Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

基于PicoBlaze的多软核网络处理结构研究

介绍一种基于多个8位CPU软核PicoBlaze的网络处理结构.该结构利用现场可编程门阵列(FPGA)的并行性和网络协议分层结构的特点,可以实现一些相对复杂的网络处理功能,并且易于扩展和开发.通过在FPGA上完整实现一个远程启动服务器端的设计实例,阐明具体的设计方法.     

测试稿件

复杂网络理论是时间序列分析中一种有力的工具。但在面对高频数据时,现有建网方法是低效的。因此,提出利用时间序列符号化技术压缩原始序列,并构造网络的方法。该方法使用最小二乘估计时序分段斜率,提取序列的局部特征,并构造字典判断节点是否邻接。模拟试验表明,所建网络的标度指数、集群系数与过程的Hurst指数高度相关,可以精确地捕捉原过程的复杂性特征     

基于事件驱动的动态免疫分簇路由算法*

为了降低应用于突发事件监测的无线传感器网络的能量消耗,设计实现了一种基于事件驱动的动态免疫分簇路由算法。将生物免疫系统的工作机制应用到无线传感器网络事件驱动的动态分簇算法中,事件作为抗原,传感器节点作为抗体,抗体对抗原有记忆保存的功能,使得相似的抗原再次出现时对事件及时响应。相似事件再次发生且传感器节点符合能量要求时,可以直接调用抗体中的记忆,对事件进行快速建簇,节省了簇的建立过程所消耗的大量能量,增加了网络的数据传输量,延长了网络的生命周期。仿真结果表明,生物免疫机制的学习记忆特性可以有效提高事件驱动的动态分簇算法的网络性能。     

Design optimization of resource combination for collaborative logistics network under uncertainty

Collaborative logistics networks (CLNs) are considered to be an effective organizational form for business cooperation that provides high stability and low cost. One common key issue regarding CLN resource combination is the network design optimization problem under discrete uncertainty (DU-CLNDOP). Operational environment changes and information uncertainty in network designs, due to partner selection, resource constrains and network robustness, must be effectively controlled from the system perspective. Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper. Phase 1 calculates the simulation result of each hypothetical scenario of CLN resource combination using the expected value model with robust constraints. Phase 2 selects the optimal network design scheme for DU-CLNDOP using the orthogonal experiment design method. The validity of the model and method are verified via an illustrative example.     

使用PVLAN设计有利于信息化教学的校园网

很多校园网因规划设计不合理而导致其对信息化教学的服务程度不高,校园网应不仅仅只提供简单的上网功能,更需要把各种信息化功能的服务器高速无缝地融合到校园网中,为此提出在交换机上使用PVLAN（Private VLAN）技术。根据校园功能区不同划分校园网,使用PVLAN中的Community、Isolated VLAN特点来进一步细化VLAN划分和数据的流控,借助Promiscuous端口模式的特性,灵活接入各种服务器,给师生的教学、学习、办公、实训提供快捷、安全和全方位的信息化服务。     

开放的大学英语网络教学运行环境和模式研究

通过对大学英语网络自主学习运行环境和教学模式的研究,介绍了现有网络学习运行环境包含的各种网络结构、软件架构、学习和考核方法等几个方面,讨论了各种环境因素在实际应用中的优劣与取舍原因,给出了开放教学理念下的网络运行环境研究结果。在研究中能给学生提供充分思考的空间、促进学生在学习中的独立思考和探索精神,进而达到学生素质全面发展的目的。     

无线传感器网络中一种高效IBE方案

无线传感器网络的应用日益广泛但安全性问题越发突出,由于其计算能力、存储能力和通信能力有限,一直被认为不适合使用公钥密码学方案。然而近年来不需要认证中心的IBE算法的研究成果表明,IBE方案非常适合无线传感器网络。本文提出一种高效的IBE方案,并做了算法介绍,同时在无线传感器网络中提出相应的密钥分配、认证、管理以及更新的应用方案。与现有其他算法相比,新方案具有系统参数短、安全性高等特点。     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.