TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation

Automatic image annotation (AIA) is an effective technology to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on hidden Markov model (HMM). Compared with the previous HMM-based annotation methods, SVM based semi-supervised learning, i.e. transductive SVM (TSVM), is triggered out for remarkably boosting the reliability of HMM with less users’ labeling effort involved (denoted by TSVM-HMM). This guarantees that the proposed TSVM-HMM based annotation scheme integrates the discriminative classification with the generative model to mutually complete their advantages. In addition, not only the relevance model between the visual content of images and the textual keywords but also the property of keyword correlation is exploited in the proposed AIA scheme. Particularly, to establish an enhanced correlation network among keywords, both co-occurrence based and WordNet based correlation techniques are well fused and are able to be helpful for benefiting from each other. The final experimental results reveal that the better annotation performance can be achieved at less labeled training images.     

基于矢量图形的中文文本零水印算法

为了实现中文电子文本的版权保护及防止未经授权的文本信息的扩散,构造了一种基于矢量图形的中文文本零水印方案,并对该方案进行了算法的设计实现.算法以文本主题特征为前提,通过对文本主题特征进行矢量化处理为基础,最终来完成中文文本的零水印构造.最后通过仿真实验对算法进行了验证,结果表明该算法能有效抵抗文本常规编辑、同义替换等操作,在一定程度上改善了水印的鲁棒性、安全性、抗攻击能力.     

有向无环图最小度生成树问题的一种近似算法

计算具有较小度的生成树是算法与复杂性研究的一个基本问题,同时在网络设计等领域具有重要应用.给定具有n个顶点的有向无环图G=(V,E)和根顶点r∈ V,最小度生成树问题欲求一棵以r为根的生成树T,使得在G的所有以r为根的生成树中T的最大度最小.给出该问题的一种迭代的多项式时间近似算法.该算法所求树的度不超过△*+1,其中△*为某一最优树的度.算法的时间复杂度为O(n2logn),其中n为顶点数目.算法没有运用过多的枚举,其实际运行时间要快得多.     

具有时变需求的配送型多级库存优化研究

考虑到战术仓库的需求率具有时变性的特点,应用供应链管理的思想研究了一个战役仓库、多个战术仓库组成的配送型多级库存优化问题,建立了战役仓库采用等量订货策略,战术仓库分别采用等间隔、等量订货策略时的模型,并采用自适应遗传算法实现了模型的求解,实例验证了模型及算法的有效性.实例表明：战役仓库采用等量订货策略,战术仓库采用等间隔订货策略时总费用最低.     

一种Ling选择进位加法器

设计一种Ling选择进位加法器,组间采用Ling进位代替传统的进位,利用内部连线与节点扇出平衡的并行前缀逻辑产生进位机制,并对通常的进位选择模块进行调整,以使其适合Ling进位。该加法器兼具了Ling加法器的快速性,又避免了逻辑产生的复杂性。实验结果表明,与超前进位加法器相比,该加法器的速度提高12％左右。     

Nonlinear Dimensionality Reduction by Local Orthogonality Preserving Alignment

We present a new manifold learning algorithm called Local Orthogonality Preserving Alignment (LOPA). Our algorithm is inspired by the Local Tangent Space Alignment (LTSA) method that aims to align multiple local neighborhoods into a global coordinate system using affine transformations. However, LTSA often fails to preserve original geometric quantities such as distances and angles. Although an iterative alignment procedure for preserving orthogonality was suggested by the authors of LTSA, neither the corresponding initialization nor the experiments were given. Procrustes Subspaces Alignment (PSA) implements the orthogonality preserving idea by estimating each rotation transformation separately with simulated annealing. However, the optimization in PSA is complicated and multiple separated local rotations may produce globally contradictive results. To address these difficulties, we first use the pseudo-inverse trick of LTSA to represent each local orthogonal transformation with the unified global coordinates. Second the orthogonality constraints are relaxed to be an instance of semi-definite programming (SDP). Finally a two-step iterative procedure is employed to further reduce the errors in orthogonal constraints. Extensive experiments show that LOPA can faithfully preserve distances, angles, inner products, and neighborhoods of the original datasets. In comparison, the embedding performance of LOPA is better than that of PSA and comparable to that of state-of-the-art algorithms like MVU and MVE, while the runtime of LOPA is significantly faster than that of PSA, MVU and MVE.     

分布式文件系统元数据服务的负载均衡框架

请求负载均衡,是分布式文件系统元数据管理需要面对的核心问题.以最大化元数据服务器集群吞吐量为目标,在已有元数据管理层之上设计实现了一种分布式缓存框架,专门管理热点元数据,均衡不断变化的负载.与已有的元数据负载均衡架构相比,这种两层的负载均衡架构灵活度更高,对负载的感知能力更强,并且避免了热点元数据重新分布、迁移引起的元数据命名空间结构被破坏的情况.经观察分析,元数据尺寸小、数量大,预取错误元数据带来的代价远远小于预取错误数据带来的代价.针对元数据的以上鲜明特点,提出一种元数据预取策略和基于预取机制的元数据缓存替换算法,加强了上述分布式缓存层的性能,这种两层的元数据负载均衡框架同时考虑了缓存一致性的问题.最后,在一个真实的分布式文件系统中验证了框架及方法的有效性.     

MapReduce框架下基于B 树的高维索引

针对MapReduce数据块处理机制、高维数据分布特征和KNN查询需求,本文设计一种基于B 树的高维索引结构（iPartition）,创新性提出基于主成分区分度的优化数据划分策略和邻接数据域分散存储等原则,将数据均匀划分到不同的Slave节点,使尽可能多的数据域对计算共同贡献,提升MapReduce任务处理并行性;利用B 树构造分布式的双层索引实现查询时数据范围快速过滤,降低高维计算代价。实验表明,iPartition在高维数据近似查询环境下,具有良好的性能和扩展性。     

一种基于组策略的过滤式特征选择算法

MRMR算法具有快速、高效等优势,在处理高维数据方面较为流行。基于此,提出一种基于组策略的MRMR改进算法（MRMRE）,该算法不仅考虑单个特征属性的相关性与冗余性,同时针对特征组间的相互关系进行研究。算法以MRMR算法为框架,以CCA作为度量基准,选择SVMs作为基分类器,使其特征选择效果提升。在UCI机器学习数据库中图像与基因序列数据集上的大量实验表明：与MRMR算法相比,所提出的算法其特征选择结果具有更高的结果稳定性与分类精度。     

A new smooth robust control design for uncertain nonlinear systems with non-vanishing disturbances

In this paper, we consider the control problem for a general class of nonlinear system subjected to uncertain dynamics and non-varnishing disturbances. A smooth nonlinear control algorithm is presented to tackle these uncertainties and disturbances. The proposed control design employs the integral of a nonlinear sigmoid function to compensate the uncertain dynamics, and achieve a uniformly semi-global practical asymptotic stable tracking control of the system outputs. A novel Lyapunov-based stability analysis is employed to prove the convergence of the tracking errors and the stability of the closed-loop system. Numerical simulation results on a two-link robot manipulator are presented to illustrate the performance of the proposed control algorithm comparing with the layer-boundary sliding mode controller and the robust of integration of sign of error control design. Furthermore, real-time experiment results for the attitude control of a quadrotor helicopter are also included to confirm the effectiveness of the proposed algorithm.