慢布拉格类孤子的传播特性

在耦合模理论的基础上，分析了线性周期结构的色散关系，给出了一维无限长的克尔类非线性介质周期结构中的慢布拉格类孤子解。并且指出，增加脉冲能量会导致群速度色散效应增强，在非线性作用下，禁带宽度会变小，波的频率也会发生偏移，其偏移量主要取决于失谐因子、传播速度、波振幅强度以及非线性系数等参量。     

电磁飞环的缓扩散传播特性

电磁飞环作为一种新型有限能量电磁脉冲具有重要的潜在应用价值.为了明晰电磁飞环的传播特性,本文讨论了电磁飞环横向和纵向场分量的空间分布、频谱特性及其在传播过程中的拓扑结构变化.理论研究表明,电磁飞环在初始传播阶段发散非常缓慢,在传播较远距离后接近线性发散,其在保持超环面拓扑结构传播的情况下具有缓扩散传播特性.本文的研究为电磁飞环的应用提供了理论基础.     

模糊聚类信息融合算法在异构多传感器网络中的应用

在多传感器协同探测的应用背景下,提出将模糊聚类算法应用于多传感器网络的数据融合方向,解决异构多传感器网络由于各传感器探测信息粒度、时空不同而造成的航迹分裂和航迹冗余现象;通过仿真结果表明,该算法可以减少传统统计学方法错关联、漏关联的概率,对于复杂运动轨迹的机动目标(交叉航路目标)能够很好地进行多传感器测量数据的聚类,正确地进行测量数据的关联和融合,并进一步在通用航空监视管理系统的工程应用中加以验证;该算法在通用航空监管、物联网、协同探测信息系统方向均有广泛的应用空间。     

一种改进的基于信息传播率的复杂网络影响力评估算法

评价网络中节点的信息传播影响力对于理解网络结构与网络功能具有重要意义.目前,许多基于最短路径的指标,如接近中心性、介数中心性以及半局部(SP)指标等相继用于评价节点传播影响力.最短路径表示节点间信息传播途径始终选择最优方式,然而实际上网络间的信息传播过程更类似于随机游走,信息的传播途径可以是节点间的任一可达路径,在集聚系数高的网络中,节点的局部高聚簇性有利于信息的有效扩散,若只考虑信息按最优传播方式即最短路径传播,则会低估节点信息传播的能力,从而降低节点影响力的排序精度.综合考虑节点与三步内邻居间的有效可达路径以及信息传播率,提出了一种SP指标的改进算法,即ASP算法.在多个经典的实际网络和人工网络上利用SIR模型对传播过程进行仿真,结果表明ASP指标与度指标、核数指标、接近中心性指标、介数中心性指标以及SP指标相比,可以更精确地对节点传播影响力进行排序.     

基于核优化密度聚类的绿色苹果分割算法

苹果的可见光谱目标的高效、精准识别是实现果园测产或机器自动采摘作业的关键,由于绿色目标果实与枝叶背景颜色较为相近,因此绿色苹果的识别成为新的挑战.再由于果园实际复杂环境因素影响,如光照、阴雨、枝叶遮挡、目标重叠等情况,现有的目标果实识别方案难以满足测产或自动采摘的实时、精准作业需求.为更好地实现果园自然环境中绿色目标果...     

基于聚类技术的三维舰船模型特征库研究

存贮目标所有的视点图像,建立完备的特征库,或者提取能够抵抗视点变化的不变特征,是三维目标识别的常用方法。这两种方案都存在不足:要么特征库规模庞大,识别过程计算量大,识别效率低;要么难以找到鲁棒的识别特征。结合两种方案研究了基于聚类技术建立三维舰船模型特征库的方法。利用仿射传播聚类方法无需事先指定聚类中心的优点,将其应用于两型舰船模型的视点空间聚类。通过提取视点图像的Hu矩特征,进行了仿真实验,给出了聚类结果的有效性分析。     

