In this manuscript, a series of catalyst SG n-[VVO2-PAMAM-MSA] (SG silica gel, PAMAM polyamidoamine, MSA 5-methyl salicylaldehyde, n=0, 1, 2, 3) was prepared and their structures were fully characterized by Fourier tr...In this manuscript, a series of catalyst SG n-[VVO2-PAMAM-MSA] (SG silica gel, PAMAM polyamidoamine, MSA 5-methyl salicylaldehyde, n=0, 1, 2, 3) was prepared and their structures were fully characterized by Fourier transform-infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS) and inductive coupled plasma emission spectrometer (ICP) etc. XPS revealed that the metal V and SG n-PAMAM-MSA combined more closely after the formation of Schiff base derivatives. Their catalytic activities for oxidation of dibenzothiophene were evaluated using tert-butyl hydroperoxide as oxidant. The results showed that the catalyst SG 2.0-[VVO2-PAMAM-MSA] presented good catalytic activity and recycling time. Meanwhile, the optimal condition for the catalytic oxidation of SG 2.0-[VVO2-PAMAM-MSA] was also investigated, which showed that when the oxidation temperature was 90 °C, time was 60 min, the O/S was 3:1, and the mass content of catalyst was 1%, the rate of desulfurization could reach 85.2%. Moreover, the catalyst can be recycled several times without significant decline in catalytic activity.展开更多
针对现有模式分类方法不能较好地保持数据空间的局部流形信息或差异信息等问题,提出一种基于流形学习的局部保留最大信息差v-支持向量机(Locality-preserved maximum information variance v-support vector machine,v-LPMIVSVM).对于...针对现有模式分类方法不能较好地保持数据空间的局部流形信息或差异信息等问题,提出一种基于流形学习的局部保留最大信息差v-支持向量机(Locality-preserved maximum information variance v-support vector machine,v-LPMIVSVM).对于模式分类问题,v-LPMIVSVM引入局部同类离散度和局部异类离散度概念,分别体现输入空间局部流形结构和局部差异(或判别)信息,通过最小化局部同类离散度和最大化局部异类离散度,优化分类器的投影方向.同时,v-LPMIVSVM采用适于流形数据的测地线距离来度量数据点对间的相似性,以更好地反映流形数据的本质结构.人造和实际数据集实验结果显示所提方法具有良好的泛化性能.展开更多
基金Supported by the National Natural Science Foundation of China （20901063） the Natural Science Foundation of Hubei Province （2011CDB221）
文摘In this manuscript, a series of catalyst SG n-[VVO2-PAMAM-MSA] (SG silica gel, PAMAM polyamidoamine, MSA 5-methyl salicylaldehyde, n=0, 1, 2, 3) was prepared and their structures were fully characterized by Fourier transform-infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS) and inductive coupled plasma emission spectrometer (ICP) etc. XPS revealed that the metal V and SG n-PAMAM-MSA combined more closely after the formation of Schiff base derivatives. Their catalytic activities for oxidation of dibenzothiophene were evaluated using tert-butyl hydroperoxide as oxidant. The results showed that the catalyst SG 2.0-[VVO2-PAMAM-MSA] presented good catalytic activity and recycling time. Meanwhile, the optimal condition for the catalytic oxidation of SG 2.0-[VVO2-PAMAM-MSA] was also investigated, which showed that when the oxidation temperature was 90 °C, time was 60 min, the O/S was 3:1, and the mass content of catalyst was 1%, the rate of desulfurization could reach 85.2%. Moreover, the catalyst can be recycled several times without significant decline in catalytic activity.
文摘针对现有模式分类方法不能较好地保持数据空间的局部流形信息或差异信息等问题,提出一种基于流形学习的局部保留最大信息差v-支持向量机(Locality-preserved maximum information variance v-support vector machine,v-LPMIVSVM).对于模式分类问题,v-LPMIVSVM引入局部同类离散度和局部异类离散度概念,分别体现输入空间局部流形结构和局部差异(或判别)信息,通过最小化局部同类离散度和最大化局部异类离散度,优化分类器的投影方向.同时,v-LPMIVSVM采用适于流形数据的测地线距离来度量数据点对间的相似性,以更好地反映流形数据的本质结构.人造和实际数据集实验结果显示所提方法具有良好的泛化性能.
