一种数据流处理环境下的节点副本放置方法

物联网环境下的许多应用表现为传感数据的连续流式处理,且系统往往通过节点的副本技术保障可用性。但是,运行时副本的备份和放置存在内存和带宽等资源开销,产生处理的延迟。该文给出一种方法,根据运行时的资源消耗以贪心方式放置节点的副本,折中了系统的可用性和开销。实际系统的仿真实验表明,在相同的条件下,该方法相比传统的随机放置,能为系统提供更稳定的可用性。     

云数据中心时延优化的数据放置方法

针对云数据中心数据获取效率低和服务器资源浪费问题,为优化云平台的数据访问和资源利用,文中提出了一种时延优化的云数据中心数据放置（LOP）方法。文中首先分析了云平台的性能,建立了云平台的资源利用和数据获取时间模型。然后基于非支配排序算法NSGA-Ⅲ实现了全局最优的数据放置策略,对数据资源进行合理部署,有效利用服务器的资源,提高了数据获取的效率。最后通过CloudSim仿真平台,对提出的数据放置方法进行了仿真和对比实验。实验结果表明,LOP方法能明显提高云服务器的资源利用率,缩短任务的数据获取时间。     

Fuzzy Logic-Based Torque Control System for Milling Process Optimization

This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.     

A Two-step Keyword Spotting Method Using Fuzzy Search Algorithm

For many practical applications, input signal is a spontaneous conversation while the acoustic model was trained with read speech because of data availability. The performance of Keyword spotting system will degrade significantly. To solve this problem, we presented a two- step keyword spotting strategy. A fuzzy search algorithm was adopted to improve the retrieval performance. We generated confusion matrix from confusion network. It gives more freedom in the representation so as to alleviate the effect phoneme mlsrecognition. Furthermore a hybrid confidence measure was applied to reject false accepts. Experiments show that the proposed algorithms significantly reduce Equal error rate （EER） on the telephone conversational task.     

A Grey Wolf Optimization Based Algorithm for Optimum Camera Placement

Wireless Personal Communications - Camera placement is very important for surveillance applications. Proposed work presents a new method of optimum placement of visual sensors for maximum coverage...     

Resource Management in a Peer to Peer Cloud Network for IoT

Wireless Personal Communications - Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale...     

基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法

针对目前光照传感器的布局方式计算量大、能耗高,易受人为因素的影响,难以准确地预测室内日光强度等问题,该文提出一种基于线性优化模糊C均值算法(LOFCM)和人工神经网络(ANN)的光照传感器布局方法。LOFCM算法利用线性优化(LO)稀疏权重矩阵后,使用模糊C均值(FCM)筛选数据,确定工作面光照传感器布局。随后,使用ANN分别训练工作面光照传感器测量值与4组辅助光照传感器测量值之间的非线性数学模型。实验结果表明,该文提出的基于LOFCM算法在保证计算工作面平均照度和均匀度准确的情况下,工作面光照传感器的数量比对比方法减少了37.5%。此外,在4组辅助光照传感器布局中,墙壁和窗户布局方式具有较好的预测精度,为预测室内日光强度提供了更加准确的预测方式。     

Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Wireless Personal Communications - Task Scheduling is one of the important aspects in Cloud Computing. The Primary Objective of the task scheduling is to effectively map tasks on to the...     

优化时延与拥挤度的增量式布局算法

提出了一种优化时延的增量式布局算法,该算法根据时延分析的结果在迭代求解的过程中动态调整线网权值.在此基础上,提出了三种同时优化时延和拥挤度的多目标优化的布局算法,在满足时延和拥挤度约束的前提下对关键路径上的单元进行位置调整.实验结果表明该算法能够有效地提高芯片速度并降低走线拥挤.对于优化线长得到的布局方案,最长路径上的时延值在增量式布局之后能够降低10%.     