聚类算法用于中药材的近红外光谱分析

将近红外光谱分析技术结合聚类算法应用于中药材的鉴别分析,为当前中药材的类别分析提供了一种新的思路。选择黄樟油、桂叶油、桉叶油、松油醇、丁香叶油及三个不同来源的艾叶油共8个中药样品用实验室自行研制的多通道近红外光谱仪器进行快速(数秒钟)吸收光谱测试。以空气为本底,测试光谱范围700～1 700nm的药品透射光谱(吸光度),结果显示8个样品的光谱差异较大。对此八种近红外光谱建立了定性数学模型,然后进行了聚类分析,并选择不同的聚类方法进行优化,得到相似系数为0.974 2,表明该聚类分析可行。在聚类分析产生的聚类树中,8个样品的聚类性质与其实际特性相符良好,特别是三种艾叶油被归为最近的类中,因此该聚类分析结果合理。     

可变聚类无标度网络上的谣言免疫策略

提出一种聚类免疫策略,使用改进的经典谣言传播模型,在可变聚类无标度网络上研究其免疫效果.研究发现,聚类免疫的效果随着网络聚类系数的增加而变好.在不同聚类系数下,比较目标免疫、介数免疫、紧密度免疫和聚类免疫的免疫效果发现,无论网络的聚类特性如何,介数免疫始终是几种免疫策略中效果最好的,当网络聚类系数较大时,聚类免疫的效果超过紧密度免疫接近目标免疫,进一步增大网络的聚类系数,聚类免疫的效果超过目标免疫而接近介数免疫.     

贝叶斯优化模糊聚类地级行政区声环境

声环境功能区划是噪声污染管理的重要手段,当前声环境功能区划研究大多是基于某个特定的地级行政区来进行的,难以反映各地级行政区声环境的异同。 该文基于134个地级行政区的人口、面积、各声环境功能区面积和及面积占比,进行地级行政区声环境表征和归一化处理。 以轮廓系数作为聚类有效性评价指标,基于贝叶斯优化模糊聚类方法对地级行政区声环境表征进行聚类分析。 通过本文方法与谱聚类、K-medoids聚类、高斯混合模型聚类的聚类性能对比,验证了该方法的有效性。 结果表明我国地级行政区声环境分为9类,城市规模和用地情况发生显著变化导致其声环境表征和归类发生变化后应重新评估当前噪声污染管理政策,并借鉴同类的地级行政区的噪声污染管理政策做出必要的调整。     

一种改进的基于层次聚类的说话人自动聚类算法

说话人聚类是语音识别以及音频检索等众多语音应用的重要组成部分.提出一种改进的基于层次聚类的说话人聚类算法,对层次聚类法做出了进一步改进:(1)改进误差平方和准则以提高聚类速度;(2)引入假设检验方法确定类别数目;(3)提出一种稳健的在线聚类方法以解决对新到来的语音段进行聚类的问题.在聚类实验中,算法的平均类纯度和说话人纯度分别为96.7%和96.6%.实验结果还表明,相比手工标注说话人信息,将该算法的聚类结果应用于说话人自适应可降低系统的误识率.     

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.     

Method of analyzing the influence of network structure on information diffusion

Social phenomena are affected by the structure of networks consisting of personal relationships. In the present paper, the diffusion of information among people is examined. In particular, the relationship between the network structure and the dynamics is studied. First, several networks are generated using the proposed network model and other network models, such as the WS model and the KE model. By changing the parameters of the network models, networks with different structures are generated. The parameters of the network models determine the topology of the networks and the statistical indicators.     

Semi-supervised clustering algorithm for community structure detection in complex networks

Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure.     

