基于无线网络技术的燃气集抄系统设计

针对燃气行业人工抄表费时费力等问题,提出一种基于GPRS和无线多跳网络技术的燃气无线抄表系统,在分析传统抄表系统的结构以及传输方式优缺点的基础上介绍了系统的总体结构、工作原理以及各部分组成和功能。采用计算机网络和数据库等技术,使得该系统具有功能强大、使用方便、可扩展性好等特点,可实现对整个燃气输配系统的监控,有效提高燃气公司的管理水平和服务效率。     

Embedding multi-dimensional meshes into twisted cubes

The twisted cube is an important variant of the most popular hypercube network for parallel processing. In this paper, we consider the problem of embedding multi-dimensional meshes into twisted cubes in a systematic way. We present a recursive method for embedding a family of disjoint multi-dimensional meshes into a twisted cube with dilation 1 and expansion 1. We also prove that a single multi-dimensional mesh can be embedded into a twisted cube with dilation 2 and expansion 1. Our work extends some previously known results.     

高速网络Fast TCP拥塞控制算法的改进

随着网络技术的飞速发展和接入性能的不断提高,如今全世界的互联主干网络呈现出一种高速网络的特性。在这种网络特性下,传统网络中的TCP拥塞控制协议已经开始显现出不适应性,发现了FAST TCP协议中＂持续拥塞＂的形成机制,建立了数学模型来准确的描述＂持续拥塞＂所导致数据流之间带宽分配不公平的问题,进一步利用优先服务队列的方法解决了FAST TCP在网络中的持续拥塞现象。     

Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters. Artificial neural networks model exhibits higher performance than traditional statistical model for predicting deformation modulus and coefficient of subgrade reaction.     

Properties and performance modelling of finite buffer M/G/1/K networks

Finite buffer, single-server queueing systems and networks are difficult to analyze since the length of time a customer spends in the system does not follow the Markovian property. A two-moment approximation schema is developed for the probability distribution of M/G/1/K systems and extended to the analysis of M/G/1/K   queueing networks. The general purpose of this paper is to develop a flexible and practical transform-free approach for computing the probability distribution and performance measures of the system as well as identify the underlying properties of these systems. It is shown that for most performance measures, a sigmoid or S-shaped curve with an inflection point at ρ=1$\rho =1$ appears as K→∞$K\to \infty$. This has direct implications for the analysis and optimization of such systems. The performance modelling of the M/G/1/K queueing networks of general topologies along with extensive numerical results accompany the paper along with the linear concave performance measures for these systems.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

无线光电定位网络的设计

为满足非接触化、小型化、集成化、数字化、智能化等现代检测技术的发展要求,从成本、工作原理、灵敏度等方面综合考虑,现采用以光电定位模块为核心的光电定位技术,以无线传感器网络作为数据传输与管理的技术手段,构建了无线光电定位网络,并进行了实验分析.可实现多点实时监测、异地显示测量数据、数据网络化管理等特殊需求;涵盖了多学科领...     

移动自组网中非完全信息节点风险评估

移动自组网是一种无中心、自组织以及多跳的无线网络,能否有效可靠地对通信节点进行风险评估是保障节点高质量通信的重要因素.当前基于可信度或是信誉的评估方法对评估样本都有特殊的要求,对评估对象提出种种假设,无法辨别间接样本的恶意推荐问题,在实际移动自组网中各个节点尤其是恶意节点对外暴漏的信息往往偏少,样本数量有限,无法明确判断其状态,大部分是非完全信息,从而导致评估结果缺乏客观性与可靠性.文中提出采用灰色系统理论描述通信节点非完全信息状态,根据灰类白化以及灰色聚类思想进行节点风险评估.方案针对自组网络通信需求对节点行为进行多关键属性划分,采用味集群方法收集原始样本,避免恶意推荐;将难以用数值精确刻画的关键属性以白化权函数量化,引入灰聚类的概念和计算方法,将实体的通信风险水平定义为实体关键属性值序列针对各评估灰类的聚类评估值,从而得到该实体通信风险水平的相对参考值.分析与实际计算表明该方法是一种适合移动自组网中非完全信息节点风险评估的有效方法.     

The scope of artificial neural network metamodels for precision casting process planning

Precision investment casting process planning has been tackled in the past according to experience. Recently, casting simulation software is being increasingly used to predict product quality by implementing ‘what-if’ scenarios. Input parameters include relatively simple factors such as mould temperature, melting temperature, casting material. They also include factors whose influence is more complex to quantify, such number and location of feeding points, diameter and length of inflow channels, angle of channel with respect to the main sprue axis. Simulation results cannot help the engineer for workpieces other than the one simulated. In this paper a series of feedforward artificial neural network (ANN) models is presented aiming at such generalisation. To achieve this, a large number of software simulation runs were conducted for a number of different small parts, with varying runner geometry and casting conditions. The parameters characterising part geometry have been chosen to be surface area and volume-to-area ratio. The different ANN models predictive capabilities are reflected to the respective training and generalisation errors. A user-friendly interface has been conducted for model execution in a complete application, whose main virtue is expandability.     

A fuzzy-based interleaved multi-hop authentication scheme in wireless sensor networks

In sensor networks, a compromised node can either generate fabricated reports with false votes or inject false votes into real reports, which causes severe damage such as false alarms, energy drain and information loss. An interleaved hop-by-hop authentication (IHA) scheme addresses the former attack by detecting and filtering false reports in a deterministic and hop-by-hop fashion. Unfortunately, in IHA, all en-route nodes must join to verify reports while only a few are necessary to the authentication procedure. In this paper, we propose a fuzzy-based interleaved multi-hop authentication scheme based on IHA. In our scheme, the fuzzy logic system only selects some nodes for verification based on the network characteristics. Moreover, we apply a voting method and a hash-based key assignment mechanism to improve network security. Through performance evaluation, the proposed scheme is found to save up to 13% of the energy consumption and to provide more network protection compared to IHA.