光谱测温法应用于转炉炼钢火焰测温

炉口火焰是在转炉炼钢过程中最重要的对炉内钢水温度和成分含量判定的一个重要依据。将炉口火焰光谱分为两部分频谱:背景光谱和原子发射光谱特征。假设背景光谱光强度补偿了特征原子的受激而产生的自吸或者自蚀光强度损失,基于火焰发射光谱(FES)原理和转炉炼钢过程中的火焰光谱,推导出特征原子光谱和强度与火焰温度之间的关系。结果表明,火焰发射光谱测温法所测得的温度与实际温度的误差能够在理想的范围内。     

基于火焰图像与光谱特征的炼钢终点温度预测

转炉炼钢终点温度的精确控制能够提高最终出钢的质量。为了提高终点钢水温度的预测精度，使用一种修正的比色测温法计算炉口火焰温度，通过改进的竞争性自适应重加权算法提取火焰光谱特征波长，最后将图像和光谱特征融合分析，建立炼钢终点温度预测模型。模型预测结果的均方根误差为15.8556 K，预测误差在±20 K内的准确率为87.50%，±30 K内的准确率为95.00%。与单独使用图像特征或光谱特征建立的模型相比，所提模型的预测误差最小，准确率最高。所提模型能够有效地预测转炉炼钢终点温度，满足炼钢生产的现场要求。     

瞬态光谱测量技术与多光谱辐射测温技术的巧妙结合——用于测定火工药剂的燃烧温度

为了准确测量火工药剂的燃烧温度,分析了火工药剂的燃烧辐射特性,利用瞬态光谱测量技术测定了几种典型火工药剂燃烧火焰的辐射光谱曲线.在分析典型火工药剂的燃烧辐射光谱曲线的基础上,根据多光谱辐射测温技术的工作原理设计和研制了一种具有12个测试通道的多光谱辐射测温系统.采用黑体炉对该系统的稳定性和准确性进行了验证.最后以某火工...     

火焰光谱探测器的光谱匹配因数

从测量得到的火焰光谱数据出发,对火焰探测器的光谱匹配因数进行研究,导出了光谱匹配因数的表达式,并在1～14μm波段范围内,计算了InSb红外探测器对不同温度黑体辐射的光谱匹配因数,为新型火焰探测器的研制提供一些必要的理论依据.     

基于小波神经网络的红外光谱发射率计算方法

红外光谱发射率是战机蒙皮重要的红外隐身参数之一,为消除大气等外界因素对红外辐射特性测试结果的干扰,建立一种基于小波神经网络的目标红外辐射亮度模型,利用这种模型对测试样本进行网络训练,建立3~5 μm 和8~12 μm 波段红外辐射亮度模型,进而计算不同波长下目标的光谱发射率。通过与标准黑体对比,验证所建小波神经网络模型的光谱发射率与黑体实际的光谱发射率相比,最大相对误差约为2%,并将该方法应用于飞机蒙皮的光谱发射率计算。     

基于最佳小波变换和神经网络的气体非线性荧光光谱的识别

将小波变换和神经网络相结合用于非线性荧光光谱的识别,针对非线性荧光光谱的特点,提出了选择最佳小波函数和分解层数的方法,处理后的光谱在保留光谱特征的基础上,大大压缩了数据维数;采用概率神经网络(PNN),对3种污染气体的非线性荧光光谱进行识别,获得了满意的实验结果。由于神经网络的输入是小波压缩后的数据,不仅提取了原始数据中的特征,而且数据的维数也下降7倍多,大大提高了气体识别的速度。     

小波神经网络在短时交通流量预测中的应用

随着交通基础设施建设和智能运输系统的发展,交通规划和交通诱导成为交通领域的研究热点,对交通规划和交通诱导而言,准确的交通流量预测是其实现的前提和关键。短时交通流量预测是一个时间序列预测问题,文中应用小波神经网络对短时交通流量进行了预测。首先,对神经网络、小波分析等相关理论进行了简要介绍。在此基础上,采用5-7-1小波神经网络结构,以Morlet小波基函数作为隐含层节点的传递函数,将车流量数据输入该模型中,以训练小波神经网络,并用训练好的神经网络来预测短时交通流量。从预测结果来看,小波神经网络的预测结果较为准确,网络预测值接近期望值,效果较好。     

