Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks.The label propagation algorithm(LPA) is a near linear time algorithm to find a good community structure. Despite various ubsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the asic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these onsensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the dge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number f partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps,y computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter o adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an pproach named the label propagation algorithm with consensus weight(LPAcw), and the experimental results showed that he LPAcw could enhance considerably both the stability and the accuracy of community partitions.     

传输线中脉冲信号反射波的测量和应用

将高频单脉冲信号输入同轴电缆，借助数字存储示波器记录输入、输出端的波形和相对延时，可以测量传输线的电缆长度和衰减系数，同时观察脉冲波形因色散引起的畸变．     

多维谱的简化探测

提出一种利用谱编辑的方法来简化二维谱的探测,使得一张二维谱可以被简化为N条一维谱(N等于二维谱中F1维上的谱线总数),从而达到缩短数据采集时间和减少数据贮存空间的目的。文中给出了运用此方法探测二维碳氢异核相关实验的实例,并分析了由于F1维的谱线不为δ函数而产生的编辑误差的大小,最后将此方法推广到三维的简化探测,即通过对N张二维谱的编辑获得三维谱的信息。     

Effects of threshold on single-target detection by using modified amplitude-modulated joint transform correlator

Effects of threshold value on detection performance of the modified amplitude-modulated joint transform correlator are quantitatively studied using computer simulation. Fingerprint and human face images are used as test scenes in the presence of noise and a contrast difference. Simulation results demonstrate that this correlator improves detection performance for both types of image used, but moreso for human face images. Optimal detection of low-contrast human face images obscured by strong noise can be obtained by selecting an appropriate threshold value.     

Adaptive detection technique for optical wireless communication over strong turbulence channels

Optical wireless communication (OWC) systems use the atmosphere as a propagation medium, so the atmospheric turbulence effects lead to fading related with signal intensity. The received signal of OWC over strong turbulence channels is assumed to be a mixture of K-distributed fading and Gaussian distributed thermal noise. Second-order spectral analysis is unable to separately estimate the mixed signal. In order to mitigate the fading induced by turbulence, the decision threshold-updating algorithm based on second and higher order cumulants is proposed and is able to operate in an unknown turbulence environment. The performance of the adaptive processing scheme has been evaluated by means of Monte Carlo simulations. Simulation results show the improvement of the bit error rate (BER) performance.     

ISG-FHEV等效燃油消耗最小控制策略

针对室内复杂环境下火灾识别准确率会降低的问题,提出了一种改进的粒子群算法优化支持向量机参数进行火灾火焰识别的方法。首先在 颜色空间进行火焰图像分割,对获得的火焰图像进行预处理并提取相关特征量;其次采用PSO算法搜索SVM的最优核参数和惩罚因子,并在PSO算法中加入变异操作和非线性动态调整惯性权值的方法,加快了搜索SVM最优参数的精度和速度;然后将提取的火焰各个特征量作为训练样本输入SVM模型进行训练,并建立参数优化后的SVM分类器模型;最后将待测试样本输入SVM模型进行分类识别。算法的火灾识别准确率达到94.09%,分类效果明显优于其他分类算法。仿真结果表明,改进的PSO优化SVM算法提高了火焰识别的准确率和实时性,算法的自适应性更强,误判率更低。     

基于ARM处理器的运动目标检测与跟踪系统

运动目标的检测及跟踪技术广泛应用于军事与民用领域。传统基于PC的目标跟踪系统不能满足对体积、功耗及便携性的需求,基于ARM处理器S3C2440A和嵌入式Linux操作系统构建了一个比较完整、实用的目标跟踪系统。该系统利用摄像头采集运动目标图像,处理器进行数字图像处理,LCD显示跟踪目标。阐述了实现原理及图像获取、目标检测、图像分割和目标跟踪算法程序流程。通过实际测试,该系统能满足对低速运动目标的检测、识别和跟踪,且具有较好的实时性和稳定性。     

基于二维重组和动态窗格的水质检测紫外-可见光谱去噪算法

立足于成功研制的紫外-可见光谱水质检测多参数测量系统,针对紫外-可见光谱水质多参数原位实时检测在精度、灵敏度、稳定性等方面的实际需要,开展了基于二维重组和动态窗格的水质检测紫外-可见光谱去噪算法的研究,以此提高紫外-可见光谱水质检测的测量精度。光谱法水质检测系统通常使用工业级低成本光谱仪,其输出光谱包含明显的非平稳噪声。传统去噪法难以在滤除噪声的同时保留谱线细节。而且,原位实时水质检测条件下,被测水样可能快速变化,传统去噪法中常用的多次采样求均值法将产生额外的测量误差。引入的去噪算法通过对水样光谱进行等间隙连续采样,将光谱数据张成由光谱轴和时间轴构成的二维矩阵,经过二维小波变换后,设置一个可变宽度的窗格在系数矩阵中水平滑动,使用窗格内的小波系数计算得到动态去噪阈值,并随窗格滑动构建去噪阈值向量,由此进行光谱去噪。其中,窗格宽度由相邻区域的噪声方差变化率决定,变化率较高的区域缩小窗格宽度,反之则扩大宽度。实验结果表明,这种去噪算法不仅能有效去除光谱中的非平稳噪声,而且能保留光谱的细节信息,有助于提高仪器的测量精度。与此同时,由于该算法并未使用时域平均,样本的快速变化对去噪性能的影响较小,适合在线或原位水质检测的水样本环境。     

可见/近红外光谱的油茶籽油三元体系掺假检测模型优化

采用可见/近红外光谱技术结合化学计量学方法对油茶籽油三元体系掺假进行定量检测研究。将菜籽油和花生油按不同比例掺入纯油茶籽油中,获得掺假样本。采集纯油茶籽油及掺假样本在350~1 800 nm范围内的可见/近红外光谱数据,随机分为校正集和预测集,并从不同建模波段、预处理方法及建模方法角度对掺假预测模型进行优化。研究结果表明,菜籽油、花生油和总掺伪量的最优建模波段及预处理方法分别为750~1 770,900~1 770 ,870~1 770 nm和多元散射校正(MSC)、标准归一化处理(SNV)和二阶微分,而最优的建模方法均为最小二乘支持向量机(LSSVM)。对于最优掺假模型,菜籽油、花生油和总掺伪量的预测集相关系数(R_p)和预测均方根误差(RMSEP)分别为0.963,0.982,0.993和2.1%,1.5%,1.8%。由此可见,可见/近红外光谱技术结合化学计量学方法可以用于油茶籽油的三元体系掺假定量检测。