基于NS2的Ad Hoc网络DRMR协议计算机仿真

针对现存多种Ad Hoc组播路由协议的有效性差、控制开销大等问题,设计并实现了一种基于动态广播环的组播路由(DRMR:Dynamic Ring based Multicast Routing)协议,确定了DRMR协议的实现框架,定义了各功能模块.在该协议中,组播路由建立和维护每个组成员广播环所构成的连通环图,当组成员广播环离开连通环图时,采用逐步扩大广播环的半径恢复其连通性.仿真结果表明,DRMR协议具有较高的数据分组递交率和较低的路由维护开销,其总体控制开销比基于需求的多播路由协议节省,能满足Ad Hoc网络对组播应用的要求,对研究Ad Hoc网络人员具有实际参考价值.     

WSN中的路由洞避开机制

贪婪路由选择算法是一种简便高效的路径建立算法,它建立的路径具有非常好的方向性和最优性,是无线传感器网络中基于地理位置的路由算法中非常重要的算法之一,但是,这种算法遭遇到路由空洞问题的可能性比较大.研究了一种路由洞避开机制,仿真结果表明:算法在保证良好的路由方向性的基础上还可以很好地避开路由洞,不仅增加了网络的存活时间,还特别适合应用在异构网络中.     

基于按需和贪婪转发的移动自组网路由协议

移动自组网的动态拓扑特性给路由协议的设计带来了一定的挑战,尤其是在高动态的网络环境中.本文针对该问题,提出了一种新的基于按需和贪婪转发的路由协议,该协议是在RGR模式的基础上提出以下三点改进,即:（1）通过受限的洪泛机制降低网络在路由发现阶段的控制开销;（2）通过移动预测机制,在被动寻路阶段监视被动路径的状态和在GGF阶段帮助节点选取适当的邻居作为下一跳节点;（3）通过路径请求延迟机制以减少不必要的资源浪费.仿真结果表明:改进的RGR协议与现有的RGR、AODV、Modified-RGR和Optimized-RGR相比,不仅具有较高的数据包接收成功率,而且平均路由开销和端到端时延也相对较低.      

动态路由协议OSPF原理和特性

OSPF是一种广泛使用的路由协议。介绍了自治系统内部的链路状态，协议OSPF的原理、特性、工作过程，以及为保证在大规模网络中的可用性所采取的特殊机制。     

分布式分级结构的用户路由行为模型

为了减少Internet中用户自私的路由行为对网络性能的损害和避免低效路由的出现,针对Internet中用户不平等、分等级的角色,研究了非合作用户的路由行为模型.描述了Stackelberg路由博弈的行为框架(不同数目的领导者和跟随者),将Stackelberg博弈应用于分级用户的路由博弈行为模型.论证了Stackelberg均衡点的存在性和唯一性,并定量地讨论了均衡点上的路由向量;最后,进一步讨论了在实际网络中"原子"和"非原子"路由博弈的问题,从而论证该模型的应用价值.     

一种无线温度传感器网络中节能的路由协议

无线温度传感器网络中,传感器节点经常被部署在大面积的农田环境里,由于网络规模大、节点数目多、节点能量有限等特点,如何延长网络的寿命是最为重要的事情.为了延长节点的存活时间,就要求在节点通信过程中,最大限度的节省节点能量,为此提出了一种高效的路由协议来解决这类问题,结果表明,采用该方案节省了节点能量,延长了网络的生存时间.     

车载通信网中基于DSR分层机制的移动代理路由策略研究

分析了现有动态源路由(dynamic source routing,DSR)协议机制以及在车载通信网路由发现中存在的问题,提出一种基于DSR分层机制的移动代理路由策略(cluster mobile agent-dynamic source routing,CM-DSR),并对设计的算法进行仿真实验.结果表明,所提出的CM-DSR协议与DSR路由协议相比较,较大幅度地降低了端到端的传输延迟,减少了路由请求次数,包的提交率也有一定程度的提高.     

