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排序方式: 共有5666条查询结果,搜索用时 15 毫秒
1.
《International Journal of Hydrogen Energy》2022,47(11):7242-7251
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. 相似文献
2.
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。 相似文献
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Zhijiang Li Yingping Zheng Liqin Cao Lei Jiao Yanfei Zhong Caiyi Zhang 《Color research and application》2020,45(4):656-670
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. 相似文献
5.
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。 相似文献
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Lepikhin P. P. Romashchenko V. A. Tarasovskaya S. A. Korbach V. G. 《Strength of Materials》2003,35(1):52-59
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. 相似文献
10.
Kernel Spectral Matched Filter for Hyperspectral Imagery 总被引:1,自引:0,他引:1
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. 相似文献