高空核电磁脉冲模拟波形的双指数函数拟合法

 许多标准和公开出版物中都用双指数函数描述高空核电磁脉冲典型波形。通过数值方法，研究了双指数函数一项重要的性质。根据该性质，详细讨论了高空核电磁脉冲模拟波形数值拟合中，双指数函数特征参数与脉冲峰值、前沿、后沿以及半宽等物理参数的关系，从而提出一种简单有效的脉冲参数计算方法。通过IEC标准中规定的高空核电磁脉冲参数的估计与一个实测高空核电磁脉冲模拟波形的数值拟合，验证了该方法的有效性和可靠性。该方法能够适应多种峰值、前沿和半宽的高宽核电磁脉冲模拟波形的数值拟合。     

温跃层边界提取的组合算法*

本文提出一种用于海洋温跃层边界提取的组合算法,该算法基于调整判定阈值后的垂直梯度法和一种改进函数模型几何形态后的拟阶梯函数逼近法。为验证该算法对于温跃层边界提取的适应性、客观性,利用中国Argo实时资料中心发布的全球海洋Argo网格资料集（BOA_Argo）,选取2018年夏季0°~30°N,135°E~165°E所在的太平洋区域的温度剖面数据进行处理,结果表明,该算法对低纬度海区温度剖面拟合质量较好,拟合均方根误差普遍低于1.3℃,而且提取到的温跃层上下边界在纬向更加平缓,然而对于中高纬度海区的温度剖面,组合算法的处理效果不佳,更适合直接通过垂直梯度法进行温跃层的提取。     

Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine

Some problems in using v-support vector machine (v-SVM) for the prediction of nonlinear time series are discussed. The problems include selection of various net parameters, which affect the performance of prediction, mixture of kernels, and decomposition cooperation linear programming v-SVM regression, which result in improvements of the algorithm. Computer simulations in the prediction of nonlinear time series produced by Mackey-Glass equation and Lorenz equation provide some improved results.     

Phase Transitions Induced by Top-Priority of Randomization

We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model (i.e.noise-first model) into account via computing the relaxation time and the order parameter.The scaling exponents of the relaxation time and the scaling relation of order parameter,respectively,axe obtained.     

Characterization of Roasting Time on Sensory Quality,Color, Taste,and Nonvolatile Compounds of Yuan An Yellow Tea

Roasting is crucial for producing Yuan An yellow tea (YAYT) as it substantially affects sensory quality. However, the effect of roasting time on YAYT flavor quality is not clear. To investigate the effect of roasting time on the sensory qualities, chemical components, odor profiles, and metabolic profile of YAYTs produced with 13 min roasting, 16 min roasting, 19 min roasting, 22 min roasting, and 25 min roasting were determined. The YAYTs roasted for 22 min got higher sensory scores and better chemical qualities, such as the content of gallocatechin (GC), gallocatechin gallate (GCG), free amino acids, solutable sugar, meanwhile the lightness decreased, the hue of tea brew color (b) increased, which meant the tea brew got darker and yellower. YAYTs roasted for 22 min also increased the contents of key odorants, such as benzaldehyde, nonanal, β-cyclocitral, linalool, nerol, α-cedrol, β-ionone, limonene, 2-methylfuran, indole, and longiborneol. Moreover, non-targeted metabolomics identified up to 14 differentially expressed metabolites through pair-wise comparisons, such as flavonoids, phenolic acids, sucrose, and critical metabolites, which were the main components corresponding to YAYT roasted for 22 min. In summary, the current results provide scientific guidance for the production of high quality YAYT.     

Approximations to Isentropic Planar Magneto-Hydrodynamics Equations by Relaxed Euler-Type Systems

In this paper, the authors consider an approximation to the isentropic planar Magneto-hydrodynamics (MHD for short) equations by a kind of relaxed Euler-type system. The approximation is based on the generalization of the Maxwell law for nonNewtonian fluids together with the Maxwell correction for the Amp`ere law, hence the approximate system becomes a first-order quasilinear symmetrizable hyperbolic systems with partial dissipation. They establish the global-in-time smooth solutions to the approximate Euler-type equations in a small neighbourhood of constant equilibrium states and obtain the global-in-time convergence towards the isentropic planar MHD equations. In addition, they also establish the global-in-time error estimates of the limit based on stream function techniques and energy estimates for error variables.     

Structural Characteristics,Antioxidant and Hypoglycemic Activities of Polysaccharide from Siraitia grosvenorii

The structural characterization, the in vitro antioxidant activity, and the hypoglycemic activity of a polysaccharide (SGP-1-1) isolated from Siraitia grosvenorii (SG) were studied in this paper. SGP-1-1, whose molecular weight is 19.037 kDa, consisted of Gal:Man:Glc in the molar ratio of 1:2.56:4.90. According to the results of methylation analysis, GC–MS, and NMR, HSQC was interpreted as a glucomannan with a backbone composed of 4)-β-D-Glcp-(1→4)-, α-D-Glcp-(1→4)-, and 4)-Manp-(1 residues. α-1,6 linked an α-D-Galp branch, and α-1,6 linked an α-D-Glcp branch. The study indirectly showed that SGP-1-1 has good in vitro hypoglycemic and antioxidant activities and that these activities may be related to the fact that the SGP-1-1’s monosaccharide composition (a higher proportion of Gal and Man) is the glycosidic-bond type (α- and β-glycosidic bonds). SGP-1-1 could be used as a potential antioxidant and hypoglycemic candidate for functional and nutritional food applications.     

一类利用回归关系的计量指标抽样方案

在产品质量判定的抽样检验问题中,当目标指标需用破坏性试验才能得其值时,更为常用的是用非破坏性试验可得量值的协变指标量来预报它。但在产品抽样验收问题上,未能形成理论较为严密的方法,这是由于预报误差这个关键问题的处理尚未解决得好,即给不出抽样方案的功效计算的正确或是近似性较好的公式。本文通过建立合理的数学模型,把对目标指标的质量要求化为对协变指标量的统计要求,从而利用两者的回归关系,结合两种复杂的抽样方案,给出功效函数的计算公式和计算方法,并进行了分析。     

改进的MEA-WNN图像复原方法

模糊图像复原是计算机视觉和图像处理领域的重要任务。针对思维进化算法(mind evolutionary algorithm,MEA)和小波神经网络(wavelet neural network,WNN)相结合的图像复原模型中,MEA的得分函数相对差别小、选优功能较弱等问题,提出了一种改进的MEA-WNN图像复原方法。该方法采用逻辑回归函数进行幂律变换,增加得分之间的差别,从而增强MEA的选优功能。将改进的模型与传统的基于WNN和MEA-WNN的图像复原模型进行对比,改进的模型把复原图像峰值信噪比(peak signal-to-noise ratio,PSNR)分别提高15%和6.5%、结构相似性(structural similarity,SSIM)提高了6.1%和5%,实验结果证明改进模型的有效性和优越性。     

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.