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.     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.     

A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour

The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning.     

Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving.     

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm.The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis.     

基于频繁项挖掘的未知P2P流量识别方法

提出一种基于多维聚类的频繁项挖掘算法,利用聚类思想自动挖掘网络中的显著流量及其规则,并在此基础上,对显著流量进行P2P疑似性判别,同时结合应用层特征识别技术,对高度疑似的P2P显著流量类进行过滤,实现未知P2P流量检测。实验结果表明,该方法是有效的。     

基于监控器时间开销的虚拟机发现方法

针对传统方法只能发现单一类型虚拟机的缺陷,提出基于虚拟机监控器时间开销的虚拟机发现方法。特定指令能使监控器运行时产生显著的额外开销,该方法能利用监控器执行不同指令序列产生的相对时间开销对虚拟机进行判别。实验结果表明,该方法能够准确发现目前3类主流虚拟机。     

基于OPNET的AdHoc网络建模与仿真

利用OPNET软件自带的支持IEEE802．11标准的节点模型构建AdHoc网络模型。合理配置输入输出接口的参数,使网络达到最佳性能,并对网络的性能进行比较分析。仿真结果表明,网络参数之间存在关联性,网络最优化参数具有唯一性,通过网络负载与标准负载的比较,可选择出网络性能最优化状态。     

基于GIS和神经网络的泥石流危险性评价系统

基于ArcEngine组件和．Net集成开发天山公路泥石流危险性评价系统,将GIS技术和神经网络集成应用于泥石流危险性评价中,发挥GIS强大的空间信息可视化管理和分析功能以及神经网络的非线性描述和分析功能,实现泥石流危险性评价的可视化管理,为工程决策者和管理人员提供一种强有力的决策支持方案。     

基于OMNeT++的Ad Hoc网络跨层协议栈仿真

针对OMNeT++的MFW组件中物理层和MAC层模块的错误进行修正和功能改进,实现基于RBAB的IEEE 802.11b协议速率自适应数据传输,建立移动Ad Hoc网络跨层协议栈模型的网络仿真框架,实现将Ad Hoc网络MAC层的速率自适应机制与其路由协议联合优化设计的仿真。结果表明,在移动环境下,采用跨层设计的网络协议栈能大幅提高Ad Hoc网络系统的性能。