to reattachment and the Schofield-Perry velocity law ex- ists in the forward shear flow.Both profiles are used in the experimental work that leads to the design of a new eddy-viscosity model.The length scale is taken from that developed by Schofield and Perry.The composite velocity scale is formed by the maximum Reynolds shear stress and the Schofield Perry velocity scale as well as the edge velocity of the boundary layer.The results of these experiments are presented in this paper

Finite Fractal Dimensionality of Compact Kernel Sections for Dissipative Non-Autonomous Klein-Gordon-Schrödinger Lattice Systems

In this paper, an upper bound of fractal dimension of the compact kernel sections for the dissipative non-autonomous Klein-Gordon-Schrödinger lattice system is obtained, by applying a criterion for estimating fractal dimension of a family of compact subsets of a separable Hilbert space.

Multi-label dimensionality reduction and classification with extreme learning machines

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.

STUDY OF RECOGNITION TECHNIQUE OF RADAR TARGET'S ONE-DIMENSIONAL IMAGES BASED ON RADIAL BASIS FUNCTION NETWORK

This paper studies the problem applying Radial Basis Function Network(RBFN) which is trained by the Recursive Least Square Algorithm(RLSA) to the recognition of one dimensional images of radar targets. The equivalence between the RBFN and the estimate of Parzen window probabilistic density is proved. It is pointed out that the I/O functions in RBFN hidden units can be generalized to general Parzen window probabilistic kernel function or potential function, too. This paper discusses the effects of the shape parameter a in the RBFN and the forgotten factor A in RLSA on the results of the recognition of three kinds of kernel function such as Gaussian, triangle, double-exponential, at the same time, also discusses the relationship between A and the training time in the RBFN.

Quantifying.Associations among Dimensions of Ears and Their Form Factors in Maize(Zea Mays)Using Dimensional Analysis

Ear morphological traits such as volume and shape are important features of maize and the quantitative associations among them can help understand kernel yield determination. 150 mature ears each of 4 maize cultivars were collected from field experiments, and ear length(L), diameter(D), area(S) and volume(V) were recorded for individual ears, kernel weight per ear also recorded for a portion of the examined ears. Following principles of dimensional analysis, 8 theoretical equations of 3 sets,which relate ear higher dimensions to its length and diameter, were developed and parameterized and validated with the field observations. The 3 optimized equations showed that the shape of ears in maize can be featured with 3 dimensionless form factors, namely diameter-to-length ratio(c=D/L), areal form factor(b=S/L/D), and volumetric form factor(a=V/L/D/D). Statistically,all of them were significantly different among cultivars, and a's values varied from 0.582 to 0.612, and b's 0.839-0.868, and c's 0.242-0.308. Volumetric form factor and areal form factor could estimate precisely ear volume and area respectively, but diameter-to-length ratio was not suitable to estimate ear diameter by its length. Ear volume explained almost all variation of ear kernel weight and product L*D*D did the same substantially. Dimensional analysis proved to be promising in understanding relationship among morphological traits of ears in maize. Its application in crop researches should improve our knowledge of the physical properties of crop plants.

Local Kernel Dimension Reduction in Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Instead of evaluating the likelihood function, ABC approximates the posterior distribution by a set of accepted samples which are simulated from a generating model. Simulated samples are accepted if the distances between the samples and the observation are smaller than some threshold. The distance is calculated in terms of summary statistics. This paper proposes Local Gradient Kernel Dimension Reduction (LGKDR) to construct low dimensional summary statistics for ABC. The proposed method identifies a sufficient subspace of the original summary statistics by implicitly considering all non-linear transforms therein, and a weighting kernel is used for the concentration of the projections. No strong assumptions are made on the marginal distributions, nor the regression models, permitting usage in a wide range of applications. Experiments are done with simple rejection ABC and sequential Monte Carlo ABC methods. Results are reported as competitive in the former and substantially better in the latter cases in which Monte Carlo errors are compressed as much as possible.

Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis

The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven.

Adaptive Metric Learning for Dimensionality Reduction

Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be converted to develop an effective distance metric. Most existing dimensionality reduction methods use a fixed pre-specified distance metric. However, this easy treatment has some limitations in practice due to the fact the pre-specified metric is not going to warranty that the closest samples are the truly similar ones. In this paper, we present an adaptive metric learning method for dimensionality reduction, called AML. The adaptive metric learning model is developed by maximizing the difference of the distances between the data pairs in cannot-links and those in must-links. Different from many existing papers that use the traditional Euclidean distance, we use the more generalized l2,p-norm distance to reduce sensitivity to noise and outliers, which incorporates additional flexibility and adaptability due to the selection of appropriate p-values for different data sets. Moreover, considering traditional metric learning methods usually project samples into a linear subspace, which is overstrict. We extend the basic linear method to a more powerful nonlinear kernel case so that well capturing complex nonlinear relationship between data. To solve our objective, we have derived an efficient iterative algorithm. Extensive experiments for dimensionality reduction are provided to demonstrate the superiority of our method over state-of-the-art approaches.

