The quantum effects of the spin and the Bohm potential in the oblique propagation of magnetosonic waves

We study the quantum corrections to the oblique propagation of the magnetosonic waves in a warm quantum magnetoplasma composed by mobile ions and electrons. We use a fluid formalism to include quantum corrections due to the Bohm potential and to the spin magnetization energy of electrons. The effects of both quantum corrections are shown in the dispersion relation for perpendicular, parallel and oblique propagation. We find that the quantum contributions to the low frequency depend on the type in the oblique propagation with respect to the background magnetic field. The relevance in astrophysical scenarios is exemplified.     

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.     

Complex object 3D measurement based on phase-shifting and a neural network

An accurate phase-height mapping algorithm based on phase-shifting and a neural network is proposed to improve the performance of the structured light system with digital fringe projection. As phase-height mapping is nonlinear, it is difficult to find the best camera model for the system. In order to achieve high accuracy, a trained three-layer back propagation neural network is employed to obtain the complicated transformation. The phase error caused by the non-sinusoidal attribute of the fringe image is analyzed. During the phase calculation process, a pre-calibrated phase error look-up-table is used to reduce the phase error. The detailed procedures of the sample data collection are described. By training the network, the relationship between the image coordinates and the 3D coordinates of the object can be obtained. Experimental results demonstrate that the proposed method is not sensitive to the non-sinusoidal attribute of the fringe image and it can recover complex free-form objects with high accuracy.     

大学物理演示实验网络教学管理平台构建与实施

详细介绍了我校开发的“大学物理演示实验网络教学管理平台”的框架,其在教学管理中所发挥的作用,运行的效果,特别是在低年级本科生创新能力培养中所显现的效果.     

基于高频雷达多普勒谱预测风向的一种新方法

通过高频雷达一阶多普勒谱与海上风向的关系, 提出了一种基于神经网络和多波束采样法相结合预测海面风向的新方法. 在神经网络不同输入、输出参数情况下对扩展因子为奇数时的仿真数据进行了风向预测, 并在扩展因子为偶数时结合多波束采样法进行风向预测, 消除了风向的模糊性. 通过预测数据和仿真数据对比, 发现两者符合较好. 从神经网络和多波束采样法相结合的预测结果中可以看出, 风向的误差大约为4°—6°, 风向扩展因子的平均误差为0.26, 为预测海面风场的研究提供一种新的思路和方法.     

Effects of Some Topological Ingredients on the Evolutionary Ultimatum Game

This study aims at figuring out the crucial topological ingredients which affect the outcomes of the ultimatum game located on different networks,encompassing the regular network,the random network,the small world network,and the scale-free network.With the aid of random interchanging algorithm,we investigate the relations between the outcomes of the ultimatum game and some topological ingredients,including the average range,the clustering coefficient and the heterogeneity,and so forth.It is found that for the regular,random and small-work networks,the average range and the clustering coefficient have evident impacts on the ultimatum game,while for the scale-free network the original degree heterogeneity and the underlying rich-club characterizations are the mainly important topologica ingredients that influence the outcomes of ultimatum game substantially.     

基于多种群混沌遗传神经网络的模拟电路故障诊断

针对标准遗传算法优化BP神经网络收敛慢,易陷入局部最优的问题,提出了改进的多种群协同进化遗传算法,该算法改变了以往的随机初始化方法,采用了附加混沌扰动的tent映射初始化均匀分布的种群,提高了初始解的质量;每个种群采用自适应交叉率和变异率,引入移民算子实现种群间的横向联系;算法通过多种群的协同进化和种群间的个体移植提高了算法的搜索均匀性和效率;仿真实验表明该算法误差小,收敛速度快,诊断正确率高,较好地解决了模拟电路的软故障诊断问题。     

结构与参量不确定的网络与网络之间的混沌同步

进行了结构与参量不确定的网络与网络之间的混沌同步研究．通过设计适当的控制输入,不但实现了两个复杂网络之间的混沌同步,而且网络节点状态方程中的未知参量和网络内部节点之间的耦合强度也被同时确定．通过采用具有调制损耗的CO2激光器的状态方程进行仿真实验,发现网络与网络之间的同步性能非常稳定．     

应用可见/近红外光谱技术快速检测果珍中二氧化钛的含量

为了实现快速检测果珍中的二氧化钛含量,提出了应用近红外光谱技术结合化学计量学的快速检测方法。研究采用了320份果珍样本进行光谱特性的检测,其中200个样本用来建模,120个样本进行预测。首先比较了标准正态变量校正(SNV)、变量标准化(Normalize)、多元散射校正(MSC)等6种不同的数据预处理方法对偏最小二乘法(PLS)建模预测效果的影响。然后将PLS模型与应用主成分(PC)建立的主成分-神经网络校正(PC-ANN)模型进行比较。结果表明,MSC预处理的效果最好,PLS模型的最佳主成分数为7,预测值与标准值的相关系数R2达0.900 8,预测标准误差RMSEP为0.05。PC-ANN模型预测值与标准值的R2为0.868 4,RMSEP为0.04。说明PLS模型比PC-ANN模型的预测效果好。同时本研究也说明能够应用可见/近红外技术对二氧化钛进行快速定量测定。