个性化公开密钥系统的设计

论文提出利用用户提供的个性化信息来随机产生关联大素数,保证了素数选取的随机化、个性化,提高了RSA算法的安全性。     

频谱分解技术在塔里木盆地北部TH地区碳酸盐岩缝洞型储层预测中的应用

本文从频谱分解技术的基本原理及计算方法入手,根据正演模型,通过对比缝洞型储层与地震反射特征的关系,总结出储层发育的反射特征,相应地精细调整频谱分解处理参数,进而进行预测。通过与钻井资料对比可知,频谱分解技术可以刻画不同频率强反射、弱反射的边界。     

Mesh结构P/G布线网络层次化快速分析方法

本文提出了一个基于网络划分的P／G布线网络层次化快速分析方法。其中，对于子网运算，通过对Cholesky分解法三角化对称正定阵的图模型分析，并基于Mesh结构网络的自身特点，提出了一个基于图顶点排序的加速子网分析运算策略；并用基于MPI的并行结构实现了P／G布线网络分析的并行运算。     

用于快速P/G网分析的Cholesky分解法图模型

通过对Cholesky分解法求解线性方程组的分析，建立Cholesky分解法三角化对称正定阵的图模型，并基于该模型及Mesh结构P／G网络的自身特点，提出一个P／G网快速分析算法．实验证明，该算法能大大降低Mesh结构P／G网络的分析运算时间和内存占用．     

On the solvability of the positive real lemma equations

In this paper, we consider the classical equations of the positive real lemma under the sole assumption that the state matrix A has unmixed spectrum: σ(A)∩σ(−A)=. Without any other system-theoretic assumption (observability, reachability, stability, etc.), we derive a necessary and sufficient condition for the solvability of the positive real lemma equations.     

Eigensolution of specially structured matrices with hyper‐symmetry

Recently, four canonical forms have been developed and applied to the dynamics and stability analysis of symmetric frames. In this paper, hyper‐symmetric matrices and specially structured matrices are defined and efficient methods are proposed for the eigensolution of such matrices. Applications are extended to hyper‐graphs and specially structured graphs. Simple methods are developed for calculating the eigenvalues of the Laplacian matrices of such graphs. The developments presented in this paper can also be considered as generalization of Form II and Form III symmetry, previously defined by the authors. Copyright © 2006 John Wiley & Sons, Ltd.     

A polynomial factorization approach to the discrete time GI/G/1/(N) queue size distribution

The queue of a single server is considered with independent and identically distributed interarrivai and service times and an infinite (GI/G/1) or finite (GI/G/1/N) waiting room. The queue discipline is non-preemptive and independent of the service times.

A discrete time version of the system is analyzed, using a two-component state model at the arrival and departure instants of customers. The equilibrium equations are solved by a polynomial factorization method. The steady state distribution of the queue size is then represented as a linear combination of geometrical series, whose parameters are evaluated by closed formulae depending on the roots of a characteristic polynomial.

Considering modified boundary constraints, systems with finite waiting room or with an exceptional first service in each busy period are included.     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

结构正则化多视图非负矩阵分解

现存非负矩阵分解（non-negative matrix factorization,NMF）研究多考虑单一视图分解数据,忽略了数据信息的全面性。此外,NMF限制其获取数据的内在几何结构。针对以上问题,提出一个结构正则化多视图非负矩阵分解算法（structure regularized multi-view nonnegative matrix factorization,SRMNMF）。首先,通过主成分分析来对数据进行全局结构的判别式学习;其次,利用流形学习来捕获数据的局部结构;然后,通过利用多视图数据的多样性和差异性来学习表征。模型提升了算法聚类的整体性能,更加有效地挖掘数据的结构信息。此外,采用高效的交替迭代算法优化目标函数得到最优的因子矩阵。在六个数据集上与现存的代表性方法比较,所提出的SRMNMF的准确率、NMI和Purity分别最大提高4.4%、6.1%和4.05%。     

融合信任关系与评论文本的矩阵分解推荐算法

针对推荐系统中用户评分数据稀疏所导致推荐结果不精确的问题,本文尝试将用户评分、信任关系和项目评论文本信息融合在概率矩阵分解方法中以缓解评分数据稀疏性问题.首先以共同好友数目及项目流行度改进皮尔逊用户偏好相似程度并将其作为用户间的直接信任值,然后考虑用户间信任传播过程中所有路径的影响构建新的信任网络;其次通过BERT预训练(Pre-training of Deep Bidirectional Transformers for Language Understanding)模型提取项目的评论文本向量,构造项目的评论文本特征矩阵;最后基于概率矩阵分解(Probabilistic Matrix Factorization,PMF)模型融合用户的评分数据、用户的信任关系以及项目的评论文本信息进行推荐.通过不断的理论分析并在真实的Yelp数据集上进行实验验证,均表明本文算法的有效性.