从江苏苏南数字微波线路看广播电视专用网发展前景

新建微波干线电路采用高质量的数字微波设备是广电部科技工作会议的精神之一。本文介绍了江苏苏南数字微波线路，并从方案确定、技术实现、设备选型等方面进行分析比较，从而得到一个合理的整体方案。本文还着重阐述了数字传输的关键设备－编码器的现状与发展趋势，提出广播电视专用网多功能应用的设想。     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.     

接入网蓄电池的维护以及对整流器的要求

该针对阀控密封铅酸蓄电池的特性和使用环境，较全面的论述了接入网点蓄电池的选型要求与维护方式。     

基于神经网络的河道浅滩演变预测模型

河道浅滩演变是一个复杂的非线性动力学过程, 作者借助神经网络处理非线性问题的优势, 在分析影响河道浅滩演变因素的基础上, 建立了预测河道浅淮演变的BP网络模型, 并对模型中的输入因子和样本的提取进行了探讨. 以闽江竹岐至侯官河段为实例,用“试控法”给出了BP网络模型的建模方案, 用正交设计原理选取相应的训练样本集, 利用该样本集对网络进行学习和训练, 并用训练好的BP网络模型预测浅滩上年内最小水深和年平均淤积厚度. 计算结果表明: 模型预测结果与实际值吻合良好. 这为河道浅滩演变预测研究提供了新方法.     

Articulating User Needs in Collaborative Design: Towards an Activity-Theoretical Approach

This paper analyses the collaborative design ofa high-technology product, a neuromagnetometerused in the analysis of the activity of thehuman cortex. The producer, Neuromag Company istrying to transform the device from a basicresearch instrument into a means of clinicalpractice. This transition is analyzed as asimultaneous evolution of the product,producer-user network and user activities. Thenetwork is analyzed as a network of activitysystems. Each activity has a historicallyformed object and a motive of its own, as wellas a system of cultural means and expertise. Weuse these to explain and understand theinterests and points of view of the actors inrelation to the product and the contradictionsof the producer-user network. It is suggestedthat the emerging user needs of collectiveactors must be analyzed at three levels. At thefirst level, the use value of the product, itscapacity of solving the vital problems andchallenges of developing user activities, ischaracterized. The second-level analysisconcerns the creation and development of thenecessary complementary tools and services thatmake the implementation and use of the productpossible. This task presupposes collaborationbetween several communities of the innovationnetwork. The third level is the situatedpractical use of the product. In ourexperience, it is advantageous that researcherscontribute with their data to a dialogue inwhich the user needs are articulated.     

数值地形图的生成及其水文地貌特征评价

数值地形图(DEM)是利用地理信息系统(GIS)进行流域水文模拟的基础, 其精度直接影响模拟结果在没有现成的数值地形图时, 应用人员往往利用传统的地形图来生成它. 本文分别采用Arc/Info地理信息系统软件提供的哈奇逊法和不规则三角网五次插值法, 对一个实验流域的高程取样点进行插值, 生成了4种不同的数值地形图, 从凹洼分布、流域界定、河网提取、地面坡度和地形指数、以及地面径流汇成等方面, 对它们的水文地貌精度进行了比较. 结果表明, 两种方法都能生成由高程点群所反映的地形, 然而由于哈奇逊法能够结合流域的河网结构及其具有物理基础的水文地貌加强法来处理凹洼问题, 因此大大地提高了其所生成的流域数值地形图水文地貌特征精度.     

人工神经网络及其应用

本文首首先介绍了人工神经网络的源泉一体神经网络的主要基本内容，然后简要论述了人工神经网络的主要内容，进程和问题，人工神经网的应用。     

A Method of Combined SHPB Technique and BP Neural Network to Study Impact Response of Materials

Abstract:  A new method combining the split Hopkinson pressure bar (SHPB) technique with the back-propagation (BP) neural network program is proposed. By this method, the treated strain wave signals become smooth with less noises induced by the transverse inertia. Moreover, the dynamic rate-dependent constitutive behaviour of materials can be identified, without any pre-assumption of a constitutive model. It is found that by taking the experimentally measured data of strain, strain rate and time as 'input' and the corresponding data of stress as 'output' of the BP neural network, the dynamic constitutive behaviour with internal damage or phase transformation evolution is easy to be identified, where the time could represent either the internal damage evolution or phase transformation process accompanied with the deformation process. It is emphasised that the data learnt by the BP neural network must include both loading and unloading processes, if the whole loading and unloading response is to be correctly predicted. The comparisons between the predictions and experimental results are in good agreement for both polyamide (PA) polymer (as an example of nonlinear viscoelastic materials) and Ti–Ni alloy (as an example of superelastic materials with stress-induced phase transformation).     

BP网络过拟合问题研究及应用

通过类比信息传递过程中的一般测不准关系式,研究BP网络出现过拟合现象时,代表网络学习能力的权值改变量与反映网络推广能力的未参与训练的检验样本的辨识误差之间的关系.在该关系式中引入一个过拟合参数P,通过对岷江流域紫坪埔站的流量预报,说明在判断网络是否出现过拟合现象时,计算过拟合参数P是一种有研究意义的参考.