基于分形脊波神经网络的遥感图像分类研究

在分形理论和脊波神经网络的基础上，综合利用彩色遥感图像的光谱、纹理和形状特征，提出了一种彩色遥感图像的分类新方法．该方法把彩色图像的蓝、绿、红波段作为3个光谱特征，由分形理论计算的DBC维和多重分形维数作为2个纹理特征，平均不变矩作为1个形状特征，并利用对曲线具有极强方向识别能力的脊波神经网络作为分类器．实验结果表明，提出的彩色遥感图像分类方法具有较高的分类准确率和较强的抗噪音能力．     

新半金属Fe2ScO4磁电性能的第一原理计算

应用基于密度泛函理论的第一原理赝势法设计了具有尖晶石结构的新半金属材料Fe2ScO4和FeSc2O4,并对它们进行了几何结构优化．详细计算并分析了它们Fe2ScO4和FeSc2O4的分子磁矩、电子结构等磁电性能,并与Fe3O4的磁电性能进行了比较．结果表明,Fe2ScO4和FeSc2O4均是新发现的典型的铁磁性II B型半金属,而Fe3O4则具有亚铁磁性．Fe2ScO4的分子磁矩为7.14 μB,远大于Fe3O4的4.0 μB和FeSc2O4的3.96 μB．Fe2ScO4具有较高分子磁矩的主要原因是在O2p和Fe3d杂化轨道作用下,Fe3d电子高度自旋极化并且局域化．Fe2ScO4中心离子的平均电子结构近似为,A位Sc：Sc+3s23p43d2和B位Fe：Fe2+t2g3"eg2"t2g#． 通过分析,预测Fe2ScO4比Fe3O4和FeSc2O4具有更大的室温磁电阻以[Ca24Al28O64]4+·4O-(C12A7-O-)为催化剂,在流动反应器中研究了苯羟基化合成苯酚的转化率以及苯酚的选择性．苯的转化率随反应温度增加而增加,苯酚的选择性与温度及反应物的组成有关．此外还通过XRD、EPR和FT-IR对催化剂的结构,表面及内部物种进行了考察．结果表明,C12A7-O-的电正性骨架结构在反应前后几乎没有任何差别,样品内部有部分O-和O2-在反应后转化为OH-．中性物种及负离子中间体分别由Q-MS和TOF-MS所检测．     

二氧化硅-十八烷基三氯硅烷界面的和频光谱相位测量及其测量精度分析

相位测量和频振动光谱(SFG)可以获得物质表面分子取向等信息,但在实验重复性、实验设计和界面分析等方面仍有一些关键问题没有解决.相位误差会引起光谱变化并误导界面结构分析,因此分析并准确控制误差是相位测量S FG的关键技术.使用z-切石英作为相位标准,测量了修饰在熔融石英基底上的十八烷基三氯硅烷(OTS)在C—H振动波段...     

基于形状特征的水声图像小目标识别方法

为了抑制背景噪声,提高目标识别准确率,该文提出一种基于形状特征的水声图像小目标识别方法.对含有目标的水声图像进行非局部均值去噪处理后,使用OTSU算法自适应选取阈值对去噪图像进行二值化分割,结合形态学处理获得分割后的目标区域;提取目标区域的矩形度、圆形度、几何不变矩等各项形状参数,将目标的特征向量输入随机森林分类器实现...     

缪轻子反常磁矩和北京谱仪实验

最新的缪子反常磁矩实验测量结果与标准模型理论预言偏离4.2σ,提供了新物理存在的重要证据。然而要确认新物理的存在,实验和理论还需要进一步提高精度。运行在量子色动力学微扰与非微扰过渡能区的北京谱仪实验能够约束缪子反常磁矩理论计算中最重要的误差来源——强相互作用的修正。文章介绍了缪子反常磁矩的实验与理论现状,特别是北京谱仪实验上相关的研究成果,并展望了未来缪子反常磁矩的实验测量与理论计算。     

ICF微靶装配体图像清晰度评价方法

现有图像清晰度评价函数在对ICF微靶装配体图像的评价过程中,由于不同零件图像对比度的变化,评价函数极易失去其理想曲线特性,导致聚焦精度降低甚至调焦过程失败。针对这一问题,在分析离焦图像模糊原因的基础上,得出离焦图像究其本质为图像结构变化失真。利用Zernike矩具有描述图像结构特征这一特性,提出一种新的针对微靶装配体图像的清晰度评价函数。评价函数由图像不同阶Zernike矩的线性组合构成,通过调节权重系数来实现微靶装配体不同零件图像的清晰度评价。实验结果表明:相较于现有的评价函数,新的评价函数在对微靶装配体图像评价过程中保持了函数的理想曲线特性,尤其是针对低对比度图像,其在灵敏度和抗噪性方面具有明显优势。     

Aircraft recognition based on improved iterative threshold selection and skeleton Zernike moment

Using the visible optics images to identify targets is an important part in the development of remote sensing technology. In this paper, a new aircraft recognition method based on the improved iterative threshold selection and the skeleton Zernike moment is presented. The method segment aircraft targets under complex background using iterative threshold selection with between-class distance and scatter, and calculate the skeleton Zernike moment for the aircraft target recognition using template matching method. The experimental results show that the new method can effectively achieve the target segmentation under complex backgrounds, and provide a satisfactory performance both in recognition rate and recognition speed.     

