工业无线传感器网络攻击源定位任务分配优化算法

针对工业无线传感器网络中参与攻击源节点定位的任务分配问题,构建和求解多目标优化定位任务分配模型,任务分配模型中设定参考节点组合总能量消耗、距离平均标准偏差目标函数,以及空间约束和剩余能量约束条件;采用循环拥挤排序将非支配排序遗传算法(NSGA-Ⅱ)进行改进后加入基于稀疏度局部搜索的混合优化算法联合求解任务分配模型,将稀疏度最小的解作为稀疏解,再采用极限优化策略在稀疏解周围进行局部搜索使得解拥有更好的分布特性.Matlab仿真结果表明该改进的混合优化算法可以提高算法收敛速度以及降低算法复杂度,在较快的时间内选择出合适的参考节点组合,减少了定位误差,提高了定位精度.     

动态压缩感知理论研究综述

动态压缩感知是静态传统压缩感知向动态信号的拓展,广泛应用于医学上的磁感应成像和目标追踪等领域。 由于工程 中的动态信号在某一转换基下具有随时间缓慢变化的稀疏特性,因而可以运用欠定的测量矩阵对其进行压缩。 动态压缩感知 理论主要包括动态信号的稀疏表示、动态压缩测量过程和动态信号的重构 3 个方面的研究内容。 全面综述动态压缩感知的基 本概念,归纳总结现有动态压缩感知理论中对动态信号的建模方法;对已有的动态信号重构算法进行了归类,并详述了各类算 法的计算思路;最后介绍了动态压缩感知的典型应用,并对动态压缩感知信号重构算法的研究前景进行了探讨。     

Mixed norm regularized recursive total least squares for group sparse system identification

A mixed l_p,0‐regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS‐based algorithms deteriorates when both input and output are contaminated by noise (the error‐in‐variables problem). We propose an l_p,0‐RTLS algorithm to handle group sparse system identification with errors‐in‐variables. The proposed algorithm is an RLS‐like solution that utilizes l_p,0‐regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l_p,0‐regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd.     

基于自适应采样算法的芳烃异构化代理模型

异构化是芳烃生产中的重要环节，提高异构化环节的建模和优化效率对工业生产有着重要意义。但是，直接使用机理模型的优化过程耗时较长，优化效率低。代理模型可以有效地对机理模型进行近似，而代理模型采样方法对模型精度有很大影响。提出了一种新的基于稀疏度和最邻近期望的自适应采样算法，该方法可以平衡全局搜索和局部搜索，通过求解优化问题找到反映函数关键信息的新采样点，再加入原始样本集中，使得代理模型精度不断提高。多个测试函数结果表明，相比于其他自适应采样算法，该算法能有效提升代理模型精度和建模效率。该算法在芳烃异构化环节代理模型中也得到了有效验证，与本文中其他算法对比，该算法模型误差减少5%以上，建模时间缩短30%以上。     

Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization

Recommendation system has been a rhetoric area and a topic of rigorous research owing to its application in various domains, from academics to industries through e-commerce. Recommendation system is useful in reducing information overload and improving decision making for customers in any arena. Recommending products to attract customers and meet their needs have become an important aspect in this competitive environment. Although there are many approaches to recommend items, collaborative filtering has emerged as an efficient mechanism to perform the same. Added to it there are many evolutionary methods that could be incorporated to achieve better results in terms of accuracy of prediction, handling sparsity as well as cold start problems. In this paper, we have used unsupervised learning to address the problem of scalability. The recommendation engine reduces calculation time by matching the interest profile of the user to its partitioned and even smaller training samples. Additionally, we have explored the aspect of finding global neighbours through transitive similarities and incorporating particle swarm optimization (PSO) to assign weights to various alpha estimates (including the proposed α₇) that alleviate sparsity problem. Our experimental study reveals that the particle swarm optimized alpha estimate has significantly increased the accuracy of prediction over the traditional methods of collaborative filtering and fixed alpha scheme.     

Gaussian approximations in high dimensional estimation

Several estimation techniques assume validity of Gaussian approximations for estimation purposes. Interestingly, these ensemble methods have proven to work very well for high-dimensional data even when the distributions involved are not necessarily Gaussian. We attempt to bridge the gap between this oft-used computational assumption and the theoretical understanding of why this works, by employing some recent results on random projections on low dimensional subspaces and concentration inequalities.     

COMPRESSED SPEECH SIGNAL SENSING BASED ON THE STRUCTURED BLOCK SPARSITY WITH PARTIAL KNOWLEDGE OF SUPPORT

Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS).Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signa...     

最大稀疏稳健恒定束宽波束形成器设计

为了降低宽带阵列恒定束宽的实现复杂性,在分析宽带阵列稀疏性的基础上,构造了以阵元和抽头延迟线(TDL, tapped delay line)稀疏性的凸组合为目标函数,满足恒定束宽约束的波束形成器优化模型,降低了所需的阵元和TDL个数。引入重加权机制,通过序列凸优化,使稀疏性递增并收敛到最大值,证明了保证波束形成器稳健性的范数约束与最大TDL稀疏目标函数之间的等价性。仿真结果表明,可用较少的阵元及TDL个数获得相同的恒定束宽性能,具有工程实用价值。     

压缩感知理论在谐波检测中的运用

运用传统的奈奎斯特定理对电力系统中的谐波信号进行采样将会产生极其庞大的数据量,而全新的压缩感知理论突破了传统采样定理的限制,在信号满足稀疏性的前提下,只需要较少的数据就可以实现信号的重构。文中在对现有的贪婪匹配追踪和自适应算法分析总结的基础之上,结合谐波信号自身的特点,提出了一种新的稀疏度自适应压缩采样匹配追踪算法。此算法可在信号稀疏度未知的情况下,通过信号代理和回溯的思想自适应调节步长逐步逼近原始信号,从而实现以较少采样数据进行谐波检测的目标。MATLAB中的仿真实验表明,运用所提出的算法进行谐波检测的效果理想。