Metal-free catalyst fabrication by incorporating oxygen groups on the surface of the carbonaceous sample and efficient hydrogen production from NaBH4 methanolysis

In the present study, metal-free catalysts for efficient H₂ generation from NaBH₄ methanolysis was produced for the first time from apricot kernel shells with two-step activation. The first stage of the two-stage activation includes the production of activated carbon with the KOH agent (AKOH), and the second stage includes hydrothermally HNO₃ activation with oxygen doping (O doped AKOH + N). The hydrogen production rate (HGR) and the activation energy (Ea) of the reaction with the obtained metal-free catalyst (10 mg) were determined as 14,444 ml min^?1 g^?1 and 7.86 kJ mol^?1, respectively. The structural and physical-chemical properties of these catalysts were characterized by XRD (X-ray diffraction), SEM (scanning electron microscopy), elemental CHNS analysis, FT-IR (Fourier transform infrared spectroscopy), and nitrogen adsorption analysis. Also, the reusability results of this metal-free catalyst for H₂ production are promising.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

PSO优化多核RVM的模拟电路故障预测

针对模拟电路健康管理的特点，提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征，计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值，将多个核函数线性组合，并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明，该方法在小样本情况下，预测效果优于单一核函数的RVM模型，适用于健康管理中实时预测，具有较好的实用性。     

一种新的主动网络安全体系的设计

尽管主动网络原型系统中设计了一些安全措施，但是它们都不具通用性且主要集中在security方面，这在主动网络必须和传统网络相兼容的情况下，阻碍了主动网络的发展。本文在定义消极型错误和积极型错误的基础上，设计了主动网络资源控制核。然后提出了集security和safety保障为一体的基于资源控制核的一种通用的主动网络安全体系，探讨了该体系应具有的特点、设计目标和可行性，并设计了主动节点的安全机制。     

PZT陶瓷的断裂分析

与有限元法相比,无网格法由于只需节点不用单元而在压电分析中具有优越性。采用一个简单的包含有极化反转及电饱和影响的锆钛酸铅系压电陶瓷(PZT)的本构模型,以局部J积分作为断裂准则,应用再生核点法这一无网格数值法导出了作用电场与断裂载荷的关系曲线,该曲线的趋势与Park[1]的实验观察现象一致。计算程序采用面向对象方法实现。     

Range of the Applicability of the Wilkins Method to the Investigation of the Dynamic Stress-Strain State of Anisotropic Elastic Axisymmetric Shells

The exactness and stability of the Wilkins method in the investigation of the stress-strain state of axisymmetric anisotropic elastic shells, including thick-walled ones, have been studied for different values of artificial viscosity, pulse load rise and fall time, and different deformation process durations. A comparison of numerical calculations, performed by means of an application software package developed by V. A. Romashchenko on the basis of the Wilkins method, with experimental results, known numerical data, and with calculations using the computation kernel LS-DYNA 3D has been carried out.     

大规模训练数据的支持向量机学习新方法

支持向量机不能直接对大规模的训练数据进行学习。提出一种新的减小样本集规模的方法；在映射后的高维空间中寻找两种类别的交界部分，交界部分上的样本作为学习样本。并且指出，不需涉及具体映射的形式只用核函数即可找到交界部分的样本。实验表明，新方法优于直接在低维样本空间中寻找交界部分样本的方法。     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.