Thermal apoptosis analysis considering injection behavior optimization and mass diffusion during magnetic hyperthermia

Thermally induced apoptosis for tumors depends mainly on the intrinsic characteristics of biological tissues as well as treatment temperature profile during magnetic hyperthermia.Further,treatment temperature distribution inside tumor depends on the injection behavior of irregular tumors,such as the injection dose and the injection location of nanofluids.In order to improve the treatment effect,the simulated annealing algorithm is adopted in this work to optimize the nanofluid injection behavior,and the improved Arrhenius model is used to evaluate the malignant ablations for three typical malignant tumor cell models.In addition,both the injection behavior optimization and the mass diffusion of nanofluid are both taken into consideration in order to improve the treatment effect.The simulation results demonstrate that the injection behavior can be optimized effectively by the proposed optimization method before therapy,the result of which can also conduce to improving the thermal apoptosis possibility for proposed typical malignant cells.Furthermore,an effective approach is also employed by considering longer diffusion duration and correct power dissipation at the same time.The results show that a better result can then be obtained than those in other cases when the power dissipation of MNPs is set to be QMNP=5.4×10⁷W·m³ and the diffusion time is 16 h.     

基于簇相似度的网络社团结构探测算法

社团结构对复杂系统的结构特性和动力学特性有重要影响.提出了一个度量社团相似度的模型,称为簇相似度.该模型能够度量两个社团的相似度大小,为研究社团间的作用机制提供帮助.而且基于该模型,设计了一个社团划分算法.算法采用层次聚类的思想,每次合并两个相似度最大的社团,并通过一个评价函数选择最优社团划分.数值实验以及与CNM,GN,EigenMod等主流算法做比较,表明本算法的精度和效率都比较高,尤其对于边密度较高的网络,性能非常理想.     

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC).     

基于信息分配和扩散理论的东北地区干旱脆弱性特征分析

基于信息分配和扩散理论, 结合标准化降水指数和东北三省(黑龙江、吉林、辽宁)旱灾灾损指数, 综合考虑了气象因子与社会因子, 分析了我国东北三省1971–2012年的干旱脆弱性特征, 并进一步计算了我国东北三省的干旱风险. 使用信息分配方法估计干旱强度概率分布, 采用二维正态信息扩散方法构造了干旱强度与旱灾灾损的脆弱性关系, 将干旱强度的概率分布与脆弱性折线相乘求和(离散分布)或积分(连续分布)即可得到多年平均风险. 研究表明, 针对灾损的小样本事件引入信息分配和扩散方法对小样本数据进行分析, 获取的干旱强度概率分布比简单直方图法所得更加平滑, 而以事件为因、灾损为果得到干旱强度-旱灾灾损的脆弱性关系, 物理意义明确, 所得脆弱性关系折线也比较符合实际情况, 并且不同样本长度所得结果相近, 对样本长度不敏感, 较好地克服了小样本分析的不稳定性.关键词：信息分配和扩散理论标准化降水指数脆弱性风险     

Detecting community structure in complex networks based on a measure of information discrepancy

Properties of complex networks, such as small-world property, power-law degree distribution, network transitivity, and network- community structure which seem to be common to many real-world networks have attracted great interest among researchers. In this study, global information of the networks is considered by defining the profile of any node based on the shortest paths between it and all the other nodes in the network; then a useful iterative procedure for community detection based on a measure of information discrepancy and the popular modular function Q is presented. The new iterative method does not need any prior knowledge about the community structure and can detect an appropriate number of communities, which can be hub communities or non-hub communities. The computational results of the method on real networks confirm its capability.     

同轴周期慢波结构色散特性的通用数值解法

运用场匹配法和傅里叶级数理论,提出一种原则上可数值求解任意同轴周期慢波结构色散特性的方法,给出了同轴慢波结构TM0n模的色散方程。基于该方法,编制了Matlab程序,给出了同轴波纹波导和同轴双波纹波导色散特性,以算例形式分析计算了同轴盘荷波导的色散特性。数值计算结果与多维全电磁模拟软件结果进行比较,证明了该数值算法的准确性和可靠性。