Improving minimax disparity model to determine the OWA operator weights

Determining the Ordered Weighted Averaging (OWA) operator weights is important in decision making applications. Several approaches have been proposed in the literature to obtain the associated weights. This paper provides an alternative disparity model to identify the OWA operator weights. The proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct OWA weights. The proposed disparity model can be used for a preference ranking aggregation. A numerical example in preference ranking and an application in search engines prove the usefulness of the generated OWA weights.     

Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Clustering analysis of temporal gene expression data is widely used to study dynamic biological systems, such as identifying sets of genes that are regulated by the same mechanism. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. In this paper, we introduce an improved clustering approach based on the regularized spline regression and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal number of basis and the regularization parameter. We show that this treatment can help to avoid over-fitting. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure for clustering. The energy based measure can include the temporal information and relative changes of the time series using the first and second derivatives of the time series. We demonstrate that our method is robust to noise and can produce meaningful clustering results.     

多目标遗传算法在GPS动态车辆调度中的应用研究

围绕车辆调度问题多目标优化这一主题,依据企业现有的GPS定位系统,设计一种基于实数编码和精英保留策略,综合应用外部惩罚函数法的多目标遗传算法.并于其中创新性的引入基因校验算子,避免了重复车次的产生.程序使用Java语言编制,后台数据库使用oracle.实验结果表明,该算法能够充分满足系统硬约束条件,快速收敛于目标约束,完成车辆的优化调度,具有较强的实用价值.     

改进的粒子群优化算法在QoS选播路由中的应用

QoS选播路由问题是一个非线性的组合优化问题,已被证明是NP完全问题.提出一种基于改进的粒子群优化的多QoS选播路由算法.算法引入一种特殊相加算子,让较差的路径能够不断向较好的路径学习,使算法尽可能向全局最优者靠近;设计一种随机变异算子,通过对全局极值进行随机变异,保证了粒子的多样性,提高了算法跳出局部最优解的能力.实验结果表明,该算法是可行和有效的,能够在资源预留的基础上较好地满足用户对带宽和时延的要求.     

增强现实中的视频对象跟踪算法

根据增强现实在视频对象跟踪中的应用需求,提出一种综合利用尺度不变特征变换(SIFT)算子、K聚类算法和轮廓检测的视频对象跟踪算法。该算法利用简易SIFT获得输入图像的特征点,通过K聚类算法获得可能的对象聚类,并采用改进的轮廓处理方法得到对象边界,移除孤立点,确定对象特征点,在对象特征点中获取增强现实应用中需要的注册点。在关键帧匹配中,只要使用对象特征点进行对象匹配。实验结果表明,该算法具有运行速度快、匹配正确率高的特点,能满足增强现实视频应用的注册需求。     

基于图像块和边缘增强的运动目标检测

针对复杂背景下的噪声、动态背景和阴影等影响,提出一种基于图像块和边缘增强的运动目标检测算法。用图像块建模,按掩膜结构特点修补断桥使掩膜连续,采用高层图像金字塔建模得到目标质心,用于提取目标,用Sobel平移算子得到细化的边缘,按边缘信息对掩膜进行增强处理从而得到完整的目标。实验结果表明,该算法可有效提取目标,消除单高斯建模法去除不了的复杂背景和阴影,克服传统帧差法常见的边缘不完整的缺点。     

抗干扰的运动目标检测算法

针对改进族群进化算法(EGEA)中对族群模式的有效采样问题,提出以竞争指数为选择指标的常规选择、以族群为选择单位的常规选择以及基于竞争指数的模拟退火排序选择3种选择机制,并通过多维函数优化问题进行实验分析,结果表明3种选择机制都能够在一定程度上提高EGEA的搜索效率,且基于竞争指数的模拟退火排序选择算子的实现更简便,对EGEA综合性能的提高也更明显。     

基于极化单光子的高效安全量子秘密共享方案

为进一步提高量子秘密共享协议的效率和安全性,结合量子Grove搜索算法提出一种基于极化单光子的量子秘密共享协议.效率分析结果表明,该方案可以将全部量子态用于密钥共享,理论效率为100%.在方案的安全性证明中,借助量子Grove算子和量子纠缠特性得出方案能够有效抵抗中间人攻击,同时该方案利用辅助量子态进行监视,能够以高概率检测特洛伊木马攻击.通过对Grove算子进行高维推广,证明了方案推广到(n,n)的可行性和实用性.     

基于Delta算子的永磁直线同步电机非脆弱保性能速度控制器设计

利用Delta算子离散化系统采样周期是显式参数的特点,基于Delta算子方法对永磁直线同步电机(PMLSM)不同采样周期下的非脆弱保性能速度控制器的统一化设计问题进行研究.以线性矩阵不等式(LMI)给出该控制器的存在条件.通过对PMLSM闭环系统极点的分析,表明所设计的非脆弱保性能速度控制器不但能保证PMLSM在其对象参数和控制器参数同时发生范数有界摄动时闭环系统仍保持渐近稳定,而且系统的二次型性能指标上界的数学期望值不超过某个给定的上界,而不考虑控制器参数摄动情况所设计的控制器在其参数摄动时闭环系统将无法保持稳定.     

Model Transduction for Triangle Meshes

This paper proposes a novel method, called model transduction, to directly transfer pose between different meshes, without the need of building the skeleton configurations for meshes. Different from previous retargetting methods, such as deformation transfer, model transduction does not require a reference source mesh to obtain the source deformation, thus effectively avoids unsatisfying results when the source and target have different reference poses. Moreover, we show other two applications of the model ...