燃料电池氢喷射器控制技术研究

为满足燃料电池功率需求,实现氢气侧压力平滑控制,提出了一种氢喷射器控制方案。通过周期性控制喷嘴数和peak-hold时间占空比,并采用了调节量屏蔽方法,以提高压力输出调节能力。建立实验系统,写入软件进行控制实验,表明了该方法的可行性。     

基于ARM的锂电池监控系统设计

安全问题日益成为锂电池技术发展的关键难题,对锂电池状态进行有效的监控显得尤为重要。本文设计了一种以ARM为控制核心的锂电池监控系统。详细描述了该系统的主要功能、硬件结构和软件设计。实现了96路单体电池的电压、16路电池温度以及电池放电电流的测量,并通过CAN总线将检测数据传送给上位机。实践表明该系统工作稳定可靠,检测精准,满足锂电池监控系统的设计需求。     

燃料电池动力系统氢气压力控制策略研究

为确保燃料电池动力系统安全性和可靠性,本文分析燃料电池动力系统氢气压力控制的影响因素,分析氢气系统回路的设备的流量特性,建立氢气回路压力控制的数学模型.在此基础上,提出压力反馈的PI控制与负载电流前馈控制结合的压力调控算法,并进行反馈和前馈控制参数的设计.在Matlab/Simulink中建立压力控制仿真模型,在给定阶跃、负载突变和排气阀开闭等条件下,对比论证所提算法的有效性.仿真结果表明,所提算法具有快速的跟踪响应能力,且对负载电流变化和排气阀开闭的扰动有很强的抑制能力.     

A probability approach to anomaly detection with twin support vector machines

AbstractClassification of intrusion attacks and normal network flow is a critical and challenging issue in network security study. Many intelligent intrusion detection models are proposed, but their performances and efficiencies are not satisfied to real computer networks. This paper presents a novel effective intrusion detection system based on statistic reference model and twin support vector machines (TWSVMs). Moreover, a network flow feature selection procedure has been studied and implemented with TWSVMs. The performances of proposed system are evaluated through using the fifth international conference on knowledge discovery and data mining in 1999 (KDD’99) data set collected at MIT’s Lincoln Labs and the results indicate that the proposed system is more efficient and effective than conventional support vector machines (SVMs) and TWSVMs.