0SPF动态路由协议在酒钢光纤骨干网上的应用

本以酒钢光纤骨干网为基础，全面论述了以太网中0SPF动态路由协议实现的手段和方法，并对IP地址规划技术、动态路由聚合技术、动态负载均衡技术进行了详细的阐述，同时对酒钢光纤骨干网的进一步的扩展提出了方法和见解。     

基于蚁群的无线传感器网络概率路由算法

针对多数无线传感器网络路由算法易在网络中形成关键节点,而节点的失效往往导致整个网络的失效问题,提出了将随机思想与蚁群算法的特征相结合的一种新的概率路由计算方法.实验表明,该算法对延长整个网络的有效生存时间的效果较好.     

动态分簇的异构传感器网络安全路由协议

当前很多无线传感器网络路由协议没有将安全因素考虑在内,而且一些已有的安全方案没有紧密结合典型分簇路由协议的运行机制,因此提出一种基于动态分簇的异构传感器网络安全路由协议(secure routing for dynamic clustering,SRDC).该协议在优先考虑能耗问题的基础上,通过多种安全机制实现密钥协商和节点认证以及高效的组密钥更新.实验表明,新的协议能够有效延长网络的生命周期,并且能给网络提供较强的抗俘获能力.     

Graph-based single-copy routing in delay tolerant networks

This paper presents a new graph-based single-copy routing method in delay tolerant networks (DTN). With time goes on in the networks, a DTN connectivity graph is constituted with mobility ofnodes and communication, and a corresponding greedy tree is obtained using a greedy algorithm inDTN connectivity graph. While there are some bad nodes such as disabled nodes or selfish nodes in delay tolerant networks, the nodes can choose the next proper intermediate node to transmit the mes- sage by comparing the location of neighboring nodes in the greedy tree. The single-copy routing method is very appropriate for energy-constrained, storage-constrained and bandwidth-constrained applications such as mobile wireless DTN networks. We show that delivery ratio is increased signifi- cantly by using the graph-based single-copy routing when bad nodes exist.     

Stage–Discharge Relations for Low-Gradient Tidal Streams Using Data-Driven Models

Development of stage–discharge relationships for coastal low-gradient streams is a challenging task. Such relationships are highly nonlinear, nonunique, and often exhibit multiple loops. Conventional parametric regression methods usually fail to model these relationships. Therefore, this study examines the utility of two data-driven computationally intensive modeling techniques namely, artificial neural networks and local nonparametric regression, to model such complex relationships. The results show an overall good performance of both modeling techniques. Both neural network and local regression models are able to predict and reproduce the stage–discharge multiple loops that are observed at the outlet of a 28.5?km2 low-gradient subcatchment in southwestern Louisiana. However, the neural network model is characterized with higher prediction ability for most of the tested runoff events. In agreement with the physical characteristics of low-gradient streams, the results indicate the importance of including information about downstream and upstream water levels, in addition to water level at the prediction site.     

Prediction of Engineering Performance: A Neurofuzzy Approach

Engineering and design professionals constitute a major driving force for a successful project undertaking. Although the industry has been active in addressing the performance of construction labor and methods to estimate or predict such performance, relatively fewer efforts have been conducted for the engineering profession. In an attempt to fill out this gap, the paper presents a study to utilize neurofuzzy intelligent systems for predicting the engineering performance in a construction project. First, neurofuzzy systems are introduced as integrated schemes of artificial neural networks and fuzzy control systems. The use of these neurofuzzy intelligent systems, particularly fuzzy neural networks, in predicting engineering performance is then demonstrated in the industrial construction sector. The development of the system is based on actual project data that was collected through questionnaire surveys. Statistical variable reduction techniques are further employed to develop linear regression models of the same engineering performance prediction scheme, and results are being compared between both techniques.     

Lipid peroxidation and phospholipid composition in rat brain regions after ischemia and in early perfusion periods

A configuration of bi-group neural networks (BGNN) is proposed combined with an evidential reasoning framework to interpret 12-lead electrocardiograms for three mutually exclusive classes. A number of pre-processing feature selection techniques were investigated prior to application of the input feature vector to each individual BGNN. The network outputs were discounted within a belief interval of 1 based on their performance on test data prior to combination. It was found that the application of the feature selection techniques enhanced the individual performance of the BGNN, and subsequently enhanced the overall performance. The proposed framework was compared with conventional classification techniques of multi-output neural networks and linear multiple regression. The framework attained a higher level of classification in comparison with the other methods; 70.4% compared with 66.7% for both multi-output neural and statistical techniques.     