三维二十面体准晶弹性半平面周期接触问题研究

借助平面弹性复变方法,研究了周期刚性压头作用下三维二十面体准晶弹性半平面的周期有限摩擦和周期粘结接触问题.从应力、位移分量的复变函数形式表达式出发,利用半平面Hilbert核积分公式、Plemelj公式,得到两类周期接触问题的解答.针对周期有限摩擦接触问题,得到了三类周期刚性压头(直水平、直倾斜和圆形基底)作用在准晶体半平面上接触应力的封闭解.针对半平面周期粘结接触问题,求得接触边界上作用周期尖劈形位移时接触应力的显式解答.当不考虑相位子场的作用时,文中求得的理论结果可退化到正交各向异性材料平面弹性周期接触问题的对应结果.数值算例说明准晶弹性常数对接触应力分布规律及大小的影响.本文结论可为分析准晶材料压痕实验结果及准晶材料性能提供一定理论依据.

基于校正光谱序列融合的小麦腥黑穗病籽粒分类方法

针对小麦腥黑穗病轻度患病籽粒易与健康籽粒混淆,人工识别难度大的问题,将校正光谱序列融合技术与深度学习模型相结合,实现小麦腥黑穗病籽粒快速、精准分类。以健康、轻度患病、重度患病各300粒小麦籽粒的高光谱数据为样本,通过多元散射校正算法(MSC)和标准正态变换算法(SNV)对原始光谱进行预处理,并利用二维相关光谱法(2D-COS)分析SNV与MSC算法处理后的光谱之间的互补性。使用校正光谱序列融合技术将原始光谱、SNV预处理光谱与MSC预处理光谱三者进行融合得到序列融合光谱,以充分利用不同光谱预处理数据间的互补信息。最终,利用序列融合光谱数据建立基于ResNet 50算法的小麦腥黑病分类模型。试验结果表明,序列融合光谱ResNet 50模型总体准确率最高为93.89%,F1值为93.87%,分类性能优于单一预处理光谱建立的ResNet 50模型。为进一步评估模型分类效果,使用序列融合光谱分别建立偏最小二乘判别分析(PLS-DA)、支持向量机(SVM)以及集成学习算法模型随机森林(RF)与极端梯度提升树(XGBoost)模型,并进行对比,结果显示:SVM、PLS-DA、RF与XGBoost总体准确率分别为81.67%、84.44%、89.44%与90.55%,F1值分别为81.59%、84.04%、89.49%与90.59%,ResNet 50总体准确率与F1值优于传统光谱分析模型。因此,本研究表明校正光谱序列融合技术结合深度学习模型,能够实现对不同患病程度腥黑穗病籽粒的有效分类。

基于相关性分析和生成对抗网络的电网缺失数据填补方法

城市电网新型电力系统中多元资源增多,数据采集难度加大,导致数据随机缺失率升高,难以满足精细化分析决策需求。为解决新型电力系统中配网量测数据在采集与传输过程中频发的缺失问题,文中提出一种基于波动互相关分析(fluctuation cross-correlation analysis,FCCA)算法和生成对抗网络(generative adversarial network,GAN)的电网缺失数据填补方法。首先,融合FCCA算法提出强相关性电网数据多维特征提取方法;其次,基于核主成分分析(kernel principal component analysis,KPCA)对多维特征数据集进行降维处理;最后,设计改进型GAN结构,融合电网数据多维特征对低维向量进行重构,实现缺失数据填补。算例采用真实电网数据进行算法验证,并在某城市电网试运行。结果表明,所提方法比传统数据填补方法具有更高填补精度。因此,在新型电力系统中量测数据连续缺失和缺失量较大的情况下,融合强相关性特征进行数据填补,对提升量测数据的完整性和可用性有明显优势。

基于PCA和ICA模式融合的非高斯特征检测识别

针对无人船(Unmanned surface vehicle,USV)航行位姿观测数据的非高斯性/高斯性判别问题,提出一种基于主成分分析(Principal component analysis,PCA)和独立成分分析(Independent component analysis,ICA)模式融合的非高斯特征检测识别方法.首先,采用基于标准化加权平均和信息熵的数据预处理方法.其次,引入混合加权核函数并使用灰狼优化(Grey wolf optimization,GWO)算法进行参数优化,以提高PCA方法的准确性.同时,该算法采用一种新的非线性控制因子策略,提高全局和局部搜索能力.最后,建立了一种基于ICA和PCA联合的相关性分析方法来实现多维数据的降维,在降维数据的基础上综合T型多维偏度峰度检验法和KS(Kolmogorov-Smirnov)检验法进行非高斯性/高斯性特征检测识别.该方法考虑了非线性非高斯的噪声对降维结果精确度的影响,有效降低了多维数据非高斯检测的复杂度,同时也为后续在实际USV位姿估计等应用中提供了保障.实验表明,该方法具有较高的准确性和稳定性,可为USV航行位姿观测数据处理提供支持.

