旱情预警几个主要技术问题的探讨

旱灾是我国主要的自然灾害之一。近年来,我国干旱灾害发生频繁,对经济社会造成的影响不断加剧。加强旱情监测,提高旱情综合评判能力,探索旱情预警方法等,显得十分必要和紧迫。目前,我国旱情监测工作相对薄弱,旱情预测预报起步较晚,旱情预警尚处于研究和探索阶段。本文结合吉林省旱情预警模型研究的有关成果,探讨预警指标、预测模型、预警综合指数等主要技术问题的创建思路,为旱情预警系统研究提供借鉴和参考作用。     

高超声速巡航导弹等速巡航段探测概率建模研究

为提高高超声速巡航导弹等速巡航段的探测概率,分析了高超声速状态下高超声速巡航导弹等速巡航段探测能力,并对探测概率的计算进行了研究,建立了等速巡航段运动模型、预警卫星探测概率模型、远程相控阵雷达探测概率模型与综合探测概率模型.仿真结果表明:预警卫星在低光谱透射率条件下探测能力较差,远程相控阵雷达受光谱透射率影响可忽略;两者同时探测巡航段,低光谱透射率下提高了探测概率,增长了预警时间,提高了预警系统对等速巡航段探测能力.     

空情预警作战指挥系统军事规则设计

针对空情预警作战指挥系统开发对空情预警作战指挥系统军事规则体系设计的需求,在定义空情预警作战指挥系统军事规则概念的基础上,分析了其军事规则的来源,初步构建了空情预警作战指挥系统军事规则体系;通过示例给出了相应军事规则的设计过程.这为空情预警作战指挥系统军事规则的抽象表示及在系统中的实现提供了依据.     

Effects of road surface appearance and low friction warning systems on driver behaviour and confidence in the warning system

Warning systems for slippery road conditions are a potential newcomer among driver support systems. A total of 75 participants drove in a high-fidelity driving simulator on roads with both visible and invisible ice, to investigate to which extent drivers rely on a low friction warning system. Three experimental groups with different versions of a low friction warning system and a control group without warning system were compared. All drivers ranked the systems according to trust. A system displaying recommended speed received the best ratings. Driving speed was analysed for three particular segments of the route. Generally, lowest speeds were achieved with the recommended speed system. The participants drove more slowly on a slippery segment that looked icy than on the segments that looked dry when they did not receive a low friction warning. When they received a warning for low friction they also lowered their speed for the segment looking like asphalt. The results provide guidelines for how to present low friction warnings to drivers. The design has substantial effects on the resulting behaviour and therefore it can have a high impact on traffic safety. So far, not much research on low friction warning systems has been reported.     

一种高压开关柜温度预测的改进方法

对一种Elman神经网络进行高压开关柜温度预测的方法进行改进,融合小波包分析的方法对温度样本进行硬阈值平滑优化,与Elman神经网络松耦合组成松散型小波神经网络进行超短期温度预测。仿真结果表明,改进方法相比单一的Elman神经网络有效预测比率提高,均方根误差下降,其结果与实测温度值更加吻合,提高了预测的准确率和精确程度。     

综合物探的奥陶系灰岩突水预警技术

为克服单一物探手段的局限,更准确地预测奥陶系灰岩突水风险,新安煤矿13151工作面采用瞬变电磁、直流电法、槽波地震和无线电波坑透等4种手段进行了综合物探.经比对物探结果,并结合地质与水文地质资料分析,4种物探异常区的重叠区域奥陶系灰岩水向上导升,低阻区条带分布,存在突水危险;煤厚变薄,可能受剪切滑动构造影响;槽波波速1 800 m/s以上,地应力(矿压)集中,利于围岩破坏.多种物探异常区的重叠区域的突水风险最大,是工作面防治水工作的重点.实例证明,综合物探可以弥补单一物探方法之不足,有效指导钻探验证、底板注浆及突水治理工作.     

基于神经网络和侧翻时间算法的轻型汽车侧翻预警

为实现车辆侧翻精确预警,建立了三自由度侧翻模型和基于侧翻时间(TTR)算法的侧翻预警算法。在此基础上引入仿生智能神经网络,提出基于多状态参量修正的NN-TTR侧翻预警算法。选取典型车辆状态参数组合对传统TTR进行修正分析,并利用Matlab/Simu-link与Carsim联合仿真平台对侧翻预警算法进行验证。结果表明,基于NN-TTR的轻型汽车侧翻预警算法能够有效提高侧翻预警精度,为主动防侧翻控制奠定良好基础。     

基于AVR单片机的安全车距智能预警系统研究

本文研发了一种安全车距智能预警系统.系统以AVR单片机为核心,利用超声波传感器发送和接收超声波,然后通过发送和接收的超声波信号的时间差,计算出前、后车辆之间的距离,并在车内的LCD显示器上显示出本车的车速以及与前、后车辆的距离,当与前、后车辆的车距小于安全车距时,发出报警信号,提醒本车驾车者采取相应措施,同时可给后车发出安全预警信号.     

微小故障诊断方法综述

针对微小故障的特点,提出将微小故障分为缓变微小故障、突变微小故障和间歇性微小故障,并进一步提出微小故障诊断方法的分类框架,将微小故障诊断方法分成了3类,即定性诊断方法、定量诊断方法和半定性半定量诊断方法.对每一类中现有的微小故障诊断方法再次归类,并对每种方法的基本思想、研究进展、适用条件和应用等进行了介绍.最后探讨了微小故障诊断有待解决的问题.     

Aggregating multiple classification results using Choquet integral for financial distress early warning

Financial distress prediction methods based on combination classifier become a rising trend in this field. This paper applies Choquet integral to ensemble single classifiers and proposes a Choquet integral-based combination classifier for financial distress early warning. Also, as the conditions between training and pattern recognition cannot be completely consistent, so this paper proposes an adaptive fuzzy measure by using the dynamic information in the single classifier pattern recognition results which is more reasonable than the static prior fuzzy density. Finally, a comparative analysis based on Chinese listed companies’ real data is conducted to verify prediction accuracy and stability of the combination classifier. The experiment results indicate that financial distress prediction using Choquet integral-based combination classifier has higher average accuracy and stability than single classifiers.