烧结过程的运行性能是生产效率和能源利用的综合表现.运行性能评价是保持烧结过程的运行性能处于最优等级的前提.考虑到时间序列数据的冗余,提出一种基于粒度聚类的铁矿石烧结过程运行性能评价方法.首先,利用单因素方差分析方法选取影响运行性能等级的检测参数;然后,采用多粒度区间信息粒化实现检测参数时间序列数据的降维,并进行粒度聚类,得到聚类标签;最后,以聚类得到的聚类标签为输入,利用随机森林算法进行运行性能等级评价.利用实际钢铁企业的运行数据进行实验,构建两个对比实验,分别采用基于时间序列数据聚类(Time series data clustering,TSDC)方法和基于时间序列特征聚类(Time series feature clustering,TSFC)方法.实验结果表明,该方法为有效评价烧结过程的运行性能提供了一套可行方案,为操作人员提升烧结过程运行性能提供了有力的指导.
Brain hypothermia treatment （BHT） is an active therapy for severe brain injury. It makes the temperature of the brain track a given temperature input curve so as to reduce the risk of tissue damage. BHT requires a brain-temperature control system because of environmental disturbances and changes in the human body. The thermal models of the human body devised so far are usually of a very high order and are not suitable for controlling brain temperature. This paper presents a method of finding a reducedorder thermal model of the human body for use in BHT. It combines minimal realization and balanced realization. Unlike other methods, this method yields a reduced-order model that is based on system theory and that takes the frequency characteristics of human thermal sensation into account. It features high precision in the frequency band for BHT and is suitable for the control of brain temperature.