基于降维处理的快速EMT图像重建算法

电磁层析成像技术(EMT)具有非侵入、响应速度快、成本低等优点,在工业过程监测和多相流测量等领域有广泛的应用前景。该文针对电磁层析成像逆问题的病态性,提出了1种非迭代的、基于灵敏度矩阵降维的EMT图像重建算法,应用核主成分分析(KPCA)方法对灵敏度矩阵进行降维,有效降低了算法计算复杂度,同时降低了灵敏度矩阵的病态程度。为验证该算法的有效性,将该算法应用于平面EMT金属探伤,并将其与传统的线性反投影算法和Landweber迭代法进行比较。仿真和实验结果表明,该算法的成像质量远高于线性反投影算法,与Landweber迭代法相近,且该算法的计算耗时仅为Landweber迭代法的20%左右。

多维要素流视角下成渝城市群空间结构特征研究

随着交通、通讯设施的日益完善与经济的快速发展,城市间各类要素流动更为频繁并形成城市网络,促进城市动态“流”数据的分析成为区域空间结构研究新范式。相比单一要素流,多维要素流可以从更加综合的视角刻画城市群内部网络联系,识别城市群空间结构特征。本文通过集成百度迁徙、快递物流线路、百度指数、企业总部—分支、科技论文合作等多元地理流数据建立成渝城市群人流、物流、信息流、资金流、技术流及综合流网络,借助社会网络分析方法识别网络节点特征并结合位序—规模法则评估城市体系规模结构,利用核密度分析法识别多维要素流动主要廊道,结合优势流和DBSCAN聚类分析成渝城市群空间组团特征。结果表明:第一,在多维要素流网络中,各节点层级分化明显,成都市、重庆市是成渝城市群的两大核心,对多维要素流的集聚扩散能力突出,而其他城市普遍发育不足。第二,重庆市—成都市关联区间联系强度最高,成都市、重庆市与14个地级市组成的关联区间次之,14个地级市之间组成的关联区间最低,成渝发展主轴、成德绵乐城市带是要素流动的主要廊道。第三,在优势流约束下,成渝城市群内部形成成都—德阳—眉山、重庆—广安、南充—遂宁、内江—自贡—宜宾—泸州、乐山—雅安共5个空间聚类,其中南充—遂宁、内江—自贡—宜宾—泸州具备培育都市圈的潜力。结合本文分析结果和现有规划,建议将多维要素流网络中心度相对较高的绵阳、南充、宜宾作为次级中心城市培育,在重点发展成都都市圈、重庆都市圈的同时着力培育南充—遂宁、内自宜泸两大都市圈,促进绵阳市、雅安市、乐山市、达州市等圈群空隙城市差异化、特色化发展,强化宜宾—泸州—重庆沿江发展轴,逐步优化成渝城市群空间结构,形成区域协调发展新格局。

基于KLPP-K-means-BiLSTM的台区短期电力负荷预测

随着智能电网的发展,各场景的用电更加多元化,而准确的台区负荷预测是确保相关电力部门制定合适检修任务的关键,同时为有序用电、电网经济运行提供重要参考。为了挖掘台区负荷的特征以提高台区负荷预测的精度,提出了一种基于核主元分析与局部保持投影降维、K均值聚类算法(K-means)以及双向长短时记忆网络(BiLSTM)的台区电力负荷预测方法。首先利用核局部保持投影(KLPP)对台区多特征负荷数据进行降维以提取主要特征信息;然后采取K-means聚类算法将相似特征的数据归类成各自的簇集;最后针对聚类后的各典型类型,有针对性地训练BiLSTM,并选取中国某高校低压台区负荷作为算例与其他经典预测方法进行对比分析,结果表明所提方法更拟合实际负荷走向,有效提升了预测效果。

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

针对大噪声的三维SLAM位姿图鲁棒优化算法

位姿图优化(pose graph optimization,PGO)是同时定位与建图(simultaneous locali-zation and mapping,SLAM)中非常重要的非凸高维优化工具。运动位姿初始化是PGO过程中的关键步骤。针对目前的初始化算法无法为大噪声PGO数据集提供较好初始值的问题,首先提出一种新的鲁棒核函数,解决大噪声带来的估计值与观测值之间的大残差问题;然后,提出一种基于迭代重加权最小二乘的位姿图鲁棒优化算法。蒙特卡罗实验结果表明,所提算法在大噪声环境中具有较好的鲁棒性和精确性,所提鲁棒核函数与其他经典核函数相比更具适用性,能够加快算法收敛。