Re-ranking for microblog retrieval via multiple graph model

As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.     

The design and test of dual/triple‐mode bandpass filter based on temporal coupled‐mode theory

In this article, the design and test of both a K‐band dual‐mode bandpass filter (BPF) pair and a K‐band triple‐mode BPF are presented based on N‐mode temporal coupled‐mode theory (CMT). The expressions of transmission and reflection responses are analytically derived. All the parameters in the expressions have clear physical meanings and are easily optimized to reach the required filter performance. Aided by eigenmode simulations, concrete structures of the three integrated BPFs are designed and optimized to approach the calculated physical parameters. After the fabrications and measurements of the three BPFs, extended upper/lower stopband with high stopband rejections are achieved, and by increasing the number of resonant modes, improved frequency selectivity and better passband flatness are obtained. The analytical analysis well predicts the simulation and measurement results, which provides an efficient way for BPF designs. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:609–622, 2016.     

给水管网抗震对策的重要度分析方法研究

针对城市给水管线不同管段在灾后对于管网整体可靠度影响的差异性,为了能够较好地评价不同管段对管网系统连接可靠度的贡献,引入失效概率重要度和关键重要度,建立了基于历史经验方法的管网可靠度及管线失效概率重要度、关键重要度计算模型。实例计算结果表明,该模型计算的管网连接可靠度结果与其他文献方法的计算结果基本一致,验证了模型的合理性、有效性。进而应用该模型计算出各段管线的失效概率重要度、关键重要度,对管线防灾等级划分,给出了管网分割方法与管网震后恢复策略。研究结果为编制给水管网抗震防灾规划、震后管网恢复计划及制定出相应的抗灾应急措施提供依据。     

一种高效强K—栅栏覆盖构建算法

K－栅栏覆盖是无线传感器网络覆盖控制的研究热点之一。本文构建了强栅栏覆盖模型,提出了分区强K－栅栏覆盖构建算法PMNSB,用最少的节点形成强栅栏。首先把监控区域分成多个子区域,通过匈牙利算法选用移动距离之和最少的网格集合为基准1－栅栏覆盖,缺少移动节点的子区域,选择附近区域的剩余移动节点修补形成1－栅栏覆盖。水平相邻的两个子区域之间构建竖直栅栏,这些1－栅栏合起来构成强K－栅栏覆盖。仿真结果证明了该方法的有效性,本文的研究对提升无线传感器网络的性能具有重要的理论与实际意义。     

熵能量堆煤图像的定位与识别

目的 针对煤矿皮带堆煤传感器受复杂环境干扰导致检测效率较低的问题,提出一种新的基于熵能量(EE)的堆煤图像定位与识别方法。方法 该方法首先在堆煤图像初始化区域内设定采样单元,统计采样单元前景和背景区域的信息熵,计算信息熵分布的极大值所对应的的灰度阶并统计其出现的概率;然后计算采样单元的熵能量和能量频率,归一化频率系数,并利用能量频率的二阶偏导确定堆煤边缘区域,将采样单元移动至堆煤边缘处;最后,根据采样单元的坐标位置计算堆煤高度和半径,进行实时预警。结果 在煤矿井下皮带堆煤视频上进行算法测试,该算法的单帧图像平均处理速度为0.35 s/帧,堆煤事故预警时间间隔最大为0.35 s,平均识别准确率为97.1%,测试结果表明,该方法对堆煤模糊边缘、环境噪音具有抗干扰特点,可以有效克服强噪音、模糊边缘等因素对堆煤识别的影响。结论 该方法对复杂环境中堆煤定位与识别具有很好的实时性和准确性,对提高煤矿安全生产效率具有重要意义。     

AM186EM处理器的地址空间优化及应用

本文介绍了一种基于 AM186 EM 处理器的地址空间优化应用,当系统需要处理器支持的最大容量内存时, FLASH 和外围存储器(例如双口 RAM等)可以共用处理器的UCS选通信号,从而实现 FLASH 和外围存储器共用尽可能大的地址空间。同样,当系统需要最大容量的FLASH 时,内存 RAM和外围存储器可以共用处理器的LCS选通信号,从而实现 RAM和外围存储器共用尽可能大的地址空间。     

Taking a free ride for routing topology inference in peer-to-peer networks

A Peer-to-Peer (P2P) network can boost its performance if peers are provided with underlying network-layer routing topology. The task of inferring the network-layer routing topology and link performance from an end host to a set of other hosts is termed as network tomography, and it normally requires host computers to send probing messages. We design a passive network tomography method that does not require any probing messages and takes a free ride over data flows in P2P networks. It infers routing topology based on end-to-end delay correlation estimation (DCE) without requiring any synchronization or cooperation from the intermediate routers. We implement and test our method in the real world Internet environment and achieved the accuracy of 92 % in topology recovery. We also perform extensive simulation in OMNeT++ to evaluate its performance over large scale networks, showing that its topology recovery accuracy is about 95 % for large networks.     

A <Emphasis Type="Italic">K</Emphasis> self-adaptive SDN controller placement for wide area networks

As a novel architecture, software-defined networking (SDN) is viewed as the key technology of future networking. The core idea of SDN is to decouple the control plane and the data plane, enabling centralized, flexible, and programmable network control. Although local area networks like data center networks have benefited from SDN, it is still a problem to deploy SDN in wide area networks (WANs) or large-scale networks. Existing works show that multiple controllers are required in WANs with each covering one small SDN domain. However, the problems of SDN domain partition and controller placement should be further addressed. Therefore, we propose the spectral clustering based partition and placement algorithms, by which we can partition a large network into several small SDN domains efficiently and effectively. In our algorithms, the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically. To evaluate our algorithms, we develop a new experimental framework with the Internet2 topology and other available WAN topologies. The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.