SMS——智能网的业务管理系统

本文叙述了智能网的业务管理系统所实现的３种功能：业务管理功能，网络管理功能和业务管理访问功能，还介绍了业务管理和网络管理之间的关系，ＳＭＳ的集中式和分布式两种体系结构及业务管理功能的设计方法和进一步研究方向。     

Using neural networks to learn hand-eye co-ordination

The work presented in this paper shows how the association of proprioceptive and exteroceptive stimuli can enable a Kohonen neural network, controlling a robot arm, to learn hand-eye co-ordination so that the arm can reach for and track a visually presented target. The approach presented in this work assumes no a priorimodel of arm kinematics or of the imaging characteristics of the cameras. No explicit representation, such as homogeneous transformations, is used for the specification of robot pose, and camera calibration and triangulation are done implicitly as the system adapts and learns its hand-eye co-ordination by experience. This research is validated on physical devices and not by simulation.     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

无线传感器网络MAC层协议的对比研究

无线传感器网络是二十一世纪新兴的网络技术，它的出现使网络的发展进入一个新的阶段。针对无线传感器网络MAC层协议的研究一直是人们普遍关注的问题。总结了近年来的研究成果并针对TDMA，IEEE802．15．4，S—MAC，T—MAC几种典型的MAC层协议进行了对比分析，根据其各自的优点提出将来工作的方向和建议。     

Estimation of fouling in a plate heat exchanger through the application of neural networks

Pasteurization is an important, if not the most important, process step in the packaging of milk. It is subject to alterations stemming from the variation in the temperature, pH and raw milk quality. The variability may manifest itself in changes in the formation of the deposit (fouling) in the pasteurization unit, such that there is a need for tools, both instrumentation and computational, to help in monitoring the process and keeping it on the desired course. In this paper we describe a practical procedure based on artificial neural networks (ANN) that allows prediction of the deposit thickness, the overall heat transfer coefficient and the critical time (the time that the unit has to be stopped for cleaning) for reducing the impact of fouling on such processes. The procedure determines when the cleaning operation is required once the system is under critical conditions of operations. A combination of fundamental studies and plant measurements were used for study of the operating conditions and thus evaluation of the trades‐offs between operating conditions and longer operating life span. The results are encouraging, enough to validate current operating industrial techniques. Copyright © 2005 Society of Chemical Industry     

An evaluation system of the setting up of predictive maintenance programmes

Predictive Maintenance can provide an increase in safety, quality and availability in industrial plants. However, the setting up of a Predictive Maintenance Programme is a strategic decision that until now has lacked analysis of questions related to its setting up, management and control. In this paper, an evaluation system is proposed that carries out the decision making in relation to the feasibility of the setting up. The evaluation system uses a combination of tools belonging to operational research such as: Analytic Hierarchy Process, decision rules and Bayesian tools. This system is a help tool available to the managers of Predictive Maintenance Programmes which can both increase the number of Predictive Maintenance Programmes set up and avoid the failure of these programmes. The Evaluation System has been tested in a petrochemical plant and in a food industry.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

Pancyclicity of recursive circulant graphs

In this paper, we study the existence of cycles of all lengths in the recursive circulant graphs, and we show a necessary and sufficient condition for the graph being pancyclic and bipancyclic.     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

两相电流互感器配电网零序电流构造方法

中性点非直接接地配电网大多只安装A，C两相电流互感器（TA），而现有单相接地故障选线方法基本都以零序电流为基础实现。如何在两相TA配电网中准确检测并隔离接地故障线路，成为长期困扰实际运行的技术难题。通过对暂态电流的频域和时域分析，找出B相暂态电流与A，C相暂态电流的对应关系，提出在A相和C相接地时由A相和C相暂态电流构造B相暂态电流，进而得到较精确零序电流的方法。通过大量仿真实验验证了所提出的构造方法的正确性。零序电流的成功构造为实现两相TA配电网单相接地故障选线提供了基础。