遥感、遥测、遥控

TP72 99061271星载微波辐射成像仪天线温度误差分析/熊强,施邦耀,杨伟中(航天工业总公司813所)11上海航天.一1 999,16(2),一16一19阐述了星载微波辐射成像仪扫描式外部两点周期定标原理,介绍了利用星载微波辐射成像仪原始测量数据计算其天线温度的方法一一定标方程.详细分析了星载微波辐射成像仪的天线温度精度.并估算了星载微波辐射成像仪的18 GHz接收通道天线温度精度.确定了影响天线温度精度的主要误差源,为提高夭线温度精度提供了依据.表1参2(许)将小波和神经网络结合起来,提出一种自适应小波函数的神经网络.这种小波函数网络经过训练…     

一种基于小波变换和FIR神经网络的广域网网络流量预测模型

该文提出了一种基于小波变换和FIR神经网络的广域网网络流量预测模型,首先采用小波分解把网络流量数据分解成小波系数和尺度系数,即高频系数和低频系数,将这些不同频率成分的系数单支重构为高频流量分量和低频流量分量,利用FIR神经网络对这些分量分别进行预测,将合成之后的结果作为原始网络流量的预测。实验结果表明:采用该模型对实际的广域网网络流量数据进行预测,不仅可以得到较快的收敛效果,而且预测性能比现有的小波神经网络和FIR神经网络要好得多。     

短期电力负荷在小波包分解下的径向基神经网络预测方法

针对短期电力负荷预测问题,提出一种在小波包分解下的径向基神经网络预测方法。通过小波包分析,将电力载荷及其温度变量对称地分解为低频的近似系数和高频的细节系数。针对不同的小波系数,设计径向基神经网络作为预测器,并通过试错法确定网络合适的结构。网络的训练过程中,采用滑动窗口数据选择策略减少数据样本集,采用随机梯度法更新权值、中心位置和扩展参数。预测的小波系数用于重构出最终的电力载荷值。与前馈多层神经网络的对比数值,实验结果表明,新提出的方法具有较高的预测准确性。     

Improving the Performance of HF Radio Networks in the Presence of Interference through Automatic Link Establishment with Frequency Hopping Technique

In the cement industry, a rotary kiln is a pyro-processing device that is used to measure temperature. Measuring and maintaining a certain range of temperature in the rotary kiln is important to ensure the production of quality clinker granules. The assessment of consuming zone temperature is acquired using radiation pyrometers from the temperature of a hotspot. However, it is a difficult task to measure the burning zone temperature due to the very high temperature developed in the turning furnace sintering process. Existing pyrometer and camera based techniques are not able to provide accurate temperature and temperature variations developed in the burning zone. This research work considers flame image processing using region of interest (ROI), fuzzy logic, and neural networks for efficient temperature measurement. Various temperature measurement and control techniques are utilized in the existing conventional (Prasanna and Bojja in ESCI (helix—the scientific explorer) 4843–4849, 2019) rotary kiln control techniques. In pyrometer-based measurements, the standard of radiation may lead to errors and inaccurate readings. Hence, the consuming zone temperature estimation got from the radiation pyrometer isn't solid and it is hard to get temperature data for a particular location. A colorimetric device-based intelligent control system measures the burning temperature of a specific point, but reading fluctuations are seen because of smoke and dust developed in the combustion process. In ROI based flame image processing, many factors, such as turbulent flame, brightness of flame zone, and dust, affect identifying the boundary for ROI based flame image analysis. In neural network models, variable selection plays a crucial role in designing effective systems with learning capabilities, but it is not an easy task to accomplish without certain rules. Hence, it is highly necessary to develop an improved control system. In view of the issues in variable and feature selection, a few neuro fuzzy systems are adopted in measurement and control. The consuming zone temperature estimation needs a lot of attention due to the very high temperature developed in the rotary kiln sintering process. Existing techniques have to be improved upon using advanced algorithms and intelligent approaches. A sintering state recognition system has been developed with features of flame images and fusion methodologies. In this approach, various flame image features and texture (Ren and Wang in Int J Autom Compu 11(1):72–77, 2014) features are extracted from the burning zone region. Though these methods address a few issues in flame image processing, the acquired image is largely affected by blurring and internal parameters of the sintering process. Charge coupled device (CCD) camera images and videos are applied to many image processing algorithms for better feature extraction and region extraction. The region of interest-based analysis is mainly focused on temperature assessment in this work. Intelligent control techniques are applied to measure the burning zone temperature in a rotary kiln. Fuzzy logic-based inference systems are combined with neural network algorithms in the development of neuro-fuzzy systems. The fuzzy surmising framework in light of mathematical models is the successful manner to anticipate the temperature esteems utilizing power measures. The fire pictures caught by the CCD cameras are handled utilizing fluffy rule-based picture investigation, which estimates temperature from a fire picture by contemplating RGB power planes. The arrangement of result temperature esteems is wanted to be a participation work. The Mamdani fluffy induction model is used to give planning of fluffy fire temperature. Exact temperature planning of fire pictures is performed to control the temperature inside the going stove to make top notch clinker. The fire picture examination is completed in different edges of three unique datasets, and temperature is estimated for various crude supper feed rates and coal feed rates. However there is a slight distinction in the acquired temperature, the general temperature evaluation process doesn't show a huge contrast as per the dataset.