优化时延与拥挤度的增量式布局算法

提出了一种优化时延的增量式布局算法,该算法根据时延分析的结果在迭代求解的过程中动态调整线网权值.在此基础上,提出了三种同时优化时延和拥挤度的多目标优化的布局算法,在满足时延和拥挤度约束的前提下对关键路径上的单元进行位置调整.实验结果表明该算法能够有效地提高芯片速度并降低走线拥挤.对于优化线长得到的布局方案,最长路径上的时延值在增量式布局之后能够降低10 % .     

一种可编程序逻辑阵列的布局算法

本文提出了一种解决可编程序逻辑阵列(PLA)布局的新算法.这个算法综合分析考虑“与”(AND)和“或”(OR)平面所形成的“组”(GROUP)之间的互连关系,在此基础上,形成了初始布局.本算法同时又考虑了总体的合理性,还照顾到局部的合理性,对初始布局进行了改善.实验结果表明,本算法在减少PLA的面积方面,具有明显的优越性.     

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

Wireless Personal Communications - Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an...     

采用入侵杂草优化算法的WSN定位精度提高方法

提高节点定位精度是无线传感器网络无需测距定位算法的关键问题之一。DV-Hop算法作为一种典型的无需测距定位算法也存在精度不高的问题。对此,提出了一种基于改进入侵杂草优化算法的新的定位算法。将算法中的目标函数设为节点定位误差,从考虑传感器节点定位问题转换为全局最优化问题,并利用人工蜂群搜索机制对入侵杂草优化算法进行改进。利用仿真技术对不同算法进行比较,从结果可以表明,本文提出的改进算法能够有效提高算法的定位精度。     

可编程逻辑阵列分段递进优化布局算法研究

为了提高FPGA(Field Programmable Gate Array)的布通率并优化电路的连线长度,在模拟退火算法的基础上,该文提出一种新的FPGA布局算法。该算法在不同的温度区间采用不同的评价函数,高温阶段采用半周长法进行快速优化布局,低温阶段在评价函数中加入变量因子并进行适度的回火处理,以此来优化布局。实验表明,该算法提高了布通率,优化了连线长度,与最具代表性的VPR(Versatile Place and Route)布局算法相比布线通道宽度提高了近6%,电路总的连线长度降低了4~23%。     

Managing the Movement of Virtual Vehicles in Cloud Environment Using Reinforcement Learning Algorithm and Fuzzy Logic

Wireless Personal Communications - Traffic monitoring methods have remarkably high costs. Hence, using intelligent vehicles can reduce the costs related to infrastructures such as equipment...     

一种综合型FPGA布局算法

FPGA的快速发展对EDA软件提出了更高的要求,布局是EDA流程中重要的一环。现有的FPGA布局算法单独应用于解决布局问题时或需要耗费太长时间,或不能给出质量较高的解。提出一种改进的综合型算法以解决FPGA布局问题,它结合了二次线性规划算法和模拟退火算法并做出相应改进,使得算法可以在较短的时间内给出质量较高的布局结果。     

一种新的模糊熵图象分割方法

图象阈值分割技术在图象分析和图象识别中具有重要的意义。本文将模糊子集理论中的模糊熵概念引入到阈值分割技术中去，在图象的模糊特征平面上采用最大熵原则确定分割阈值。实验结果表明了该方法的有效性。     

A Class of Sequential Blind Source Separation Method in Order Using Swarm Optimization Algorithm

We consider the problem of sequential, blind source separation in some specific order from a mixture of sub- and sup-Gaussian sources. Three methods of separation are developed, specifically, kurtosis maximization using (a) particle swarm optimization, (b) differential evolution, and (c) artificial bee colony algorithm, all of which produce the separation in decreasing order of the absolute kurtosis based on the maximization of the kurtosis cost function. The validity of the methods was confirmed through simulation. Moreover, compared with other conventional methods, the proposed method separated the various sources with greater accuracy. Finally, we performed a real-world experiment to separate electroencephalogram (EEG) signals from a super-determined mixture with Gaussian noise. Whereas the conventional methods separate simultaneously EEG signals of interest along with noise, the result of this example shows the proposed methods recover from the outset solely those EEG signals of interest. This feature will be of benefit in many practical applications.