基于凸分解与OBB层次结构的碰撞检测方法

目前的碰撞检测方法大部分是基于简单的包围盒方法和简单的搜索算法的，这种算法精确度低且效率不高。基于凸分解与OBB层次结构的碰撞检测方法是对传统碰撞检测算法的一种改进，该方法继承了传统碰撞检测算法的优点，同时又对传统算法进行了必要的改进。实验证明，利用物体表面凸分解的方法解决了传统碰撞检测算法不能测试非凸物体相交的问题，拓宽了碰撞检测算法的应用范围；根据物体前后碰撞点的相关性，运用加速搜索提高了碰撞检测效率，降低了算法复杂度。     

基于GA 的系统分解协调算法Agen t实现

为解决实际工程问题，将Agent的设计思想引入分解协调算法，实现了大系统的分解和生产单元之间的协作，并采用GA算法进行参数协调。改进后的分解协调算法具有较好的适用性。     

改进的椭圆曲线数字签名方案及实例分析

与传统的公钥算法进行比较，椭圆曲线密码学(ECC)在基于有限域上离散对数问题或者大整数分解问题等方面具有明显的优势。以椭圆曲线为基础，提出改进的数字签名方案，并对改进方案的安全性和复杂度进行分析。改进方案对生日攻击具有较高的防御性，提高了数字签名的安全性能，且运算量不大。通过MATLAB实例验证了该算法的正确性。     

Series-parallel图的Δ-SP算法的实现

讨论了series-parallel图和它的二叉分解树，对S－P图的Δ－SP画法进行了详细的描述，给出了该算法的实现过程，并且对S－P图的分解树建树算法进行了改进。最后，通过一个具体实例尝示了该画法的结果。     

基于GA的心电信号稀疏分解MP算法改进

基于遗传算法(GA)的信号稀疏分解算法运算量较大。为解决该问题,提出一种基于 GA 的心电信号匹配追踪改进算法。结合心电信号的特征,根据信号特征波形建立窗函数,将信号分为能量集中和稀疏部分,分别采用不同的算法流程和参数。实验结果表明,该改进算法的运算量较原算法降低了1/3,能提高心电信号稀疏分解的运算速度和压缩处理性能。     

改进小波包分析在图像噪声处理中的应用

针对图像消噪问题,提出了改进小波包分析算法.该算法对小波包分解和重构算法进行了改进,通过对小波包分解系数的重新排序,解决了由于隔点采样而引起的频带混叠问题,并对小波消噪原理进行了深入分析.通过对图像的消噪处理,该算法消噪效果优于二维小波变换和小波包分析算法,具有更为广泛的应用价值.     

一种新的改进的加权k-核分解方法

k-核分解算法是一种优秀的评估复杂网络节点重要性的方法,然而该方法对于复杂网络节点的排序还存在一些问题。本文提出了一种改进的加权k-核分解算法,通过改进节点加权度的计算对已提出的方法进行改进。然后在四个真实网络上利用SIR传染病模型进行了实验仿真。实验结果表明,改进后的算法比原有方法在评估节点重要性方面更具有优越性。     

利用FFT实现语音信号稀疏分解的改进算法

研究基于Matching Pursuit（MP）方法实现的语音信号稀疏分解问题,通过对语音信号稀疏分解中使用的过完备原子库结构特性的分析,提出了一种改进的信号稀疏分解算法。该算法针对语音信号的特点,以FFT算法实现的稀疏分解为基础缩小了原子的搜索范围,从而不仅进一步提高分解速度,还能以更稀疏的形式表示语音信号。算法的有效性为实验结果所证实。     

基于LFM矩阵分解的推荐算法优化研究

在推荐系统中,基于矩阵分解的推荐算法是目前的研究热点之一,然而普通矩阵分解算法的推荐精确度偏低,为了改善该问题,以矩阵分解算法中的潜在因子模型（LFM）优化为研究对象,分析LFM中两种基础推荐算法在寻优速率与推荐精度上的不足,然后提出两种改进算法：带冲量的批量学习算法和混合学习算法,最后通过实验数据测试,对比了不同算法的推荐效果,结果证明改进算法的性能更优。     

动态截断二进制指数后退冲突分解算法的研究

传统的截断二进制指数后退冲突算法解决冲突问题时,每次都将初始窗口设为2个时隙,而不管可能存在的冲突端数.已有学者基于已知冲突端口数而提出了一种动态改进算法,然而确定冲突端口数是很困难的.针对上述问题,本文提出了一种基于概率的动态设置初始窗口的改进算法,仿真实脸表明该算法是能有效降低冲突分解次数和分解时隙.     

Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm

The present study investigates the cost concerns of distribution centers and formulates a vehicle routing problem with time window constraints accordingly. Based on the embedded structure of the original problem, a decomposition technique is employed to decompose the original problems to a clustering problem (main problem) and a set of traveling salesman problems (sub-problems) with time window constraints. This decomposition not only reduces the problem size but also enable the use of simpler solution procedures. A genetic algorithm is developed to solve the clustering problem, while a simple heuristic algorithm is formulated to solve the set of traveling salesman problems. The solution of the original problem is obtained through iterative interactions between the main problem and the set of sub-problems. The performance of the proposed approach is compared with the well-known insertion method and a manual scheduling of a distribution center.     

An augmented Lagrangian decomposition method for quasi-separable problems in MDO

Several decomposition methods have been proposed for the distributed optimal design of quasi-separable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new decomposition algorithm for quasi-separable MDO problems. In particular, we propose a decomposed problem formulation based on the augmented Lagrangian penalty function and the block coordinate descent algorithm. The proposed solution algorithm consists of inner and outer loops. In the outer loop, the augmented Lagrangian penalty parameters are updated. In the inner loop, our method alternates between solving an optimization master problem and solving disciplinary optimization subproblems. The coordinating master problem can be solved analytically; the disciplinary subproblems can be solved using commonly available gradient-based optimization algorithms. The augmented Lagrangian decomposition method is derived such that existing proofs can be used to show convergence of the decomposition algorithm to Karush–Kuhn–Tucker points of the original problem under mild assumptions. We investigate the numerical performance of the proposed method on two example problems.     

经验模式分解回顾与展望

经验模式分解EMD打破了Fourier变换、小波分解等传统数据分析方法需要预先设定基函数的局限,是一种完全由数据驱动的自适应非线性非平稳时变信号分解方法,可以将数据从高频到低频分解成具有物理意义的少数几个固有模态函数分量和一个余量。首先介绍了原始EMD方法的原理和算法;接着,总结归纳了EMD当前的研究现状,分析了EMD存在的端点效应、模态混叠、运行速度问题及其在二维情况下的问题并对国内外学者解决这些问题的方法进行了概述和比较;最后结合EMD研究存在的难题指出了EMD进一步研究与应用的发展方向。     

Improved group-theoretical method for eigenvalue problems of special symmetric structures,using graph theory

Group-theoretical methods for decomposition of eigenvalue problems of skeletal structures with symmetry employ the symmetry group of the structures and block-diagonalize their matrices. In some special cases, such decompositions can further be continued. This particularly happens when submatrices resulted from the decomposition process, correspond to substructures with new symmetrical properties which are not among the properties of the original structure. Thus, a group-theoretical method is not able to recognize such additional symmetry from the original problem. In this paper, an algorithm is presented based upon a combination of group-theoretical ideas and graph-methods. This algorithm identifies the cases where the structure has the potential of being further decomposed, and also finds the symmetry group, and subsequently the transformation which can further decompose the system. It is also possible to find out when the block-diagonalization is complete and no further decomposition is possible. This is particularly useful for large eigenvalue problems such as calculation of the buckling load or natural frequencies of vibrating systems with special symmetries.     

Method of sequential modification of the objective function in the assignment problem

A new method for solving the assignment problem based on the decomposition of the original problem into a number of two-dimensional optimization problems is proposed. The integer-valuedness and monotonicity of the iterative procedure with respect to the objective function ensures that the algorithm is finite. As a result, either a unique optimal solution of the original assignment problem or a system of constraints is obtained. In the latter case, the system of constraints can be used to obtain all optimal solutions. Examples illustrating the algorithm are discussed.     

