Benchmarking Image Segmentation Algorithms

We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.     

Improving the generalization performance of RBF neural networks using a linear regression technique

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.     

Quantitative analysis in a heuristic evaluation experiment

In usability context of interactive systems the heuristic evaluation method is widespread. In most applications the results tend to be qualitative, describing such aspects that require some improvement for the benefit of usability. However, these qualitative results do not allow us to determine how usable it is or how it becomes an interactive system. Hence the need for quantitative results may also be very necessary in order to determine the effort that would be needed to get a sufficiently usable system.This article describes, following the idea of the UsabAIPO Project, a new experiment to obtain quantitative results after a heuristic evaluation. This new experimentation has required some variation on the original idea, working with a set of different heuristic categories, while considering the use of the score depending on severity and frequency parameters.     

一种面向多核系统的并行计算任务分配方法

随着多核处理器的普及,目前的大规模并行处理系统普遍采用多核处理器,这对于资源管理和调度提出了更高的要求.提出了基于共享Cache资源划分的方法,建立了面向多核处理器支持Cache资源分配的进程调度模型,设计并实现了并行任务到多核处理器的映射算法,更好地解决了大规模资源管理系统中面向多核处理器的任务分配问题,降低了使用共享Cache的多个进程运行时的相互干扰,提升了应用程序性能.     

一种多核处理器存储层次性能评估模型

一种用于评估多核处理器存储层次性能的模型,使用排队论建模,求解速度快,可以在设计早期给出不同配置参数对处理器整体性能的影响,从而调整存储层次结构,优化设计.     

基于聚类的P2P电子商务信誉评价算法

提出基于聚类的P2P电子商务信誉评价算法。该算法根据节点行为的相似性将节点聚类成若干集合,将每个节点集合视为一个节点进行迭代信誉计算。诚实节点行为相似性较高,通常聚类为最大的节点集合,在迭代计算中占主导地位。实验结果表明,与基于社会网络和概率估计的信誉评价体系算法相比,该算法误差较小。     

基于视频三音子的双模态语料自动选取算法

为实现可视语音合成,建立符合条件的双模态语料库,提出双模态语料自动选取算法。根据视频中唇部发音特征,对已有的三音子模型归类,形成视频三音子,在其基础上从原始语料中自动选取语料,利用评估函数对原始语料中的句子打分。与其他双模态语料库相比,该语料库在覆盖率等指标上有较大改进,为实现具有真实感的可视语音合成奠定基础。     

PID型广义预测控制在过热温控中的应用

在分析PID算法和广义预测控制算法的基础上,将广义预测控制算法的性能指标构造成PID形式,并推导出改进的PID型广义预测控制算法（PIDGPC）。通过在发电厂的过热蒸汽温度串级控制系统中的仿真,在时域内分析PIDGPC算法控制器的参数选择对控制性能的影响,说明PIDGPC算法比基本广义预测控制算法具有更好的控制性能,具有较好的应用前景。     

TPC-H自动测试工具TPCHDriver的研究

TPC—H基准测试可以比较不同数据库系统之间的查询性能差别,它的测试结果已经成为评价DBMS和服务器的重要标准。该文开发了自动化的TPC—H测试工具TPCHDriver,该工具应用面向对象设计、Java语言开发,图形用户界面,实现了从数据装载到Power测试和Throughtput测试、生成测试报告整个过程的自动化。简述了TPC—H基准的测试模型、TPCHDriver程序的结构和主要功能,阐述了TPCHDriver的关键实现技术。     

基于节点性能估算的应用层组播算法

提出一种基于节点性能估算的应用层组播树构建和维护算法(PPE)。综合考虑节点的性能及其在组播树中的位置,尽可能使高性能的节点接近源节点,使低性能的节点接近叶子节点,以此来提高组播树的效率,降低端到端的延迟,减小链路压力和控制开销。仿真结果表明,PPE在大规模节点环境中能有效改善组播树的性能。