快速扫描机载气象雷达多普勒解模糊算法

本文针对机载气象雷达前视快速扫描下的多普勒模糊问题,提出了两种多普勒解模糊算法：基于改进方位向波束形成算法和基于阻塞矩阵算法。基于改进方位向波束形成算法在传统的Capon波束形成的基础上,利用空间谱积分的方法对非期望信号协方差矩阵进行重构和对导向矢量进行估计,进一步提高了算法的鲁棒性,降低方位角估计误差;基于阻塞矩阵算法需要估计导向矢量,并利用导向矢量计算阻塞矩阵,将接收信号进行多普勒模糊相消预处理,阻塞每个多普勒频点的模糊多普勒分量,以达到解模糊的目的。通过仿真实验证明,两种方法均可以有效地恢复信号的多普勒信息,其中基于阻塞矩阵算法与基于改进方位向波束形成算法相比,有更优良的性能,并有更小的运算量。     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

The assembly line worker assignment and balancing problem with stochastic worker availability

Assembly lines can be employed successfully in sheltered work centres to better include persons with disabilities in the labour market as well as to improve production efficiency. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). These assembly lines are characterised not only by a heterogeneous workforce, but also by high levels of absenteeism, which makes it more difficult to obtain stable and efficient line balancing solutions. In this paper, an extension of the ALWABP to minimise the expected cycle time under uncertain worker availability is proposed. We model this problem as a two-stage mixed integer program, and propose local search heuristics for solving it. Computational experiments show that stochastic modelling can help to improve the line’s efficiency and that the proposed heuristics produce good results for instances of practical size.     

一种新的大整数乘法算法

为解决超出计算机系统基本整数类型表达能力的整数(大整数)算术运算问题,以基础算法--大整数乘法为研究对象,根据大整数的表示形式与多项式表示形式上的相似性,结合大整数乘法进位与取模的特点,给出了一种关于大整数乘法的多项式算法.其方法与别的方法最大的不同是,虽然是求两个大整数乘法,但整个算法没有使用乘法,只是用加法运算而已...     

奇异值分解视频水印嵌入与检测及安全性测试

文章针对目前现有的基于小波变换和奇异值分解相结合的数字水印算法存在的缺陷,提出了一种新的视频水印算法:以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法,重点论述了奇异值分解的视频水印嵌入与检测以及安全性的测试.实验表明,提出的新算法具有较好的高透明性和鲁棒性.     

高脉冲重复频率雷达混合滤波解距离模糊方法

针对高脉冲重复雷达距离模糊问题,提出一种新的混合滤波解距离模糊方法.通过把脉冲间隔数和脉冲间隔变化量作为目标待估计状态,对离散的脉冲间隔数、间隔变化量和连续的目标状态(位置和速度)进行混合滤波,从而将解距离模糊问题转换为对脉冲间隔数的估计问题.初始时刻,由于距离量测模糊,利用多个高脉冲重复频率(HPRF)采用欧氏距离对混合状态进行初始化.在滤波过程中,为了避免门限选择,首先根据脉冲间隔变化量的有限个离散取值,将六维向量的混合滤波模型等效为有限个五维向量的混合滤波模型;然后对量测分别进行混合滤波;最后通过对各模型得到的新息的2范数进行比较获得混合状态估计,从而实现解距离模糊.混合滤波解距离模糊方法为实现高脉冲重复频率雷达实时解距离模糊提供了一种新的研究思路,仿真结果表明,本文提出的混合滤波方法在只有一个脉冲重复频率获得目标量测时较现有多假设方法收敛速度快,并且可以克服多假设方法在脉冲间隔数变化时发生的解模糊错误.     

基于秘密共享模数的一般性多方求逆协议

Catalano、Gennaro和Halevi提出了一个实用的基于秘密共享模数的分布式求逆协议,然而他们仅仅考虑了门限敌手结构的情况.文中考虑了一般敌手结构的情况,针对半诚实敌手和恶意敌手,利用Damgard和Thorbek提出的线性整数秘密共享方案,分别构造了一个多方模求逆协议.该协议在敌手结构是Q2(对应Q3以及强RSA假设)的条件下针对半诚实(对应恶意)敌手是安全的.该协议是Catalano等人方案的一个推广,可以用来分布式地计算RSA私钥以及构造标准模型下安全的分布式Gennaro-Halevi-Rabin、Cramer-Shoup和Mames-Joye签名方案.另外,文中的构造方法也是对环上的安全多方协议构造方法的一个有力补充.     

The distributed permutation flowshop scheduling problem

This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPFSP. Under this generalization, we assume that there are a total of F identical factories or shops, each one with m machines disposed in series. A set of n available jobs have to be distributed among the F factories and then a processing sequence has to be derived for the jobs assigned to each factory. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. This production setting is necessary in today's decentralized and globalized economy where several production centers might be available for a firm. We characterize the DPFSP and propose six different alternative mixed integer linear programming (MILP) models that are carefully and statistically analyzed for performance. We also propose two simple factory assignment rules together with 14 heuristics based on dispatching rules, effective constructive heuristics and variable neighborhood descent methods. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed methods.     

Min-degree constrained minimum spanning tree problem: New formulation via Miller–Tucker–Zemlin constraints

Given an undirected network with positive edge costs and a positive integer $d>2$, the minimum-degree constrained minimum spanning tree problem is the problem of finding a spanning tree with minimum total cost such that each non-leaf node in the tree has a degree of at least $d$. This problem is new to the literature while the related problem with upper bound constraints on degrees is well studied. Mixed-integer programs proposed for either type of problem is composed, in general, of a tree-defining part and a degree-enforcing part. In our formulation of the minimum-degree constrained minimum spanning tree problem, the tree-defining part is based on the Miller–Tucker–Zemlin constraints while the only earlier paper available in the literature on this problem uses single and multi-commodity flow-based formulations that are well studied for the case of upper degree constraints. We propose a new set of constraints for the degree-enforcing part that lead to significantly better solution times than earlier approaches when used in conjunction with Miller–Tucker–Zemlin constraints.     

The generalized assortment and best cutting stock length problems

This paper introduces two new one-dimensional cutting stock models: the generalized assortment problem (GAP) and the best cutting stock length (BSL) problem. These new models provide the potential to reduce waste to values lower than the optimum of current models, under the right management circumstances. In the GAP, management has a standard length and can select one or more of any additional custom stock lengths, and management wishes to minimize cutting stock waste. This model is different from existing models that assume that the selection is from a small fixed set of stock lengths. In the BSL problem, management chooses any number of custom stock lengths, but wishes to find the fewest custom stock lengths in order to have zero waste. Results show waste reductions of 80% with just one custom stock length compared with solutions from standard cutting stock formulations, when item lengths are long relative to the stock length. The models are most effective when the item lengths are nearly as long as the stock length. Solutions from the model have been implemented for a manufacturer. The model is easily generalized to allow multiple existing stock lengths and different costs.