基于L~1度量分析的图象分解与重构算法

Mallat‘s decompositon and reconstruction algorithms are very important in the the field of wavelet theory and its applications to signal processing.Wavelet Anal-ysis,which is based on L^2(R) space,can eliminate redundancy of signals with the help of orthogonality and characterize the processing precision with the meansquare error.In the recent years,it is understood that the mean square measuredoes not match human visual sensitivity well.From the point of view,R.DeVore studied L^1 measure instead.Similarly,considering the principles of image com-pression,Yang introduced and dealt with orthogonality in L^1 space based on thebest approximation theory,and consequently established the corresponding decom-position and reconstruction algorithms for signals.In this paper,error analyses for the algorithms above are taken and the selection of the best parameters in the algorithms are discussed in detail.Finally,the algorithms are compared with the classical Haar and Daubechies‘‘s orthogonal wavelets based on the singal-to-noiseratio data computed.     

B(X)上保持算子拟仿射性或值域稠性的线性映射

设X和y是无限维的复Banach空间,φ是从B(X)到B(y)保单位的线性满射.本文证明了φ双边保算子的拟仿射性当且仅当φ为同构或反同构;φ双边保算子的值域稠性当且仅当φ是同构.     

单圈图的最大特征值的上界的改进

Let G（V, E） be a unicyclic graph, Cm be a cycle of length m and Cm G, and ui ∈ V（Cm）. The G - E（Cm） are m trees, denoted by Ti, i = 1, 2,..., m. For i = 1, 2,..., m, let eui be the excentricity of ui in Ti and ec = max{eui ： i = 1, 2 , m}. Let κ = ec＋1. Forj = 1,2,...,k- 1, let δij = max{dv ： dist（v, ui） = j,v ∈ Ti}, δj = max{δij ： i = 1, 2,..., m}, δ0 = max{dui ： ui ∈ V（Cm）}. Then λ1（G）≤max{max 2≤j≤k-2 （√δj-1-1＋√δj-1）,2＋√δ0-2,√δ0-2＋√δ1-1}. If G ≌ Cn, then the equality holds, where λ1 （G） is the largest eigenvalue of the adjacency matrix of G.     

有限个渐近伪压缩映射近迫点序列的收敛性

研究了Banach空间中有限个渐近伪压缩映射近迫点序列的收敛性问题,此结果推广了以前的结论.     

A kinematic formula for integral invariant of degree 4 in real space form

In this paper,kinematic formulae in a real space form are investigated.A kinematic formula for homogeneous polynomial of degree 4 on the second fundamental forms in a real space form is obtained.The formula obtained is a concrete form of the result of Howard and the analogue of the known formula of Chen and Zhou.     

矩阵代数收敛至球面与非交换度量几何

介绍了Rieffel定义的紧致量子度量空间与量子Gromov-Hausdorff距离和近来Latrémolière定义的量子Gromov-Hausdorff邻距,分别讨论了矩阵代数如何在这两种量子距离下收敛至球面.     

I(L)-拓扑空间中的可数性、分离性与仿紧性

Kubiak^[9]与王戈平^[2]分别独立地引进了诱导I(L)-拓扑空间概念。本文用在[1]定义的L-拓扑空间的可数性、分离性与仿紧性等来刻画由它诱导的I(L)-拓扑空间的相应的这些性质。     

逆极限空间的伪轨跟踪性

证明了对于由{xi,φi,fi}∞i=l生成的逆极限系统(X∞,f∞),如果每个fi具有伪轨跟踪性,则诱导映射f∞也具有伪轨跟踪性.并构造了一个例子说明它的逆命题不成立.还证明了零维紧致度量群的自同构拓扑共轭于一族有限型子转移生成的逆极限系统.     

基于改进Adaptive Lasso的多工序制造过程关键质量特性识别

为解决多工序制造过程关键质量特性识别中存在的质量特性间具有多重相关性以及数据高维度, 小样本等问题,本文采用主成分回归改进Adaptive Lasso方法并融合状态空间思想和Bootstrap方法实现多工序过程关键质量特性识别。首先引入状态空间思想构建多工序过程关键质量特性识别模型,然后利用Bootstrap方法重构样本,扩大样本量;进而采用改进Adaptive Lasso方法识别关键质量特性,并通过仿真验证改进Adaptive Lasso方法与Lasso,Adaptive Lasso和岭回归方法在质量特性间不同相关度下识别的有效性;最后通过实例说明改进Adaptive Lasso的具体应用过程,仿真及实例结果显示,改进Adaptive Lasso方法对多工序过程有良好的关键质量特性识别能力,特别当质量特性间有较强相关性时显著优于其它两种方法。     

NUMERICAL ANALYSIS OF QUASICONFORMAL MAPPINGS BY CIRCLE PACKINGS

Thurston proposed that conformal mappings can be approximated by circle packing isomorphisms and the approach can be implemented efficiently. Based on the circle packing methods the rate of convergence of approximating solutions for quasiconformai mappings in the plane is discussed.