     

LF 炉钢水非接触测温系统及应用

温度控制在 LF 炉精炼过程中具有重要地位, 它是控制钢水成分质量、连铸质量及稳定工艺的关键。研发了 一种非接触式的钢水温度监测系统, 该系统通过双光路近红外面阵 CCD 探测器实时获取 LF 炉内钢水高清热像, 再利 用计算机对该图像信息进行技术处理,从而准确地识别出钢水, 并根据测温模型计算出实时的温度数据。该测温系统 还可作为工业电视使用, 便于炼钢工人及时了解炉内状况。不同于以往的接触式测温方法, 新系统可以在不影响钢水 冶炼进度的情形下完成对LF炉内钢水温度的实时监测, 操作便捷、安全, 是一项十分值得推广的技术, 对其它测温应 用领域也具有一定的指导作用。     

Combining Artificial Neural Network Models, Geostatistics, and Passive Microwave Data for Snow Water Equivalent Retrieval and Mapping

A new modeling framework combining neural-network-based models, passive microwave data, and geostatistics is proposed for snow water equivalent (SWE) retrieval and mapping. Brightness temperature data from the seven-channel special sensor microwave/imager and the interpolated minimum temperature are the inputs of a multilayer feedforward neural network (MFF). Kriging with an external drift algorithm is applied to ground-based SWE data to produce gridded SWE data that are used as the target of the neural network. An optimal division of the sample of available pixels is achieved by a self-organizing feature map. Prediction error is used for model selection and is assessed by bootstrap. It is shown that a committee of a network containing neural networks with different architectures can provide consistent SWE retrievals. This modeling framework is applied for SWE retrieval and mapping over La Grande River basin in north eastern Quebec (Canada). The results are very promising for operational purposes particularly for SWE mapping during periods with no ground measurements and operational streamflow forecasting.     

Temperature uniformity in RTP furnaces

The heat transfer to a wafer in a rapid thermal processing (RTP) furnace is simulated by an analytical/numerical model. The model includes radiation heat transfer to the wafer from the lamps, heat conduction within the wafer, and emission of radiation from the wafer. Geometric optics are used to predict the radiant heat flux distribution over the wafer. The predicted wafer surface temperature distribution is compared to measurements made in an RTP furnace for two different reflector geometries. Lamp configurations and the resulting irradiance required to produce a uniform wafer temperature are defined     

