A Gaussian RBFs method with regularization for the numerical solution of inverse heat conduction problems

In this paper, a recursion numerical technique is considered to solve the inverse heat conduction problems, with an unknown time-dependent heat source and the Neumann boundary conditions. The numerical solutions of the heat diffusion equations are constructed using the Gaussian radial basis functions. The details of algorithms in the one-dimensional and two-dimensional cases, involving the global or partial initial conditions, are proposed, respectively. The Tikhonov regularization method, with the generalized cross-validation criterion, is used to obtain more stable numerical results, since the linear systems are badly ill-conditioned. Moreover, we propose some results of the condition number estimates to a class of positive define matrices constructed by the Gaussian radial basis functions. Some numerical experiments are given to show that the presented schemes are favourably accurate and effective.     

Identification of diffusion parameters in a non-linear convection–diffusion equation using adaptive homotopy perturbation method

This paper is concerned with the numerical identification of diffusion parameters in a non-linear convection–diffusion equation, which arises as the saturation equation in the fractional flow formulation of the two-phase porous media flow equations. In order to overcome the defect of the local convergence of traditional methods, an adaptive homotopy perturbation method is applied to solve this parameter identification inverse problem. The adaptive homotopy perturbation method provides a simple way to adapt computational refinement to the choice of the homotopy parameter. Numerical simulations illustrate that the proposed algorithm is globally convergent and computationallyefficient.     

Half supervised coefficient regularization for regression learning with unbounded sampling

In this study, we investigate the consistency of half supervised coefficient regularization learning with indefinite kernels. In our setting, the hypothesis space and learning algorithms are based on two different groups of input data which are drawn i.i.d. according to an unknown probability measure ρ _X . The only conditions imposed on the kernel function are the continuity and boundedness instead of a Mercer kernel and the output data are not asked to be bounded uniformly. By a mild assumption of unbounded output data and a refined integral operator technique, the generalization error is decomposed into hypothesis error, sample error and approximation error. By estimating these three parts, we deduce satisfactory learning rates with proper choice of the regularization parameter.     

第五代移动通信中双向中继技术研究

为了提高5G系统的频谱效率,满足移动业务流量增长的需求,本文对具有高网络吞吐量和频谱利用率的双向中继系统进行深入研究,从去噪转发的双向中继系统（2P-DNF-TWR）的模型建立入手,分析了该系统中继和源节点解码的误比特率,并对2P-DNF-TWR系统、解码转发系统和去噪转发系统的误比特率进行了仿真对比,实际仿真结果表明,随着信噪比的增大,去噪转发的双向中继系统的性能均优于2P-DNF-TWR系统和解码转发系统。     

基于二阶广义全变差的多帧图像超分辨率重建

图像超分辨率重建是图像处理领域的重要问题.本文将二阶广义全变差用于基于正则化的多帧图像超分辨率重建问题,构建了基于二阶广义全变差正则项的图像超分辨率模型.为了更好地保持重建图像的边缘和细节,采用图像空域自适应正则化参数,并针对该重建模型的非光滑性,给出了基于半二次正则化和交替方向法的求解算法.实验结果表明该模型和数值算法能够较好地提高图像的分辨率,同时可以较好地保持图像的细节信息.     

基于无噪后验错误矢量信号能量的变正则因子仿射投影算法

变正则因子技术是提高仿射投影自适应算法性能的重要方法之一.由于环境噪声的影响,现有的变正则因子自适应算法收敛速度较慢且稳态误差较大,各种测量、评估误差的存在进一步恶化了算法性能.为提高自适应算法的跟踪性能,本文在分析无噪先验错误矢量、无噪后验错误矢量和额外均方错误间关系的基础上,提出通过最小化无噪后验错误矢量信号能量来推导自适应变正则因子表达式的方法.在实践应用中,该方法利用了测量噪声的统计方差特性,并提出一种更加光滑且更加容易控制的指数缩放因子评估方法.系统辨识的仿真结果表明本文方法与传统的变正则因子方法以及变步长方法相比有更快的收敛速度与更低的稳态误差.     

混合蛙跳在网络编码协作系统资源分配中的应用

网络编码协作中继技术能合理分配IMT-Advanced的资源,从而提高系统性能,受到了广大学者的关注。针对基于网络编码的双向多中继协作系统,提出基于混合蛙跳的资源分配方案。以最小化系统传输功率为优化目标,在满足用户QoS下,推导每个节点的最优化功率,建立最优化资源分配模型,利用混合蛙跳算法解决系统资源分配问题。仿真结果表明,在满足单节点QoS下,所提出的基于混合蛙跳的资源分配方案能有效减小系统的总功率,提高了系统性能。     

自适应约束下的双边全变差正则化超分辨率重建

在经典的双边全变差( BTV)超分辨率重建中,加权系数和正则化参数的恒定性导致重建结果边缘保持能力受限。为此,提出了一种自适应约束的BTV正则化先验模型。算法首先定义了图像的局部邻域残差均值以区分当前像素属于平坦区域还是边缘区域;然后针对加权系数的不变性导致边缘削弱的问题,利用边缘方向和垂直边缘方向扩散性的不同,设计自适应权重矩阵;最后根据代价函数的极值问题推导出迭代公式,从而进行图像的超分辨率重建,重建过程中采用自适应的方法确定正则化参数,以便求得代价函数的全局最优解,提高了算法的鲁棒性。实验结果表明：与双三次线性插值法和经典BTV算法相比,该算法取得了更好的视觉效果和更高的峰值信噪比,更多地保留了图像的边缘细节信息。     

一种基于多传感器的红外图像正则化超分辨率算法

提出一种红外图像多传感器超分辨率重建算法。 算法存在两个关键点:一是有效利用两类图像的相关性;二是针对红外图像的特点利用其自 身信息 构造正则化模型。采用相位一致性算法提取可见光图像边缘,利用此边缘信息对正则化模型 加权,以 充分利用可见光和红外图像的相关性;将一阶梯度锐化算子引入总广义变分模型,构成针对 红外 图像特点的正则化模型;最后采用一阶主-对偶优化算法求得加权后模型的最优解。实验表 明,本文算法可获得边缘清晰的重建结果,并且有效抑制噪声,在主观视觉效果和客观评价 指标方面均优于其他算法。     

基于Tikhonov正则化方法的移动荷载识别实验研究

根据正交异性板理论和模态叠加法,推导了移动荷载作用下简支板桥的荷载识别方程。为了解决识别方程病态问题,考虑Tikhonov正则化方法,选择最优正则参数值,从而得到方程的正则化解。为了验证Tikhonov正则化方法的有效性,进行了简支板桥的移动荷载识别模型实验,通过实验确定不同速度车辆经过板桥时的应变响应历程,并将所测得数据代入识别方程求得车辆荷载信息。实验和计算结果对比表明,通过引进Tikhonov正则化方法可以获得识别方程的稳定解,提高识别精度,识别结果与实际小车重量吻合较好。