某型飞机振动传感器试验器的研制

为测试某型飞机振动传感器性能,采用背靠背比较法振动测试方法,基于苏试振动台(VT-500)、功率放大器(LA-800)、单轴向电荷输出型加速度标准传感器(Ymc222A25)、电荷放大器(Ymc8102T)、普源信号发生器(DG1032Z)以及研华PCI工控机与A/D板卡(PCI-1716L),搭建振动传感器试验器振动测试平台,以DG1032Z输出作为振动激励信号源,基于VC ++6.0开发出信号发生器远程控制与板卡信号采集软件,对试验器的功能进行试验验证,结果表明其有效性.所研制的试验器可实现振动信号源的产生与标准/被测信号的校准,从而为某型飞机振动传感器的测试与修理提供技术支撑.     

基于Henon映射的加密遥感图像的安全检索方案

遥感图像具有多时相、多语义、多波段等特点,鉴于遥感图像在商业行业及国防军事中的重要性,海量遥感图像密文检索的效率和精度,直接影响了遥感大数据使用的广泛性和实时性.对密文存储的遥感大数据的安全检索是其可用性最重要的标志之一.本文提出了一种基于Henon 映射的遥感图像可搜索加密方案,根据遥感图像的成像原理及多波段特征,采用改进的Henon 映射对每个波段的灰度值进行加密处理;并根据遥感图像的“大数据”特征,通过统计灰度值的区间信息来构造遥感图像的特征向量,并根据相似度匹配算法来检索目标图像.通过对Landsat 8遥感图像进行加密与检索实验,该方案有效地提高了检索密文遥感图像的安全性及准确性,且计算复杂度低、通信成本开销小.     

群集系统分群行为建模与控制研究进展

群集系统的分群行为表征为一个聚合的母群分裂成多个子群的现象, 探讨其产生机理和控制方法具有重要的理论意义和应用价值. 鉴于此, 从分群行为的理论建模和控制方法两个角度出发, 对近年来群集系统分群运动研究的主要成果进行综述, 回顾了目前分群运动理论建模研究的最新进展, 重点总结了分群运动的现有控制方法和结论, 并对分群控制研究中尚存在的问题进行了探讨, 最后就分群行为后续可能的研究方向进行了展望.

     

开源社区中已有开发者的合作行为分析

理解开发者的合作行为是促进开源社区协作开发的关键一步。已有文献主要对社区成员的加入与迁移展开研究,而对社区中已有开发者之间的实际合作行为关注甚少。主要从开发者的交互关系与个人技能两个角度出发,探析社区已有开发者如何建立新合作以及哪些因素影响他们的合作。应用Sourceforge.net平台公开的数据进行实验,结果表明不同的开发者的偏好合作模式不同,拓扑距离上的优势(“朋友的朋友”关系)对开发者间首次合作的影响不大,开发者之前的合作次数越多越有利于他们 再次建立合作,而开发环境(操作系统)与开发者(管理)经验对合作也有明显的影响。所得结论有助于提高开源社区已有开发者的合作意识,促进社区的稳定与可持续性。     

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.     

Parallel particle filter for tracking road centrelines from high-resolution SAR images using detected road junctions as initial seed points

Acquiring road junction information is helpful for improving the quality of road network extraction. This article presents a new road network extraction method with three stages. First, road junction candidates are extracted by our previously proposed valley-finding algorithm. Second, numerous particle filters running in parallel track road centrelines starting from all of the detected junction candidates. Finally, the road centrelines are approximated by fitting the tracked centrepoints. The main contribution of this article lies in two aspects. First, road centrepoint tracking routes are launched simultaneously by considering the detected road junctions as starting points, which helps overcome the interference of various obstacles. Second, parallel tracking is implemented by a graphics processing unit (GPU) running accelerated particle filters, which significantly improves the execution time of the road network extraction task. The experimental results show that our proposed method not only handles various obstacles that occur on the roads capably, but also, on average, is 10.5 times as fast as previous methods. We compared the proposed method with two other existing methods in the literature using two different synthetic aperture radar (SAR) images to validate the superior performance of our proposed method.     

Parametric editing of clothed 3D avatars

Easy editing of a clothed 3D human avatar is central to many practical applications. However, it is easy to produce implausible, unnatural looking results, since subtle reshaping or pose alteration of avatars requires global consistency and agreement with human anatomy. Here, we present a parametric editing system for a clothed human body, based on use of a revised SCAPE model. We show that the parameters of the model can be estimated directly from a clothed avatar, and that it can be used as a basis for realistic, real-time editing of the clothed avatar mesh via a novel 3D body-aware warping scheme. The avatar can be easily controlled by a few semantically meaningful parameters, 12 biometric attributes controlling body shape, and 17 bones controlling pose. Our experiments demonstrate that our system can interactively produce visually pleasing results.     

Enriching one-class collaborative filtering with content information from social media

In recent years, recommender systems have become popular to handle the information overload problem of social media websites. The most widely used Collaborative Filtering methods make recommendations by mining users’ rating history. However, users’ behaviors in social media are usually implicit, where no ratings are available. This is a One-Class Collaborative Filtering (OCCF) problem with only positive examples. How to distinguish the negative examples from missing data is important for OCCF. Existing OCCF methods tackle this by the statistical properties of users’ historical behavior; however, they ignored the rich content information in social media websites, which provide additional evidence for profiling users and items. In this paper, we propose to improve OCCF accuracy by exploiting the social media content information to find the potential negative examples from the missing user-item pairs. Specifically, we get a content topic feature for each user and item by probabilistic topic modeling and embed them into the Matrix Factorization model. Extensive experiments show that our algorithm can achieve better performance than the state-of-art methods.