基于遗传算法极化雷达反干扰接收极化选择

在有效采用极化状态基础上,采用极化对消技术使接收天线与之极化正交,可以使雷达工作在相对于干扰失配状态,从而达到反干扰目的。基于遗传算法,给出了在雷达工作频带内存在多个不同频、非相干干扰源时,接收天线最佳极化选择方法。针对典型算例,与穷举法、拉格朗日乘子求解比较,在达到相同的求解精度前提下,遗传算法用时较少,操作简单,大大节省了计算资源。     

面向软件定义网络的服务功能链优化部署算法研究

针对网络功能虚拟化(NFV)环境下,现有服务功能链部署方法无法在优化映射代价的同时保证服务路径时延的问题,该文提出一种基于IQGA-Viterbi学习算法的服务功能链优化部署方法。在隐马尔可夫模型参数训练过程中,针对传统Baum-Welch算法训练网络参数容易陷入局部最优的缺陷,改进量子遗传算法对模型参数进行训练优化,在每一迭代周期内通过等比例复制适应度最佳种群的方式,保持可行解多样性和扩大空间搜索范围,进一步提高模型参数的精确度。在隐马尔科夫链求解过程中,针对隐含序列无法直接观测这一难点,利用Viterbi算法能精确求解隐含序列的优势,解决有向图网络中服务路径的优化选择问题。仿真实验结果表明,与其它部署算法相比,所提IQGA-Viterbi学习算法能有效降低网络时延和映射代价的同时,提高了网络服务的请求接受率。

     

Synthesis of satellite footprint patterns from rectangular planar array antenna by using swarm‐based optimization algorithms

A pattern synthesis method based on Firefly Algorithm (FA) and Artificial Bee Colony (ABC) optimization has been presented to generate satellite footprint patterns from a rectangular planar array of isotropic antennas by modifying the amplitude, phase, and the state of the array elements. Three cases comprising three different footprints of rectangular, square, and circular boundary are generated from the same array by using two different swarm‐based optimization algorithms FA and ABC. Both the algorithms, following the proposed procedures are able to generate the three different footprint patterns while maintaining a satisfactory lower peak side lobe level and ripple. A comparative analysis has been carried out between FA, ABC, and Genetic Algorithm (GA) for the presented problem in terms of fitness value for the three different cases. The superiority of FA and ABC over GA has been established in terms of finding better solutions for all the three cases of the proposed problem. Copyright © 2013 John Wiley & Sons, Ltd.     

多元LDPC码列分层DBC译码算法

针对多元LDPC码扩展最小和（Extended Min Sum,EMS）译码算法收敛速度慢、运算复杂度高的问题,提出一种多元LDPC码列分层动态检泡（Dynamic Bubble-Check,DBC）译码算法。首先对变量节点按不同列重进行分层处理,译码时率先更新列重较大分层的变量节点消息,不同层之间采用串行方式进行消息传递,通过并串结合的方式降低译码迭代次数。在校验节点消息更新过程中,采用动态检泡方法减少EMS算法中的运算量,降低算法复杂度。仿真结果表明,在几乎不损失性能的前提下,该算法的平均最大迭代次数仅为EMS译码算法的50%,复杂度降低为EMS算法的50%。     

GHQ推进剂的热分解特性研究

采用差示扫描量热仪DSC和绝热加速量热仪ARC,对比研究了双基推进剂SF、改性双基推进剂GHQ和单质RDX的热分解过程,并分析评估了GHQ推进剂的热危害性。DSC实验结果表明:GHQ推进剂起始分解温度为182.4℃,热分解明显分为双基组分和RDX分解两个过程,分解峰温为202.2℃和240.4℃,分别与双基推进剂SF、单质RDX分解峰温接近,说明双基组分与RDX混合后作用不激烈。ARC实验结果表明:GHQ推进剂在最危险状态(即绝热条件)下的起始分解温度为135.3℃,绝热温升为1 197.5℃,tMR为15.9min,单位质量产生气体最大压力为15.8MPa·g~(-1)。研究结果表明:添加RDX后,GHQ推进剂发生热自燃可能性较双基推进剂SF稍有提高,热危害性大大增强。     

Micro-expression recognition using advanced genetic algorithm

In recent years, numerous facial expression recognition related applications had been commercialized in the market. Many of them achieved promising and reliable performance results in real-world applications. In contrast, the automated micro-expression recognition system relevant research analysis is still greatly lacking. This is because of the nature of the micro-expression that is usually appeared with relatively lesser duration and lower intensity. However, due to its uncontrollable, subtlety, and spontaneity properties, it is capable to reveal one’s concealed genuine feelings. Therefore, this paper attempts to improve the performance of current micro-expression recognition systems by introducing an efficient and effective algorithm. Particularly, we employ genetic algorithms (GA) to discover an optimal solution in order to facilitate the computational process in producing better recognition results. Prior to the GA implementation, the benchmark preprocessing method and feature extractors are directly adopted herein. Succinctly, the complete proposed framework composes three main steps: the apex frame acquisition, optical flow approximation, and feature extraction with CNN architecture. Experiments are conducted on the composite dataset that is made up of three publicly available databases, viz, CASME II, SMIC, and SAMM. The recognition performance tends to prevail the state-of-the-art methods by attaining an accuracy of 85.9% and F1-score of 83.7%.     

一种基于粗糙集理论的双向选择属性约简算法

文章首先对粗糙集的基本理论进行了简要介绍,然后重点讨论粗糙集理论中的属性约简问题,在总结分析现有属性约简算法的基础上,提出了一种双向选择属性约简算法--BSARA,并用一个例子与其它算法进行了对比分析,结果表明该算法是可行和有效的.     

基于自适应遗传算法的星载SAR天线方向图与模糊综合

该文针对改善星载合成孔径雷达(SAR)的模糊特性,提出了一种自适应遗传算法。该算法同时对模糊和方向图进行优化。首先确定模糊区域,然后以天线方向图的主瓣宽度和副瓣电平(包括星载SAR模糊区域的副瓣电平)为目标函数,应用自适应遗传算法对天线方向图进行综合。为了避免早熟的现象,在该算法中,交叉概率、变异概率和变异范围同时进行了自适应的变化。和非自适应遗传算法相比较,该算法迭代步骤少,收敛速度快。仿真结果表明,模糊度得到了很好的抑制,对星载SAR系统设计具有实际意义。     

二进制堆排序算法的改进研究

基于对网络服务质量中队列调度算法的介绍和分析,从队列的优先级角度出发,对经典的二进制堆Heap调度排序算法进行了改进,提出了一种新的Heap＋算法,该算法充分利用原算法出队、入队操作数固定的特点,使其在保证硬件实现复杂度低的情况下能够做到出队、入队操作的流水化,从而达到高度的并行性。且具备很高的资源利用率和可扩展性,可用于高速链路上高精度虚拟时间的排序操作。