基于空时显著性感知的运动目标检测方法*

从视觉系统生理结构出发,对Itti视觉注意力模型进行了改进,融入运动特征,提出了一种基于视觉空时显著性感知的运动目标检测方法。首先提取图像的空间特征,形成空间显著图;然后利用相邻帧图像的全局运动、局部运动和相对运动,确定运动目标与背景的运动差异,形成运动显著图,并且对各显著区的空间特征和运动特征进行融合形成空时显著图,检测出运动目标。理论分析和实验结果表明,该方法能快速、准确地发现目标,减少目标的截获时间,提高目标跟踪性能。     

二次B-Spline时域基函数的TDFEM的应用

提出了时域有限元法(TDFEM)的一种新的基函数—二次B-spline时域基函数.首先简述了时域有限元法的原理和基本公式;然后提出了新型的基于B-spline函数的条件稳定和无条件稳定的时域有限元法方案,并应用于三维电磁辐射问题.通过典型的算例对这两种方案的精度、运算时间进行了比较,证实了基于二次B-spline函数的时域有限元法的有效性.通过稳定性理论分析得出该算法的精确稳定性,并且通过数值计算的结果得到验证.     

行为时序逻辑中公平性的研究与完善

基于行为时序逻辑(TLA)的并发系统描述,就是对系统的初始状态、系统行为和行为的公平性进行规约和描述,但TLA中的公平性具有局限性,无法准确地描述某些系统的行为,从而限制了TLA的描述能力。通过研究TLA中公平性的推导过程,分析公平性的概念与定义方法,并以实际例子说明它的局限性。在此基础上,提出以加入两级新的公平性方式对其进行完善。最后,证明了新公平性等级之间的蕴涵关系。完善后的公平性具有更强的描述能力,能够对系统进行更完整的描述与规约。     

甘肃中部地区农村劳动力转移的问题及对策

摘 要 甘肃中部地区在农村劳动力转移过程中出现了从未有过的“五化”（整体人口低能化、劳动者队 伍的老龄化、村庄凋敝化、公共管理弱化、男性鳏夫化）现象。通过对该现象进行深入分析,有针对性地 提出了大力提高农村人口素质,创新农地产权制度,大力发展民营企业,着力推进农村就业公共服务体系 建设,有序、较快推进农村城镇化等对策建议。     

一种新的盲源分离拟开关算法

针对电子战中各种信号混叠严重难以分离的现象,在盲源分离开关算法基础上提出一种新的盲信源分离拟开关算法.该算法引入单位对称加权滑动向量来加权每次迭代所得的分离信号作为源信号,用峭度取代原算法的峭度符号位作为判断函数来自适应选择加权相应激活函数,以此优化学习算法,结合信号分选的具体应用,给出了迭代结束的评判方法.计算机仿真实验表明,在强噪声背景影响下,该算法能够更加有效地分离空间未知多源线性混叠信号,且在分离效果、稳定性、处理速度和抗噪性能上都比原算法有较大改进.     

Variability of complementary relationship and its mechanism on different time scales

In the complementary relationship (CR) between actual and potential evapotranspiration, wet envi- ronment evapotranspiration (Ew) is usually calculated using the Priestley-Taylor (P-T) equation. Based on the data obtained from 38 catchments of the Haihe River basin and the Weishan experiment site in Shandong Province of China, this study has found variations in the Priestley-Taylor parameter α (e.g. increase in values with increase in the amount of precipitation on annual scale, seasonal fluctuation noted t...     

任务级行动序列问题中的定性偏好研究

关注了一类典型行动序列,研究如何在动作集合上存在定性偏好,且偏好集合存在不一致性时开展规划。所考虑的行动序列问题称为任务级COA,以抽象层次的动作为基本要素,所考虑的定性偏好包括静态偏好和时序偏好,所讨论的规划目的是获得最大满意度的COA方案。首先建立了偏好与约束的归一化形式描述,在此基础上形成了COA方案设计算法;进一步,使用计算辩论技术排除偏好集合中的不一致性,形成用户接受度最高的COA方案。文中建立的以定性推理为基础的规划框架,实现了偏好解耦,能够适应不同的领域问题,是以定量计算为基础的传统规划算法的有效补充。通过快速响应卫星成像的COA案例,演示了算法的可行性。     

Learning a discriminative mid-level feature for action recognition

In this paper,we address the problem of recognizing human actions from videos.Most of the existing approaches employ low-level features(e.g.,local features and global features)to represent an action video.However,algorithms based on low-level features are not robust to complex environments such as cluttered background,camera movement and illumination change.Therefore,we propose a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features of densely sampled 3D cuboids.Each cuboid is classified by the corresponding random forests with a novel fusion scheme,and the cuboid’s posterior probabilities of all categories are normalized to generate a histogram.After that,we obtain our mid-level feature by concatenating histograms of all the cuboids.Since a single low-level feature is not enough to capture the variations of human actions,multiple complementary low-level features(i.e.,optical flow and histogram of gradient 3D features)are employed to describe 3D cuboids.Moreover,temporal context between local cuboids is exploited as another type of low-level feature.The above three low-level features(i.e.,optical flow,histogram of gradient 3D features and temporal context)are effectively fused in the proposed learning framework.Finally,the mid-level feature is employed by a random forest classifier for robust action recognition.Experiments on the Weizmann,UCF sports,Ballet,and multi-view IXMAS datasets demonstrate that out mid-level feature learned from multiple low-level features can achieve a superior performance over state-of-the-art methods.     

Temporal evolution of contacts and communities in networks of face-to-face human interactions

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific coil- ferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze tile evolution of contacts and communities over time to consider the stability of the respective conmmnities. Furthermore, we assess different factors which have an influence on the quality of com- munity prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.     

时序图节点嵌入策略的研究

相较于传统的图数据分析方法,图嵌入算法是一种面向图节点的新型图数据分析策略.其旨在通过将图节点向量化表达,进而在节点向量基础上利用神经网络相关技术更有效的进行图数据分析或挖掘工作,如在节点分类、链接预测及交通流预测等经典问题上取得效果显著.虽然研究者们在图嵌入方面已取得了诸多成果,但是面向时序图的节点嵌入问题却未被充分重视,本文便是在先前研究工作的基础上,结合信息在时序图中的传播特性,提出了一种对时序图节点进行自适应嵌入表达的方法ATGEB （Adaptive Temporal Graph Embedding）.首先,为了解决不同类型时序图节点活跃程度不同的问题,通过设计一种自适应方式对其活跃时刻进行聚类.而后,在此基础上设计一种游走模型用以保存节点对之间的时间关系,并将节点游走序列保存在一种双向多叉树上进而可以更快速的得到节点时间相关的游走序列.最后,在基于节点游走特性和图拓扑结构的基础上,对节点向量进行重要节点采样,以便在尽可能短的时间内训练出满足需求的网络模型.通过充分的实验证明,本文面向时序图的嵌入策略相较于现流行的嵌入方法,在时序图时序中节点间时序可达性检测以及节点分类等问题上得出了更好的实验效果.