基于Laplacian金字塔和小波变换的医学CT图像增强算法

对医学图像进行增强可提高信息的利用率。传统的图像增强方法应用于医学图像时处理效果一般,存在诸多问题,如在增强图像的同时使图像的细节丢失,减弱了图像中目标的边缘信息,降低了图像的对比度。针对上述问题,提出一种基于小波变换和Laplacian金字塔分解的图像增强算法。首先,对原医学图像进行小波变换分解,得到处理结果;然后,对原医学图像进行Laplacian金字塔分解,得到医学图像的高频信息;最后,利用小波变换的结果和Laplacian金字塔分解的结果进行重构,得到增强后的图像。实验结果表明,该方法的增强效果明显优于传统的图像增强算法,对医学图像具有较好的增强效果,同时能更好地抵抗噪声。     

基于Arnold置乱和小波包分解的自适应水印算法

针对小波变换的不足,根据原始图像各子块对水印信息的适应程度不同,提出一种基于Arnold置乱和小波包分解的自适应水印算法。首先,该算法采用Arnold变换对水印图像进行预处理,然后对原始图像进行小波包分解,小波包分解能够提供一种更为精细的分解方法,将频带进行了多层次的划分,最后将水印图像嵌入到小波包分解后的子带中,水印的嵌入强度和嵌入位置均根据原始图像的内容自适应地决定,这样很好地解决了水印鲁棒性和不可见性之间的矛盾。仿真实验结果表明,算法对常见的图像攻击具有较强的鲁棒性和稳健性。     

Large-scale convex optimal control problems: time decomposition,incentive coordination, and parallel algorithm

A parallel algorithm based on time decomposition and incentive coordination is developed for long-horizon optimal control problems. This is done by first decomposing the original problem into subproblems with shorter time horizon, and then using the incentive coordination scheme to coordinate the interaction of subproblems. For strictly convex problems it is proved that the decomposed problem with linear incentive coordination is equivalent to the original problem, in the sense that each optimal solution of the decomposed problem produces one global optimal solution of the original problem and vice versa. In other words, linear incentive terms are sufficient in this case and impose no additional computation burden on the subproblems. The high-level parameter optimization problem is shown to be nonconvex, despite the uniqueness of the optimal solution and the convexity of the original problem. Nevertheless, the high-level problem has no local minimum, even though it is nonconvex. A parallel algorithm based on a prediction method is developed, and a numerical example is used to demonstrate the feasibility of the approach     

Frequency decomposition technique for large archivable databases

A frequency decomposition technique is suggested for very large archivable databases. Modern large databases intended for use in a highly interactive and responsive environment face serious performance and availability problems caused by prolonged archiving and back up procedures. The suggested technique solves the problem by splitting the original database into a collection of smaller independently archivable ones. The decomposition pattern is based on the frequency separation criterion and provides a flexible and economical way of achieving a needed degree of database recoverability and archivability. The principles of database frequency decomposition are presented and discussed in detail. A numerical algorithm is suggested to allocate resources needed for implementation of the frequency decomposition technique. The quantitative analysis of the algorithm is illustrated by a practical example of large file management system decomposition. This paper also presents an adaptive procedure that controls growth and dynamic restructuring of the decomposed structure. Necessary modifications in database logical organization and access mechanisms are considered as well.     

Multi-index transport problems with decomposition structure

Consideration was given to the multi-index problems of linear and integer linear programming of the transport type. An approach based on the study of reducibility of the multi-index transport problems to that of seeking a flow on the network was proposed. For the multi-index problems with decomposition structure, a reduction scheme enabling one to solve the original multi-index problem using the cyclic decomposition of the minimum-cost flow of the auxiliary flow problem was constructed. The developed method underlies the heuristic algorithm to solve the NP-hard integer multi-index problem with a system of constraints featuring decompositional properties and general cost matrix.