Dynamic Multicast Session Provisioning in WDM Optical Networks with Sparse Splitting Capability

In this work, we study dynamic provisioning of multicast sessions in a wavelength-routed sparse splitting capable WDM network with an arbitrary mesh topology where the network consists of nodes with full, partial, or no wavelength conversion capabilities and a node can be a tap-and-continue (TaC) node or a splitting and delivery (SaD) node. The objectives are to minimize the network resources in terms of wavelength-links used by each session and to reduce the multicast session blocking probability. The problem is to route the multicast session from each source to the members of every multicast session, and to assign an appropriate wavelength to each link used by the session. We propose an efficient online algorithm for dynamic multicast session provisioning. To evaluate the proposed algorithm, we apply the integer linear programming (ILP) optimization tool on a per multicast session basis to solve off-line the optimal routing and wavelength assignment given a multicast session and the current network topology as well as its residual network resource information. We formulate the per session multicast routing and wavelength assignment problem as an ILP. With this ILP formulation, the multicast session blocking probability or success probability can then be estimated based on solving a series of ILPs off-line. We have evaluated the effectiveness of the proposed online algorithm via simulation in terms of session blocking probability and network resources used by a session. Simulation results indicate that our proposed computationally efficient online algorithm performs well even when a fraction of the nodes are SaD nodes.     

A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

炼铁生产环境负荷分析和预测研究

利用寿命周期评价的思想，计算了炼铁生产过程中的环境负荷，并分析了各因素对环境负荷的影响，运用神经网络对炼铁生产过程环境负荷进行了预测。     

Agglomeration and networks in spatial economies

We consider the parallel developments in the economics of agglomeration and the economics of networks. We explore the complementarities between the productivity benefits of agglomeration and those of network linkages, arguing that networks of actors dispersed over space may substitute for agglomerations of actors at a single point.JEL Classification: D20, L14, R30     

Neural network systems to reproduce crash behavior of structural components

The use of neural networks as global approximation tool in crashworthiness problems is here investigated. Neural networks are not only asked to return some meaningful indices of the structural behavior but also to reproduce load-time curves during crash phenomena. To contain the number of examples required for the training process, parallel subsystems of small neural networks are designed. Design points for the training process are obtained by explicit finite element analyses performed by PAMCRASH. The settlement of the points in the design domain is defined using a maximum distance concept. The procedure is applied to different typical absorption structures made of aluminum alloy: riveted tubes, honeycomb structures, longitudinal keel beam and intersection elements of helicopter subfloors.     

基于神经网络基础上的两种系统安全综合评价方法

从工业企业生产发展的特点出发,在界定安全评价概念的基础上，从系统论的角度简要介绍了运用三层BP神经网络技术进行安全综合评价的方法和安全模糊综合评价方法。通过对这两种较为流行的安全评价方法进行分析，指出了传统评价方法的不合理性，辨证说明了这两种评价方法的优越性。     

微地震相分析在河流储层精细描述中的应用

传统地震相分析满足不了目前油田勘探开发的需要。神经网络微地震相分析将神经网络应用于高质量三维地震数据体中，可对单一反射同相轴进行波形信号分析和训练，建立模型地震道，并对实际地震道分类，进而得到在平面和剖面上精度较高的微地震相分布。采用该方法并结合测井资料，在研究区块中建立的微地震相分布图能够清晰地表现出该区块的沉积微相特征，且较好地解释了工区内2口井的产能差异，表明神经网络微地震相分析是可靠和有效的。     

基于ANN的声目标识别系统及其DSP实时实现

介绍了一种基于人工神经网络(ANN)的声目标识别系统,概述了用MATLAB专用工具箱对神经网络权值进行训练及仿真的过程,叙述了ANN目标识别系统的数字信号处理器(DSP)实时实现过程,并着重分析定点实现过程中程序变量的定标、非线性运算的实现、溢出的处理等关键步骤.对不同字长的识别结果进行比较表明,基于定点实时实现的系统数据保持很高的精度,可以得到与浮点处理相同的识别率.     

TCP/IP网络的动态模型描述

拥塞现象成为TCP/IP网络发展面临的一个重要问题。因此，拥塞控制对TCP/IP网络的鲁棒性和稳定性具有重要作用。目前，网络拥塞控制策略主要包括两类：端到端的控制机制，如TCP拥塞控制算法；网络内部的主动队列管理(AQM)策略。但由于缺乏对网络系统动态特性的了解，这些拥塞控制策略大都基于专家经验，并没有建立完整的理论分析框架。为此，本文从数据流的角度出发，通过建立网络基本单元状态方程模型来实现IP网络系统的数学解析模型，然后用混杂系统来描述TCP带有拥塞控制策略的数据传输过程，建立了TCP/IP网络的动态模型，为网络系统中动态性能的分析、拥塞控制策略的设计奠定了基础。实验结果表明，该数学模型与NS仿真实验的结果相一致。     

神经网络系统学习过程初探

本文给出了神经网络系统学习过程的一个随机模型--马尔柯夫模型,从最大熵原理的 观点讨论神经网络系统的学习过程,提出将神经网络系统的学习过程分为两个阶段的想法并 给出学习过程的算法.