Modeling of Hyperconcentrated Sediment-Laden Floods in Lower Yellow River

This paper presents a rapid forecast model for simulating hyperconcentrated sediment-laden floods in the Lower Yellow River. The model is a hybrid of a conventional one-dimensional mathematical model for unsteady sediment-laden flow and an artificial neural networks model for encapsulation of numerical results. The former provides detailed river flood routing information under typical scenarios, whereas the latter extracts modeling outputs from the former and establishes a station-specific model for efficient flood forecasting. Three typical floods that occurred in the Lower Yellow River in 1977, 1982, and 1996 are simulated. Not only the hybrid model predictions are found to be in close agreement with measured data, but also the computational speed is significantly enhanced. It is found that sediment transport is of significance with regard to the flooding behavior of hyperconcentrated flows. Therefore, the model presented herein is of particular use for rivers with high sediment concentration.     

神经元网络技术在连铸漏钢预报中的应用

介绍在连铸神经元网络漏钢预报系统中开发应用的人工神经元网络技术。对人工神经元网络的结构选型和训练进行了说明。现场运行结果表明：该系统对于不同的生产条件和工艺参数均具有良好的性能。     

Efficient Algorithm for Gradually Varied Flows in Channel Networks

This paper presents an efficient solution technique for one-dimensional unsteady flow routing through a general channel network system—dendritic, looped, divergent, or any combination of such networks. The finite difference method is used to solve the de St. Venant equations in all the branches of the network simultaneously. The number of equations to be solved at a time during any iteration is reduced to only four times the number of branches of the network. This results in a significant reduction in storage requirements and solution time. Importantly, the algorithm does not require any special node numbering schemes and the nodes can be numbered independently for each branch. The algorithm is also suitable for programming on a parallel-processing computer.     

Brain imaging and age-related changes in cognition

Although there is currently some debate as to the degree of structural changes in the brain that occur with age, there is little doubt that such changes occur. There also are physiological changes in many areas that could have implications for cognitive function in the elderly. One way to study the impact of these age-related changes in the brain on cognition is to use neuroimaging techniques to examine brain activity during the performance of various tasks, and determine how this activity differs between young and older individuals. This approach has been used to study functions such as memory, perception, and attention, and it has generally been found that older individuals utilize different areas of the brain than do young subjects when carrying out the same cognitive task. This has led some researchers to suggest that older persons utilize different functional brain networks, perhaps to compensate for reductions of efficiency in some brain areas. The areas of the brain most often found to be more active during cognitive tasks in the elderly are the frontal lobes. Studies that have directly examined the functional networks utilized during cognition have found that older people do indeed have different functional interactions involving the frontal lobes, and therefore, utilize different functional networks. In some cases this differential activity has been accompanied by cognitive performance in the older participants that is equivalent to that seen in the young, suggesting that greater reliance on this brain region is related in some way to the maintained ability of the older individuals to perform the task. However, data collected to date on this issue are still limited, so although the evidence is intriguing, the definitive interpretation of these findings must await further experiments.     

Prediction of Sewer Condition Grade Using Support Vector Machines

Assessing the condition of sewer networks is an important asset management approach. However, because of high inspection costs and limited budget, only a small proportion of sewer systems may be inspected. Tools are therefore required to help target inspection efforts and to extract maximum value from the condition data collected. Owing to the difficulty in modeling the complexities of sewer condition deterioration, there has been interest in the application of artificial intelligence-based techniques such as artificial neural networks to develop models that can infer an unknown structural condition based on data from sewers that have been inspected. To this end, this study investigates the use of support vector machine (SVM) models to predict the condition of sewers. The results of model testing showed that the SVM achieves good predictive performance. With access to a representative set of training data, the SVM modeling approach can therefore be used to allocate a condition grade to sewer assets with reasonable confidence and thus identify high risk sewer assets for subsequent inspection.     

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The sintering basic performance of iron ore fines and its research status were overviewed, the existing research methodologies and techniques of sintered ore blending were analyzed, and the further development and improvement of sintering optimization ore matching was prospected. It is suggested that the establishment of integrated comprehensive system of sintering ore, which is based on the iron ore sintering performance, is one of the development direction of sintering optimization ore matching. It should make full use of advantages of neural networks and Matlab mathematical software to develop the expert system for sintering optimization ore matching.