基于SRLG约束和资源共享的交迭段保护机制研究

通过对共享风险链路组约束机制和交迭段保护机制的分析,将两者相结合并根据工作和保护路径分别采用动态链路权重调整,提出了一种基于共享风险链路组不相关的交迭段共享保护算法.交迭段共享保护算法在共享风险链路组不相关的约束条件下,为整个工作路由提供了多个交迭的保护段,并给出了一种能够合理有效选择这些交迭保护段的方法.对交迭段共享保护算法的分析和仿真结果表明,与以往保护算法相比,交迭段共享保护算法不但大大提高了网络连接的可靠性,而且还通过不同交迭保护段间资源的合理共享,有效地提高了网络资源的利用率.     

Analysis of Transmission Performance in SUPERPONs

In this work we study how optical amplifiers can deeply improve the performance of the passive optical networks (PONs), permitting a large number of optical network units (ONUs), transmitting at high bit rate. These high-capacity networks are called SUPERPONs, and we show how to achieve a network with 8,192 ONUs with a transmission in the downstream direction up to 10 Gbit/s.     

An efficient statistics-based method for the automated detection of sperm whale clicks

In this paper, we present a new method for the automated detection of sperm whale regular clicks and creaks based on statistical computations. In the first stage, a spectrogram is computed from the input waveform, followed by a noise normalisation process. A frequency domain filter is then applied, and the energy accumulated in each time frame is calculated. Two-second time-windows are then classified as containing either regular clicks, creaks, or noise based on statistical parameters using a neural network classifier. Finally, previously obtained statistical parameters are used to implement an energy-based detection criterion for the classified time-windows. Individual regular clicks and creaks are isolated by linking contiguous detected time frames. The proposed method was tested on five recordings of sperm whale sounds. Comparison of the detection performances to hand-labelled regular clicks and creaks revealed that this method outperforms two recently reported waveform-based methods when working with the same recordings files. An average percentage of detection of 86.97% was attained for the set of files. This method consumes also little computation time.     

Self-averaging in complex brain neuron signals

Nonlinear statistical properties of Ventral Tegmental Area (VTA) of limbic brain are studied in vivo. VTA plays key role in generation of pleasure and in development of psychological drug addiction. It is shown that spiking time-series of the VTA dopaminergic neurons exhibit long-range correlations with self-averaging behavior. This specific VTA phenomenon has no relation to VTA rewarding function. Last result reveals complex role of VTA in limbic brain. Received 17 April 2002 / Received in final form 30 September 2002 Published online 31 December 2002     

Effective medium representation and complex modes in 3D periodic metamaterials made of cubic resonators with large permittivity at mid-infrared frequencies

We review some of the techniques that lead to the effective medium representation of a three-dimensional (3D) periodic metamaterial. We consider a 3D lattice of lead telluride cubic resonators at mid-infrared (MIR) frequencies. Each cubic resonator is modeled with both an electric and a magnetic dipole, through a method called the dual dipole approximation. The electric and magnetic polarizabilities of a cubic resonator are computed via full-wave simulations by mapping the resonator's scattered field under electric/magnetic excitation only to the field radiated by an equivalent electric/magnetic dipole. We then analyze the allowed modes in the lattice, with transverse polarization and complex wavenumber, highlighting the attenuation that each mode experiences after one free space wavelength. We observe the presence of two modes with low attenuation constant, dominant in different frequency ranges, able to propagate inside the lattice: this allows the treatment of the metamaterial as a homogeneous material with effective parameters, evaluated by using various techniques. We then show that the metamaterial under analysis allows for the generation of artificial magnetism (i.e., relative effective permeability different than unity, including negative permeability with low losses) at MIR frequencies.     

Quantum control based on machine learning in an open quantum system

Designing robust control schemes in n-level open quantum system is significant for quantum computation. Here, we investigate two quantum control strategies based on supervised machine learning to suppress the quantum noise in an open quantum system. One is controlling state distance and the other is governing the average of a Hermitian operator. In this process, the dynamics of the system is mapped to a neural network where the control fields correspond to the weights. Besides, the system is transformed into the coherence Bloch space without using superoperator thus the complications are reduced largely. As an example, the two control protocols are demonstrated in a two-level and four-level systems, respectively. By applying these examples, the results show that the state of the system transfers to the target state and the average of a Hermitian operator to its minimum value in a given time despite disturbed by various types of noise.     

Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural NetworkSCIEI北大核心CSCD

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.     

Adaptive control of chaotic systems based on a single layer neural network

This Letter presents an adaptive neural network control method for the chaos control problem. Based on a single layer neural network, the dynamic about the unstable fixed period point of the chaotic system can be adaptively identified without detailed information about the chaotic system. And the controlled chaotic system can be stabilized on the unstable fixed period orbit. Simulation results of Henon map and Lorenz system verify the effectiveness of the proposed control method.     

一种基于激光直写的水浊度传感结构

提出并制作了一种基于激光照射改变折射率方法的光度比浊度传感单元,综合了导波光学及检测相关知识原理.以UV光敏树脂为基底材料,采用“气体干法”除胶及分步固化的方法获得了该芯片化结构.传感器整个制作过程仅仅需要7~8 min因而使本方法具有很高的效率.由直写成型的单元组建传感系统对标准悬浊液样品进行了实验测试,所得的相关数据在SISO算法神经元网络系统中进行处理,获得了传感器浊度与对应散射系数的关系曲线.通过试剂检验的方法得知实际相对误差在5%左右.     

Clusters in weighted macroeconomic networks: the EU case. Introducing the overlapping index of GDP/capita fluctuation correlations

GDP/capita correlations are investigated in various time windows (TW), for the time interval 1990–2005. The target group of countries is the set of 25 EU members, 15 till 2004 plus the 10 countries which joined EU later on. The TW-means of the statistical correlation coefficients are taken as the weights (links) of a fully connected network having the countries as nodes. Thereafter we define and introduce the overlapping index of weighted network nodes. A cluster structure of EU countries is derived from the statistically relevant eigenvalues and eigenvectors of the adjacency matrix. This may be considered to yield some information about the structure, stability and evolution of the EU country clusters in a macroeconomic sense.