Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

Tool path generation framework for accurate manufacture of complex 3D sheet metal parts using single point incremental forming

The prototyping of complex sheet metal parts using single point incremental forming (SPIF) requires the generation of optimal tool paths and/or tool path sequences that ensure that the formed part is within geometric design specifications. The presence of a multitude of features on complex parts leads to multiple inaccuracy inducing phenomena occurring simultaneously due to interactions between the features. This paper proposes a network analysis methodology using topological conceptual graphs to capture the effects of different phenomena on the final accuracy of a sheet metal part manufactured by SPIF. Using this framework optimized tool paths can be generated that compensate for the inaccuracy inducing behavior. Tool path generation algorithms to create partial tool paths that account for the accuracy of specific features in the part based on the proposed framework are also presented. Finally, the creation of integrated tool paths maintaining complementarity between tool paths and desired continuity behavior using non-uniform cubic B-splines is illustrated. A number of case studies demonstrating the applicability of the integrated framework are discussed, where the maximum deviations in the part are significantly reduced and the average absolute deviations for the complete part are brought down to less than 0.5 mm.     

人工神经网络在图论中的应用

人工神经剐络与图论之闻有密切的联系。本文把人工神经网络应用于图论的问题求解中,利用Hopfield网络对图的着色、图的最大独立集和最大团进行求解,构造了各自的能量函数,进而得出网络的运行方程。     

校园卡充值点最优位置的数学模型研究

选址问题目前学术界已有较多的研究成果,但大多数是将总费用作为目标函数,一般要求事先给出网络结点的位置坐标,且无需考虑结点间的最短路程,旨在确定新的地理几何中心。而对已有网络,在不改变原有路径及各结点位置的条件下,以总路程最小为目标函数,在现有网络结点中寻找其中某些结点的最优位置却是一个新的研究课题。本文以某高校校园卡充值点为例,将校园示意图转化为赋权连通图,求得该连通图的邻接矩阵,利用Floyd算法及图论软件包构造一个最短路径矩阵,得到一个赋权完全图,利用穷举法或混合整数规划法及数学软件求解,得到各学院、楼栋、学生宿舍区到三个校园卡充值点的最短总路程及三个校园卡充值点的最优位置。     

基于BondGraph模型的定性故障诊断方法

研究一种基于键合图（Bond Graph）模型的定性故障诊断方法。根据Bond Graph模型元件中有关参数和变量的特定因果关系,推导出当某观测参量发生变化时,系统内所有可能产生故障的部位,并在此基础上预测每个故障的将来状态,通过与系统实际观测特征比较,在可能产生故障的集合中准确定位故障源。通过实例仿真验证,该方法是便捷有效的。     

基于图卷积网络的微博新闻故事线抽取方法

微博作为人们获取和传播新闻事件的主要平台,隐藏着丰富的事件信息。从微博数据中抽取故事线能为用户提供一种直观的方式来准确理解事件演化,然而微博数据稀疏和上下文缺乏的特点为故事线抽取带来了挑战。因此,通过两个连续的任务从微博数据中自动抽取故事线：1）基于微博传播影响力对事件进行建模,并提取出首要事件;2）基于事件特征建立异构事件图,提出事件图卷积网络（E-GCN）模型来提升对事件间隐式关系的学习能力,从而实现事件的故事分支预测并链接事件。在真实数据集上从故事分支和故事线两个角度进行评测,结果表明所提方法在故事分支生成测评中,相较于贝叶斯模型、斯坦纳树和故事森林在F1值上,在Dataset1上分别高出28个百分点、20个百分点和27个百分点,在Dataset2上分别高出19个百分点、12个百分点和22个百分点;而在故事线抽取评测中,相较于故事时间线、斯坦纳树和故事森林在正确的边准确率上,在Dataset1上分别高出33个百分点、23个百分点和17个百分点,在Dataset2上分别高出12个百分点、3个百分点和9个百分点。     

基于交互式图割算法的结肠组织提取

针对腹部CT医学图像中结肠组织自身局部特征的问题,研究了一种基于交互式Graph Cuts结肠组织的分割方法。首先人工标定一部分体素标记为＂目标＂和＂背景＂种子点。然后将图像映射成网络图,通过相邻像素间的灰度特征分配边的权重值,采用26邻域系统实现三维图像的分割。最后使用最大流/最小割方法最小化能量函数,得到结肠区域。实验结果表明,交互式的图割算法能够准确地从三维腹部CT医学图像中提取出结肠组织,体现了结肠数据的局部特征。算法能自动将所有腹部CT切片的结肠组织分割出来,实验获得的结果有利于结肠病变的发现和精确定位。     

液压系统齿轮泵故障树分析

介绍了故障树分析方法.以液压系统齿轮泵为例,通过分析系统故障形式、液压系统结构以及组件与系统之间的逻辑关系,绘制了系统的故障树,再通过最小割集的求解和重要度分析对其进行定性和定量的分析.此方法简便、直观、实用,应用于液压系统故障诊断中能够取得令人满意的效果.