基于半圆阵的解模糊技术研究

传统的干涉仪测向解模糊方法要求特定的天线阵列形式,其应用容易受到可利用体积的限制.针对传统测向解模糊方法在实际系统应用中的问题,提出了任意基线测向原理并建立了任意基线测向模型.对建立模型的镜像模糊问题、多值模糊问题进行了分析并且提出了解决方法.为解决被动雷达多模复合制导导引头体积受限的问题建立了半圆阵天线阵列模型.时建立的模型进行了Matlab仿真,验证了方案的可行性.     

基于解析振速的ESPRIT算法

基于声矢量传感器在二维平面波入射时的输出信息,提出了其"解析振速"的输出模型,将目标的方位信息蕴含在输出信号的相位之中,并由此得到了矢量阵的输出模型.当ESPRIT算法应用于矢量阵时,由于矢量阵的内禀属性,旋转矩阵不再与阵型和信号的频率有关,只与目标方位信息有关,从而能够实现任意阵型全空间的无模糊测向.理论分析和仿真表明:本算法无需先验已知阵型;单声矢量传感器也可以应用ESPRIT算法,且此时其性能逼近克拉美-罗界.湖试表明本算法是有效的.     

一种在未知噪声下的快速波达方向估计方法

提出了一种基于传播算子方法的波达方向估计（direction of arrival, DOA）算法,和传统的子空间方法相比,该方法不需要进行矩阵特征分解,有效降低了运算复杂度,同时它对相干信源有更高的分辨率。另外,传统的传播算子方法只在噪声协方差矩阵已知并且信噪比（signal-to-noise ratio, SNR）较高的情况下才有很好的分辨率,而本方法在未知相关噪声场中也能够保持非常高的精度。最后,引入了基于混沌优化思想的谱峰搜索算法,进一步减少了算法运算时间,并通过仿真实验验证了文中结论。     

A Modeling and Decision Making Based on Evolution

1　ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅｒｅａｒｅｍａｎｙｄｉｆｆｉｃｕｌｔｉｅｓｉｎｑｕａｎｔｉｔａｔｉｖｅｒｅｓｅａｒｃｈｆｏｒｓｕｓｔａｉｎａｂｌｅｄｅｖｅｌｏｐｍｅｎｔｓｙｓｔｅｍ .Ｓｕｓ ｔａｉｎａｂｌｅｄｅｖｅｌｏｐｍｅｎｔｓｙｓｔｅｍｃｏｎｓｉｓｔｓｏｆａｎｕｍｂｅｒｏｆｓｕｂｓｙｓｔｅｍｓｔｈａｔａｒｅｉｎｔｅｒａｃｔｉｖｅｅａｃｈｏｔｈｅｒ ,ｗｈｉｃｈｉｓｎｏｎｌｉｎｅａｒａｎｄｌｏｎｇ ｔｅｒｍ .Ｉｔｓｏｂｊｅｃｔｓａｎｄｃｏｎｄｉｔｉｏｎｓａｒｅｅｎｔａｎｇｌｅｍｅｎｔｗｉｔｈｍａ…     

Optimizing Concurrent Product Development Process Using Genetic Algorithm

0　IntroductionTimesaving,timetomarketandcycletimereductionarethenewgoalsandaimsforenterprisesinordertosuccessfullycompeteintothemodernglobalmarket[1].Thereisnodoubtthatconcurrentconsiderationofproductandprocessdesignimprovesproductqualityandreducesthere-designwork,whichleadstoshorterproductlead-time.Buttheconcurrentdevelopmentfashionbringsaboutmoreobstaclessuchascomplextimeorder,informationandresourceconstrainednatureofrelationshipsbetweenactivitiestothedesignprocess,whichleadtothefactthatiti…     

上海电子金融发展战略目标分析

面对网络经济与互联网的飞速发展,金融作为高度信息化的产业,对信息和信息科学有着密切的依赖关系。阐述了电子金融的基本概念;对上海国际金融中心建设的目标、途径和发展现状进行了分析,指出发展电子金融是上海国际金融中心发展的重要途径。最后在分析了上海电子金融发展相关因素的基础上,提出了上海电子金融发展的战略和战略目标。     

信息系统开发的并行工程方法

叙述了并行工程的概念和特点 ,并且将并行工程的原理应用于信息系统开发过程 ,提出了信息系统开发的生命周期、信息系统开发的上游工程和下游工程等概念 ,并以一具体开发实例说明这种方法的优点 .     

Consumer Participation in Designing Community Based Consumer-Directed Disability Care: Lessons from a Participatory Action Research-Inspired Project

User participation has been embraced worldwide as a means to provide better consumer outcomes in health and community care. However, methodologies to achieve effective consumer engagement at the programme design level have remained under-explored. The purpose of this study was to evaluate the impact of a Participatory Action Research (PAR)-inspired methodology used to develop a consumer-directed community care/individualised funding service model for people with disabilities. A retrospective analysis of case notes and internal reports for the first 6 years of an ongoing project were examined. The findings suggest that PAR methodologies need to take into account community development, group support, and capacity building as well as succession planning and risk management issues in order to facilitate the often lengthy policy and project development process. Drawing on these findings, this article discusses five lessons and their methodological implications for PAR in a health or social policy/programme design context.

复杂产品开发过程仿真与预测研究

为了更有效的对复杂产品开发过程进行预测分析,提出一种两层设计结构矩阵模型作为仿真的结构关系模型.建立了服从三角分布的时间与成本模型,采用拉丁超立方抽样法对该模型进行抽样并生成样本点.采用任务返工概率、返工影响与学习效应曲线来描述开发过程的迭代特征,对任务返工概率的变化调整、返工需求判断策略、多任务返工执行约束等问题给出了相应的仿真模拟方法.在此基础上提出了改进的产品开发过程仿真算法,并设计开发了基于Matlab的复杂产品开发过程仿真平台.最后,通过一个实例证明了该仿真平台的有效性和实用性.     

Higher-order principal component pursuit via tensor approximation and convex optimization

Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit （HOPCP）, since it is critical in multi-way data analysis. Unlike the convexification （nuclear norm） for matrix rank function, the tensorial nuclear norm is stil an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme： the augmented Lagrangian alternating direction method （ALADM） and its truncated higher-order singular value decomposition （ALADM-THOSVD） version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.