400GeV/c pp碰撞集束参数和集束关联子数据

利用400GeV/c pp碰撞产生的带电粒子赝快度分布和方位角分布计算了集束参数和集束关联子.结果表明集束参数不仅依赖于多重数分布的宽度,还与粒子间的关联有关.在赝快度和方位角空间,集束参数_η 2(M)和η₃ (M)遵循同样的幂律行为,表明在400GeV/c　pp碰撞多重产生中多重分形行为被观测到了.在赝快度空间二级集束关联子依赖于多重数分布的宽度,并随子区间之间的距离的增加而减小.     

Semicovariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and Other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality

Complex modeling has received significant attention in recent years and is increasingly used to explain statistical phenomena with increasing and decreasing fluctuations, such as the similarity or difference of spike protein charge patterns of coronaviruses. Different from the existing covariance or correlation coefficient methods in traditional integer dimension construction, this study proposes a simplified novel fractional dimension derivation with the exact Excel tool algorithm. It involves the fractional center moment extension to covariance, which results in a complex covariance coefficient that is better than the Pearson correlation coefficient, in the sense that the nonlinearity relationship can be further depicted. The spike protein sequences of coronaviruses were obtained from the GenBank and GISAID databases, including the coronaviruses from pangolin, bat, canine, swine (three variants), feline, tiger, SARS-CoV-1, MERS, and SARS-CoV-2 (including the strains from Wuhan, Beijing, New York, German, and the UK variant B.1.1.7) which were used as the representative examples in this study. By examining the values above and below the average/mean based on the positive and negative charge patterns of the amino acid residues of the spike proteins from coronaviruses, the proposed algorithm provides deep insights into the nonlinear evolving trends of spike proteins for understanding the viral evolution and identifying the protein characteristics associated with viral fatality. The calculation results demonstrate that the complex covariance coefficient analyzed by this algorithm is capable of distinguishing the subtle nonlinear differences in the spike protein charge patterns with reference to Wuhan strain SARS-CoV-2, which the Pearson correlation coefficient may overlook. Our analysis reveals the unique convergent (positive correlative) to divergent (negative correlative) domain center positions of each virus. The convergent or conserved region may be critical to the viral stability or viability; while the divergent region is highly variable between coronaviruses, suggesting high frequency of mutations in this region. The analyses show that the conserved center region of SARS-CoV-1 spike protein is located at amino acid residues 900, but shifted to the amino acid residues 700 in MERS spike protein, and then to amino acid residues 600 in SARS-COV-2 spike protein, indicating the evolution of the coronaviruses. Interestingly, the conserved center region of the spike protein in SARS-COV-2 variant B.1.1.7 shifted back to amino acid residues 700, suggesting this variant is more virulent than the original SARS-COV-2 strain. Another important characteristic our study reveals is that the distance between the divergent mean and the maximal divergent point in each of the viruses (MERS > SARS-CoV-1 > SARS-CoV-2) is proportional to viral fatality rate. This algorithm may help to understand and analyze the evolving trends and critical characteristics of SARS-COV-2 variants, other coronaviral proteins and viruses.     

Magnetic moment predictions of odd-A nuclei with the Bayesian neural network approach

The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei. The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square (rms) deviations from data, i.e., 0.949 \begin{document}$ \mu_\mathrm{N} $\end{document} and 1.272 \begin{document}$ \mu_\mathrm{N} $\end{document} for odd-neutron nuclei and odd-proton nuclei, respectively. By including the dependence of the nuclear spin and Schmidt magnetic moment, the machine-learning approach precisely describes the magnetic moments of odd-A nuclei with rms deviations of 0.036 \begin{document}$ \mu_\mathrm{N} $\end{document} for odd-neutron nuclei and 0.061 \begin{document}$ \mu_\mathrm{N} $\end{document} for odd-proton nuclei. Furthermore, the evolution of magnetic moments along isotopic chains, including the staggering and sudden jump trend, which are difficult to describe using nuclear models, have been well reproduced by the Bayesian neural network (BNN) approach. The magnetic moments of doubly closed-shell \begin{document}$ \pm1 $\end{document} nuclei, for example, isoscalar and isovector magnetic moments, have been well studied and compared with the corresponding non-relativistic and relativistic calculations.