飞机发动机尾流流场数值模拟与红外特性计算

建立飞机尾喷管的三维几何模型,利用Fluent 6.3.26软件对喷管外的流场区域进行数值模拟,基于发动机完全燃烧假设得到尾焰流场温度、压强等流场数据。利用K-分布法计算尾焰气体辐射参数,采用反向蒙特卡洛法(RMC)对气体红外辐射特性进行了计算,最终获得尾焰的红外辐射图像。     

基于MIV和BRBP神经网络的电路板红外诊断方法

针对BP神经网络对于海量数据训练及多维数据训练收敛困难的问题,在使用增加动力项、自适应学习速率等方法的基础上,引入均值影响度算法(MIV)构造了贝叶斯正则化反向传播(BRBP)神经网络,以此提高电子线路板红外故障诊断算法的效率。利用红外测温方式,获取了不同室温及运行状态下电路板中21个元器件温度数据。将此21个参数作为故障诊断模型的初始输入变量,经过MIV算法简约为12个参数输入至BRBP神经网络,进行故障评估和诊断。结果表明:相对于传统的BRBP神经网络,本文设计的基于MIV和BRBP神经网络模型诊断方法极大简化了数据训练的数据量并解决了数据收敛的困难,因此效率更高,用时更省。     

基于神经网络的红外辐射大气透过率建模及计算

基于定点测量的标准黑体温度实验数据,建立大气透过率的神经网络估计模型。在不同距离测量黑体温度后,引入BP网络自适应学习测试数据的潜在规律,建立大气透过率与当前测量距离及测试温度之间的函数关系,可以精确计算目标的实际温度。仿真结果表明,用本文方法所建的BP网络可以有效地学习样本信息,建立的非线性大气透过率模型解决了大气透过率因影响因素复杂计算难度大等问题。     

一种加热炉炉管表面红外检测温度的修正方法

加热炉炉管表面温度检测的准确性直接关系到加热炉的运行状态和生产安全,利用红外热像仪测量炉管温度时易受到火焰和烟气、邻近炉管、炉墙辐射等因素的强烈干扰。为了实时准确获取炉管表面的真实温度,以某炼油厂加热炉为例,首先给出了炉管表面热红外检测温度的干扰分析,进而提出一种基于现场参数的实时温度校正模型,并结合该模型给出一套判别某厂加热炉炉管状态的专家规则,最后将这套软件应用于实际工程检验。研究结果表明,该方法有效实现了炉管表面的温度修正。     

基于红外热像的钢水测温模型研究

基于热辐射测温原理,介绍了红外热像仪测温理论,为了提高钢水的测温精度,搭建了实验平台,经过实验获得不同温度下钢水的红外图像。利用Matlab软件提取图像的灰度均值,用最小二乘法和BP神经网络进行温度-灰度拟合曲线,从而得到红外热像测温的模型,使钢水测温误差达到了1%,最终达到测量精度和设计要求,此方法为熔融金属在线红外热像测温的研究打下了坚实的基础。     

基于Fluent的飞行器流场建模与红外辐射特性分析

以某型战斗机为研究对象,通过三维建模与网格划分过程建立飞机的流场计算模型,基于商用CFD软件ANSYS Fluent 16.0对飞机的外流场特性进行数值模拟。计算中利用太阳射线追踪算法综合考虑了太阳辐射对机身温度场的影响,采用离散坐标法（DO辐射模型）对辐射传输方程进行耦合迭代计算,利用不带化学反应的组分输运模型（Species Transport Model）模拟燃烧后高温尾焰喷射过程,获得了飞机外流场的温度、浓度及尾流组分分布数据。简要分析了太阳辐射对温度场变化的影响,飞行马赫数对流场红外辐射的影响以及尾焰流场分布情况。分析表明:太阳辐射对蒙皮加热较小,最高升温效果仅为5 K左右,随马赫数的增加飞行器机身背部与腹部红外辐射强度差异明显,最高时腹部辐射强度为机身最大辐射强度2倍左右,激波作用下尾焰后方会出现最高450 K和580 K两个间断的核心高温区域,尾焰红外辐射强度分布符合梨形特征分布趋势。