基于宇称-时间对称光子晶体结构的磁场传感器研究

提出了一种基于宇称-时间(parity-time,PT) 对称结构的磁场检测微腔模型,利用水 基Fe₃O₄磁流体介质层的磁光效应和PT对称结构的光放大效应,当磁场强度发生变化时 ,引 起结构禁带中缺陷模位置的改变。因此,可以通过检测缺陷模位置实现磁场强度的检测。本 文利用传递矩阵法(transfer matrix method,TMM)从理论上分析了结构的透射光谱,在此 基础上对结构周期、介质层厚度、PT对称单元中宏观洛伦兹振荡强度等参数进行了数值优化 。优化后的仿真结果表明,该传感器能够实现有效的磁场磁强检测,磁场强度在0—500Oe 的 范围内,传感器的灵敏度可以达到97.14 nm/RIU,优值和检测极限分 别可以达到10⁴和10－7 RIU。同时, 传感器的分辨率数值最小可以达到0.05 Oe和最 大可以达到2Oe。本文所设计的传感器结构可用于高综合性能磁场强度检测设备的设计。     

谐振器加载石墨烯的双通带可调滤波衰减器设计

石墨烯材料在微波段的阻抗可控特性是其最有价值的应用特性之一。文中首次分析了石墨烯对微带 多模谐振的影响机理,提出了一种集成滤波衰减功能为一体的灵活可控器件。首先,对不同位置加载石墨烯的微带双 模谐振器进行了严格的等效电路建模,通过奇偶模分析法及输入导纳参数计算,探究了双模谐振受石墨烯阻抗及其加 载位置的影响,明晰了石墨烯材料对微波谐振器的影响。随后设计了动态可调的滤波衰减器并阐述了石墨烯材料在谐 波控制上的潜在应用点。最后,采用不同尺寸的双模谐振器进行石墨烯加载并完成双通带幅度独立可控的滤波衰减器 的设计,仿真与实测结果吻合良好,验证了阻抗可控石墨烯材料在可调微波衰减类器件上的应用前景。     

图像分类问题的几种深度学习策略

针对图像分类学习不够深入的问题,提出图像分类问题的几种深度学习策略研究。通过分析当前主流的主动深度学习图像、多标签图像和多尺度网络图像三种深度学习方法的工作原理和存在的优势与不足,探讨图像分类问题的优化学习策略。随后采用图像分类问题的几种深度学习策略实验的方式加以对比,实验结果表明,参数共享的深度学习图像分类方法不仅提高了预测速度,而且还能确保模型的准确性。     

一种增强型PTP光纤级联精细时频同步方法

该文提出了一种增强型PTP光纤级联精细时频同步方法,该方法以PTP同步技术为基础,结合同步以太网时钟传递技术和基于数字双混频时差法的多级级联精细时钟同步技术,对PTP技术进行改进和增强,然后基于该方法,通过多级时频设备光纤级联的形式实现多节点、大跨度、网络化的时频信号传递与同步输出,并解决多级级联情况下同步精度会逐级恶化的问题,实现ns量级的系统时间同步精度,保证系统各环节在高度统一的时间尺度下进行高效同步与联动工作。通过设计、试验,验证了该方法的可行性和有效性。     

(CoFe)Se2@NC超级电容器电极材料的制备及其电化学性能

金属-有机框架(MOF)衍生的过渡金属硒化物和多孔碳纳米复合材料具有巨大的储能优势,是应用于电化学储能的优良电极材料。采用共沉淀法制备CoFe类普鲁士蓝(CoFe-PBA)纳米立方,并通过静电组装在CoFe-PBA上包覆聚吡咯(PPy)得到CoFe-PBA@PPy;通过在400℃氮气中退火并硒化成功制备了氮掺杂的碳(NC)包覆(CoFe)Se₂的(CoFe)Se₂@NC纳米复合材料,并对其结构和形貌进行了表征。以(CoFe)Se₂@NC为电极制备了超级电容器,测试了其电化学性能,结果表明,在电流密度1 A/g时超级电容器的比电容达到1047.9 F/g,在电流密度5 A/g下1000次循环后具有良好的循环稳定性和96.55%的比电容保持率。由于其性能优越、无毒、成本低和易于制备,未来(CoFe)Se₂@NC纳米复合材料在超级电容器中具有非常大的应用潜力。     

关于模拟法描会聚点电场的讨论

本文论证了“ＱＥ－２”型静电场描迹中模拟聚焦电场的电极在设计上有误的观点。     

Turán's Pure Power Sum Problem

Let be complex numbers, and consider the power sums , . Put , where the minimum is over all possible complex numbers satisfying the above. Turán conjectured that , for some positive absolute constant. Atkinson proved this conjecture by showing . It is now known that , for . Determining whether or approaches some other limiting value as is still an open problem. Our calculations show that an upper bound for decreases for , suggesting that decreases to a limiting value less than as .

     

Optimal error estimates of a locally one-dimensional method for the multidimensional heat equation

For the multidimensional heat equation in a parallelepiped, optimal error estimates inL ₂(Q) are derived. The error is of the order of +¦h¦² for any right-hand sidef L ₂(Q) and any initial function ; for appropriate classes of less regularf andu ₀, the error is of the order of ((+¦h¦² ), 1/2<1.Translated fromMatematicheskie Zametki, Vol. 60, No. 2, pp. 185–197, August, 1996.     

A preconditioner for constrained and weighted least squares problems with Toeplitz structure

We study methods for solving the constrained and weighted least squares problem min _x by the preconditioned conjugate gradient (PCG) method. HereW = diag (₁, , _m ) with _{1 m} 0, andA ^T = [T ₁ ^T , ,T _k ^T ] with Toeplitz blocksT _l R ^{n × n} ,l = 1, ,k. It is well-known that this problem can be solved by solving anaugmented linear 2 × 2 block linear systemM +Ax =b, A ^T = 0, whereM =W ^–1. We will use the PCG method with circulant-like preconditioner for solving the system. We show that the spectrum of the preconditioned matrix is clustered around one. When the PCG method is applied to solve the system, we can expect a fast convergence rate.Research supported by HKRGC grants no. CUHK 178/93E and CUHK 316/94E.     

The Padé-Rayleigh-Ritz method for solving large hermitian eigenproblems

We make use of the Padé approximants and the Krylov sequencex, Ax,,...,A ^m–1 x in the projection methods to compute a few Ritz values of a large hermitian matrixA of ordern. This process consists in approaching the poles ofR _x()=((I–A)^–1 x,x), the mean value of the resolvant ofA, by those of [m–1/m]_Rx(), where [m–1/m]_Rx() is the Padé approximant of orderm of the functionR _x(). This is equivalent to approaching some eigenvalues ofA by the roots of the polynomial of degreem of the denominator of [m–1/m]_Rx(). This projection method, called the Padé-Rayleigh-Ritz (PRR) method, provides a simple way to determine the minimum polynomial ofx in the Krylov subspace methods for the symmetrical case. The numerical stability of the PRR method can be ensured if the projection subspacem is sufficiently small. The mainly expensive portion of this method is its projection phase, which is composed of the matrix-vector multiplications and, consequently, is well suited for parallel computing. This is also true when the matrices are sparse, as recently demonstrated, especially on massively parallel machines. This paper points out a relationship between the PRR and Lanczos methods and presents a theoretical comparison between them with regard to stability and parallelism. We then try to justify the use of this method under some assumptions.