红外光谱统计波长选择方法用于废气检测

为了从含有噪声的大气红外光谱中提取微量待测污染组分的定量特征,进而建立校正模型,本文提出了一种基于统计理论的波长选择方法。该方法针对待测组分,在对光谱各波长位置的噪声强度进行统计估计的基础上,提出了选择最佳波长子集的目标函数。该目标函数包含波长子集的噪声和长度参数,这使得在最小化模型误差的同时也防止了模型规模的无限制膨胀。为了检验该方法的性能。文章利用含背景噪声的实测光谱数据针对三种气体进行了波长选择,并利用神经网络技术分别建立了校正模型。实验结果与波长子集的优化结果相符,所选择的波长子集的长度不足光谱波长总点数的2％,同时,光谱中的噪声也在校正模型中得到了明显的抑制。实验结果证明了该波长选择方法的有效性。     

Information filtering in evolving online networks

Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics.     

Localization of diffusion sources in complex networks with sparse observations

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum–minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.     

Simultaneous Determination of Multicomponents in Air Toxic Organic Compounds Using Artificial Neural Networks in Ftir Spectroscopy

The application of Artificial Neural Networks (ANNs) for nonlinear multivariate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using the back-propagation learning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in order to get a satisfactory combination of all parameters. The mixtures of four air toxic organic compounds whose FTIR spectra are overlapped were chosen to evaluate the calibration and prediction ability of the network. The relative standard error (RSD%), the percent standard error of prediction samples (%SEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.     

Overview of DWDM/ODC Project

Abstract

This article introduces the main achievements resulting from the DWDM/ODC project. The five areas of research activity within the DWDM/ODC project cover some of the main issues of design and development of dense wavelength division multiplexing systems for transparent optical networks. These issues are: performance assessment with arbitrary optical filtering; performance of signaling formats; dispersion compensation strategies for directly and externally modulated systems in presence of nonlinear transmission-induced degradation; and the impact of noise and crosstalk in the extent of transparent optical networks. All five areas of research activity have contributed significantly to a better understanding of the limitations present in dense wavelength division multiplexing systems.     

复杂网络思想方法在大学物理教学中的应用

复杂网络是近年来复杂性研究的热点领域,与物理学的发展有密切的联系.让学生了解该领域的基本概念、方法及其与物理之间的关系大有裨益.本文对复杂网络中的一些基本概念和思想进行了简要介绍,并利用复杂网络的基本概念和思想对物理教学中两个较复杂的典型问题进行了分析.     

基于休眠机理的三维小基站蜂窝网络能效优化

小基站通常部署在写字楼、商贸区等城市密集区域以弥补传统宏基站在覆盖和传输方面的不足.小基站的分布一般是根据高峰时的网络负荷设计的,这必然导致网络负荷较低时的资源浪费.讨论了在平均接入率和信道容量双重约束下基于休眠机理的三维小基站蜂窝网络的能效优化问题.借助泊松点过程理论推导了三维小基站网络下行信道容量和平均接入率的数学表达式.通过分析下行信道容量和平均接入率的单调性得出同时满足传输信道容量和接入率要求的最佳休眠概率.分析了小基站最大用户连接数的最佳值,通过对该参数的合理配置,可以在满足通信指标的前提下最大程度地降低网络能耗.仿真结果表明,设计的基站休眠机理可以使小基站网络的能耗下降约21%.     

应用BP神经网络识别内层表面细小缺陷的研究

内层表面细小凹坑的识别是超声无损评估的一个难点。本文利用人工神经网络对于信号的分类功能,建构和训练了一个BP神经网络,并用它对尺寸为1mm的圆锥形和半球形两种凹坑成功地进行了识别。研究表明,应用凹坑回波的DCT谱作为缺陷特片输入,可使BP神经网络的训练和缺陷识别既快捷又有效。     

基于炉口辐射和改进神经网络的转炉终点预测模型

针对国内外转炉炼钢终点控制的现状,建立了一种用于终点预测的神经网络模型.以炉口辐射信息获取系统为实验平台,运用光纤谱分复用和颜色空间模型转换技术.分析发现了光谱与图像信息特征量在吹炼过程中晕现出中前期类似、末期相反的规律.从得到的特征规律曲线中选用一些关键特征量,在改进的修正系数算法基础上,进行了模型的训练和预测分析.实验结果表明:响应时间在2 s以内,满足快速判定的时间要求;改进算法的模型预测精度高于常规算法,该系统可以止常工作在转炉炼钢的恶劣环境下,达到了预期效果.     

基于DCGANs的半片光伏组件电致发光图像增强技术

针对半片光伏组件电致发光（electroluminescence,EL）缺陷自动识别过程中训练用样本不足导致模型过拟合的问题,采用深度卷积生成对抗网络（deep convolutional generative adversarial networks,DCGANs）生成可控制属性的半片光伏组件EL图像,再采用多尺度结构相似性（multiscale structural similarity,MS-SSIM）指标对生成的EL图像与拍摄的EL图像之间的相似程度进行了评估。评估结果得到,使用DCGANs生成的所有类型半片光伏组件的EL图像与拍摄的EL图像的MS-SSIM指标都大于0.55,大部分的MS-SSIM值在0.7附近。在分类模型的训练过程中,测试集准确率随着训练集中生成图像数量的增加而升高,当生成图像数量达到6 000张时,测试集准确率达到97.92%。实验结果表明,采用DCGANs能够生成高质量且可控制属性的半片光伏组件EL图像,较好地解决因缺少训练样本而导致的模型过拟合问题。