一种软件定义物联网中的数据传输方法

在软件定义的物联网中主要通过减轻控制器端的请求负载和增加数据服务器数量来降低网络中数据传输的延迟,但未考虑数据转发平面上的数据传输延迟问题,导致网络整体数据传输延迟.对此,提出一种基于蚁群算法的数据传输方法.在数据转发平面上,对数据流的路径进行动态规划,实现数据流合理均衡分配到可行的多路径上.该方法能够充分利用网络资源...     

架空在“云”端的网络体系结构

随着近年来云技术的飞速发展,常规的数据网络在扩展性、带宽、规模、成本等方面都明显处于劣势地位,已经无法适应未来的发展需求。在这种情况下,云端网络技术在节约运算成本、提升公司运行效率方面有很好的效果。受到了企业的青睐。基于此本文对架空在“云”端的网络体系结构进行探讨。     

蚁群算法在生产调度中的应用

介绍了蚁群算法的基本思想,以旅行商问题说明了蚁群算法的模型结构,总结了蚁群算法在作业车间以及流水车间中的应用,并与其它启发式算法进行了简单的比较。在分析了目前半导体生产线调度研究现状的基础上,探讨了蚁群算法在半导体生产线调度中的应用前景。     

码书距离排序对索引压缩算法的性能改进

图像经过矢量量化后得到的索引图具有很强的统计相关性,从而使得邻近块的索引以较大的概率相等或偏移量较小。按照某种准则对码书进行排序,可以有效增强索引之间的相关性。基于平方欧几里得距离提出一种新的码书按距离排序方法。与传统的按均值、方差和能量等排序方法相比,距离排序能大大提高索引图的相关性,使索引之间的偏移量向值小的方向明显集中。将距离排序后的码书用于AICS(adaptive index coding scheme)算法,实现了更好的压缩性能。     

广域污染源自动监控系统分布式网络平台研究

针对广域污染源自动监控系统的特点和技术要求,必须采用中间分布式网络平台来满足系统的要求。通过对Microsoft的.NET、Sun的JavaONE以及OMG的CORBA这三种主要分布式网络平台的深入分析和比较研究,得出采用CORBA分布式平台是目前最佳方案的结论,并在“广东省广域水质污染源在线监测监控系统”课题中得到成功验证。     

High-Impact Bug Report Identification with Imbalanced Learning Strategies

In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects (i.e., surprise bugs), or break pre-existing functionalities and destroy the user experience (i.e., breakage bugs). Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE (synthetic minority over-sampling technique) + KNN (K-nearest neighbours) for surprise bug identification and RUS (random under-sampling) + NB (naive Bayes) for breakage bug identification outperform the F1-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.     

Asymptotic switched composite adaptive control with application to robotic interaction tasks

This article proposes a new switched adaptive control design for uncertain switched systems with composite (time-driven and state-dependent) switching and shows its applicability in switched impedance control. A composite switched adaptive control design, consisting of the direct switched adaptive control and the indirect switched adaptive control counterpart, is developed to improve the control performance. Specifically, a new stability condition for composite switching is proposed by making use of differential matrix equations and Sylvester matrix equations, which are a generalization of Lyapunov matrix equations. The design results in a time-varying multiple Lyapunov function that is decreasing at the switching instants. From the theoretical point of view, the relevance of this work is the construction of the adaptive laws that guarantee asymptotic tracking error and asymptotic estimation for the direct and indirect switched adaptive control loops, respectively. From the practical point of view, the relevance of this work is validated in a new switched impedance control for the robot interaction with uncertain and discontinuous environments.     

基于GA-RBF模型的无线电波信号预测研究

随着通信行业的发展,无线电波信号的预测问题成为科学研究的重要课题。因此研究了一种基于遗传算法优化径向基神经网络参数的无线电波信号预测模型,通过实验表明,这种经过遗传算法优化的模型比单纯的RBF网络模型预测精度更高,运行更稳定。     

小波变换-免疫算法用于重叠分析化学信号的解析

将小波变换与免疫算法结合，首先采用小波变换对原始信号进行数据压缩，然后采用免疫算法对压缩后的信号进行解析，从而使计算速度加快，对多组分核磁共振信号和二维重叠色谱信号的解析结果表明：小波变换－免疫算法可以方便地用于大数据量化学信号的解析，为快速复杂化学信号的解析提供了一种有效途径。     

基于光谱法的内窥光学相干层析扫描成像系统的研究

结合了内窥镜和光谱OCT (spectral domain optical coherence tomography, SDOCT) 技术,介绍了SDOCT的成像原理和系统的一些参数.利用光谱仪对干涉仪产生的干涉信号进行光谱展开,得到光谱解析干涉图样,设计算法以获取样品的深度信息.搭建基于光谱干涉法的光纤化SDOCT内窥成像系统,利用电机驱动直角反射棱镜旋转,对样品扫描成断层图像.采用多层盖玻片作为样品进行旋转扫描成像实验,获得的样品图像同时显示了样品的表面和深度的信息,表明该方法可以旋转扫描成像.