基于RBF神经网络的较低浓度下同步荧光光谱的溢油鉴别

针对海面溢油样品的含量难以确定,同时考虑到海水掺杂及风化等问题的影响,提出了在较低非线性浓度范围内采集溢油嫌疑样品的同步荧光光谱,获取其训练样本集,利用主成分分析法(Principal com-ponent analysis,PCA)提取其特征光谱,结合径向基函数(Radial basis function,RBF)神经网络对肇事样本和嫌疑样本进行模式识别的方法。通过对相近油源原油样品分类识别研究表明:该方法仅需单次对肇事样本同步光谱测量,再借助数据分析,就可以很好区分相近油源溢油样品,外扰对识别率影响也不大。RBF神经网络算法识别率在92%左右。该结论对海洋环境中溢油的实时检测及油指纹数据信息库的建立有重要意义。     

Epidemic spreading in scale-free networks including the effect of individual vigilance

In this paper, we study the epidemic spreading in scale-free networks and propose a new susceptible-infected-recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Furthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.     

物联网云平台ZigBee无线通信网络节点设计

随着科学技术的发展,无线通信技术不断成为当今社会的通信方面的主流发展方向;对于一种可以集所有通讯方式的优点为一体,并且可以实现远距离网络通信传输的方式成为现在通讯技术行业的主要研究方向;而文章根据一种基于ZigBee协议的无线网络通信架构进行研究,设计出一种可以满足当今通信需求的硬件平台;其中应用到ZigBee协议中的短距离传输技术和将其与SPI接口连接完成的一种通信技术手段;最后将ZigBee单片机通过USB接口与计算机相连接完成通信无线传输工作;文章中提出了这种平台设计的软件设计方案和硬件设计方案,并且通过软硬件设计,分析出其所具备的优点和未来发展能力;通过对比试验,得出这种无线传播方式有更好的传播效率和节省能源的优势。     

基于免疫遗传算法改进的BP神经网络在灭火系统电路板故障诊断中的应用

针对装甲车辆灭火系统电路板规模较大,功能日趋多样与完善的同时,其复杂程度也日益提高,故障层次越来越多,故障现象与故障原因的映射关系更加复杂,组合故障频发,传统的故障诊断方法已不能满足灭火系统电路板故障诊断的要求。设计了基于免疫遗传算法优化的BP神经网络对灭火系统电路板进行故障诊断,并在免疫和遗传过程中保留了部分训练最优解。实现了神经网络收敛速度的提高,使用Matlab编程优化算法并完成了电路板仿真故障的诊断。通过实验验证了该诊断模型的准确性和可靠性,为电气系统通用检测设备的神经网络诊断方法实现提供了理论支撑。     

Rapid and high-capacity ultrasonic assisted adsorption of ternary toxic anionic dyes onto MOF-5-activated carbon: Artificial neural networks,partial least squares,desirability function and isotherm and kinetic study

The present paper focused on the ultrasonic assisted simultaneous removal of fast green (FG), eosin Y (EY) and quinine yellow (QY) from aqueous media following using MOF-5 as a metal organic framework and activated carbon hybrid (AC-MOF-5). The structure and morphology of AC-MOF-5 was identified by SEM, FTIR and XRD analysis. The interactive and main effects of variables such as pH, initial dyes concentration (mg L⁻¹), adsorbent dosage (mg) and sonication time (min) on removal percentage were studied by central composite design (CCD), subsequent desirability function (DF) permit to achieved real variable experimental condition. Optimized values were found 7.06, 5.68, 7.59 and 5.04 mg L⁻¹, 0.02 g and 2.55 min for pH, FG, EY and QY concentration, adsorbent dosage and sonication time, respectively. Under this conditions removal percentage were obtained 98.1%, 98.1% and 91.91% for FG, EY and QY, respectively. Two models, namely partial least squares (PLS) and multi-layer artificial neural network (ANN) model were used for building up to construct an empirical model to predict the dyes under study removal behavior. The obtained results show that ANN and PLS model is a powerful tool for prediction of under-study dyes adsorption by AC-MOF-5. The evaluation and estimation of equilibrium data from traditional isotherm models display that the Langmuir model indicated the best fit to the equilibrium data with maximum adsorption capacity of 21.230, 20.242 and 18.621 mg g⁻¹, for FG, EY and QY, respectively, while the adsorption rate efficiently follows the pseudo-second-order model.     

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.     

Mechanical properties of jammed packings of frictionless spheres under an applied shear stress

By minimizing a thermodynamic-like potential, we unbiasedly sample the potential energy landscape of soft and frictionless spheres under a constant shear stress. We obtain zero-temperature jammed states under desired shear stresses and investigate their mechanical properties as a function of the shear stress. As a comparison, we also obtain the jammed states from the quasistatic-shear sampling in which the shear stress is not well-controlled. Although the yield stresses determined by both samplings show the same power-law scaling with the compression from the jamming transition point J at zero temperature and shear stress, for finite size systems the quasistatic-shear sampling leads to a lower yield stress and a higher critical volume fraction at point J. The shear modulus of the jammed solids decreases with increasing shear stress. However, the shear modulus does not decay to zero at yielding. This discontinuous change of the shear modulus implies the discontinuous nature of the unjamming transition under nonzero shear stress, which is further verified by the observation of a discontinuous jump in the pressure from the jammed solids to the shear flows. The pressure jump decreases upon decompression and approaches zero at the critical-like point J, in analogy with the well-known phase transitions under an external field. The analysis of the force networks in the jammed solids reveals that the force distribution is more sensitive to the increase of the shear stress near point J. The force network anisotropy increases with increasing shear stress. The weak particle contacts near the average force and under large shear stresses it exhibit an asymmetric angle distribution.     

Network dynamics and its relationships to topology and coupling structure in excitable complex networks

All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.     

Optimization of transport protocols in complex networks

In this paper, an optimal routing strategy is proposed to enhance the traffic capacity of complex networks. In order to avoid nodes overloading, the new algorithm is derived on the basis of generalized betweenness centrality which gives an estimate of traffic handled by the node for a route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved, as our strategy, by redistributing traffic load from nodes with large betweenness centrality to nodes with small betweenness centrality in the proceeding of computing collective routing table. Particularly, depending on a parameter that controls the optimization scale, the new routing can not only enlarge traffic capacity of networks more, but also enhance traffic efficiency with smaller average path length. Comparing results of previous routing strategies, it is shown that the present improved routing performs more effectively.