基于模糊滤波算法的时滞系统温度控制及误差补偿

利用自适应滤波算法调整模糊控制的输出,可解决大时滞温控系统的稳态振荡问题,但无法消除系统稳态误差.提出新的误差补偿算法,可以进一步消除稳态误差,减小参数整定工作量.将其应用于全自动化学发光免疫分析仪的试剂仓恒温控制.仿真结果表明,该算法能使大时滞温控系统得到稳定控制并增强系统的自适应能力和鲁棒性.     

基于LS-SVM的温度传感器非线性关系拟合及参考端温度补偿

文章编号: 提出一种基于最小二乘支持向量机(LS-SVM)的温度传感器非线性关系拟合模型,并根据温度传感器的输入 输出特性给出两种方案对参考端温度进行补偿. 建立了LS-SVM回归模型,利用LS-SVM超强的学习能力对温度与电势 间的非线性关系进行精确拟合. 两种方案均可对参考端温度进行有效补偿,其中方案2 可根据参考端温度、传感器实测 电势对实际温度直接拟合,简化了补偿过程,提高了识别精度. 实验表明,LS-SVM回归法及文中所提出的补偿方案能很 好地逼近实际温度,提高测量精度.     

加权约束最小二乘法计算等效静力风荷载

基于平均风压与脉动风压根方差相组合的分布模式,利用加权约束最小二乘优化方法计算等效静力风荷载,在计算时通过约束条件来限制等效荷载的大小,并通过引入权值因子确保等效荷载作用下典型响应的正确性.算例分析结果表明,该方法可用于计算大跨结构的多个目标峰值响应,克服了传统方法仅能等效单个响应的缺陷,对绝对值较大的目标响应等效精度较高,并且典型响应的等效精度要大于一般响应;同时可以有效控制等效荷载的大小,计算得到的等效荷载变化比较均匀,没有出现风压大小及方向剧烈变化的情况.     

温度和气氛对锰稳定氧化锆立方相稳定性的影响

研究了采用传统的固相反应法制备具有单一立方相结构的锰稳定氧化锆陶瓷,并对影响锰稳定立方氧化锆稳定性的温度和气氛进行了分析.结果表明：当锰的掺杂量（原子分数）为20%时,氧化锆为单一立方相结构.对于具有立方相结构的氧化锆固溶体的相稳定研究发现,在1 000℃以上的高氧分压条件下,立方相易发生向单斜相相变;在1 000℃低氧分压的条件下,立方相结构相对稳定;在温度低于800℃条件下,无论氧分压的高低,立方相均能稳定存在,只是在高分压氧下伴随立方相晶格常数的减小及Mn元素价态的变化.     

电磁出钢装置中座砖内温度分布的分析

针对钢包电磁引流技术中,钢包不是固定装置,现场流动性比较强,无法对感应线圈进行连续冷却这一技术难点.提出了将座砖做成内外组合式,把感应线圈镶嵌到内部座砖里,然后在线圈的顶端和内侧添加高温隔热材料,来减少热量向线圈传递,防止没有水冷时线圈因温度过高而失去工作能力.在实验室条件下,对座砖内温度分布进行了测试,并以此为不同材质的隔热板隔热性能的高温实验做出指导,结果表明：通过添加40mm厚蛭石隔热板后,当高温面温度达到1 000℃,而低温面温度为294℃,低于铜的再结晶温度,线圈处于能够工作温度范围内.     

中厚板控制冷却系统中的实测温度处理方法

以中厚板轧后冷却过程控制系统的实测温度处理方法为研究对象,建立具有一定容错性的实测温度滤波处理方法,消除跟踪误差、测量误差等对模型计算精度的影响.在此基础上,利用最小二乘法对实测温度进行曲线拟合,控制模型以拟合结果作为衡量钢板纵向冷却均匀性判据,并进行相应的冷却规程设定和自学习计算.建立钢板纵向分样本控制的温度处理方法,利用线性插值计算获得钢板纵向各个样本的温度值,从而满足系统进行终冷温度和冷却速度高精度控制的需要.     

饱和黏土的一维热固结特性试验研究

通过室内试验,研究了饱和黏土在不同温度作用下的固结效应.结果表明:温度对土颗粒的膨胀作用和孔隙比的收缩作用影响较小;高温作用下发生的热膨胀在一定程度上阻碍了变形的发生,高温条件下压缩指数较低温条件下有减小的趋势.但渗透性的增强使得该现象逐渐减弱.随着温度的升高,先期固结应力均随之减小.通过反分析得到了两种黏土的材料参数γ.材料参数γ越大,相同温度下的归一化先期固结应力越小.温度越高,水溶液的黏度越低,越容易被排出,其渗透系数越大,超静孔隙水压的消散越快,所以超静孔隙水压随着温度的升高而减小.渗透系数随着孔隙比的升高而升高.粒径较大试样的渗透系数大于粒径较小试样的渗透系数.     

氯代乙酰基乙酰甲胺合成技术的改进

改变了二氯代乙酰基乙酰甲胺的转化剂,提高了转化温度,从而减少了废水量,使转化时间由5.0￣5.5h减少到2.0￣2.5h,缩短了生产周期,提高了生产效率.     

Microhabitat selection in the sand recluse spider (Sicarius thomisoides): the effect of rock size and temperature

In spiders, temperature is considered an important environmental variable for microhabitat selection. In this study, we evaluated the effect of temperature and rock size on the presence of the sand recluse spider Sicarius thomisoides and the degree of selectivity in different locations. This species is a large spider that lives under rocks in desert and semi-desert climates and is particularly active during the summer. In Chile, these spiders can be found at both coastal and inland locations under different thermal conditions, where usually the temperatures are lower near the coast. If large-scale climatic conditions are important for this species, they may be expected to select lower rock temperatures on the coast than at inland locations. In addition, we would expect that the spiders would choose larger rocks in inland compared to coast locations, which reduce the effect of high temperatures. We found that the probability of finding individuals of this species increased according to rock temperature and rock size in the field. Our results suggest that S. thomisoides prefers larger and warmer rocks to shelter under during the day, this selectivity being similar at both coastal and inland locations. Thus, this species tends to select rocks with the same thermal and structural conditions, independent of the climatic conditions.     

Mortality effects of temperature changes in the United Kingdom

Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter (Journal of the American Statistical Association, 1992, 87: 659–671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature‐related factor. The new model is shown to provide a significantly better‐fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed.