改性磷石膏球作缓凝剂对水泥性能的影响

研究了工业化生产的改性磷石膏球对水泥标准稠度用水量、凝结时间、胶砂流动度、胶砂强度及水泥与减水剂相容性的影响,并与原状磷石膏和天然石膏进行对比,结合X-射线衍射、综合热分析等微观测试,分析了改性磷石膏球对水泥水化产物相、水化程度的影响.结果表明:采用改性磷石膏球配制的水泥,其初凝、终凝时间与掺配原状磷石膏水泥相比分别缩短217 min、227 min,1d、3d强度显著高于原状磷石膏配制的水泥,28d强度高于天然石膏配制的水泥,且标准稠度用水量、胶砂流动度、与减水剂的相容性等指标优于天然石膏配制的水泥.改性磷石膏球对水泥早期水化无不良延缓作用,且能提高水泥后期水化程度.综合对比上述三种石膏对水泥性能影响的各项指标,认为改性磷石膏球可以完全替代天然石膏作水泥缓凝剂.     

A RANDOM PARAMETER PROCESS FOR MODELING AND FORECASTING TIME SERIES

Abstract. A generalized autoregressive (GAR) process {Z ( t ) ; t = 0 , ±1, …} is defined to satisfy the recurrence relation Z(t) = A_θ (t)Z (t -l)+ u( t ), where {A_θ(t); t = 0,±1, …} is itself a stochastic process depending on a vector parameter θ and where {u( t ); t = 0, ±1, …} is white noise with Eu ² ( t ) = a ². This paper develops theory and methodology and implementing the class of GAR processes for time series modeling and forecasting. Conditions on the 'parameter process' { A _θ ( t ); t = 0, ±1, …} are obtained for the existence of a GAR process; necessary and sufficient conditions on { A_θ ( t ) ; t = 0, ±1, …} for existence of a stationary GAR process are also obtained. Procedures are developed for computing maximum likelihood estimates of the parameters 0 and u² and for computing the minimum mean squared error forecasts for GAR processes.     

一种不确定图中最可靠最大流问题的解决方案

最可靠最大流是不确定图中可靠性最高的最大流,它是传统最大流问题在不确定图上的自然延伸.现有的最可靠最大流算法SDBA时间复杂性较高,无法满足实际中不同应用的需求,为此,文中提出一种具有普遍适用性的最可靠最大流解决方案.该方案包含面向不同需求的3种算法:基于负权群落消去的NWCE算法、基于时间约束优先单环消去的SPEA-t算法和基于概率阈值约束优先单环消去的SPEA-p算法.其中,NWCE算法借鉴最小费用最大流的"流平移"思想并基于文中提出的负权群落概念,在辅助剩余图中不断地消去可使可靠性增加而流量不变的负权群落,可证当消去所有负权群落时对应的最大流即为最可靠最大流.根据负权群落中由单环组成的群落占很高比例且相对于多环组成的群落更易查找和消去的性质,同时考虑到NWCE算法为了获得最优解,往往为了消去最后少数几个对概率提高贡献很小的负权群落却花费了很长时间的现象,提出SPEA-t和SPEA-p两种快速近似算法,前者是以规定时间内尽可能逼近最优解为目标,后者是以最少时间达到预设的概率阈值为目标,它们都采用了优先消去概率-时间效益较好的单环群落的策略,加快对最优解的逼近速度,减少或放弃时间开销较大的多环群落的消去,以满足那些对算法时间性能要求很高而结果以近似最优即可的应用需求.实验表明,相对于SDBA算法,NWCE算法结合概率剪枝策略在时间性能上有了数量级的提高,而SPEA-t算法和SPEA-p算法则具有更高的性能和更好的适用性.     

HEC influence on cement hydration measured by conductometry

Cellulose ethers are of universal use in factory-made mortars, though their influences on mortar properties at a molecular scale are poorly understood. Recent studies dealt with the influence of hydroxyethylmethyl cellulose (HEMC) and hydroxypropylmethyl cellulose (HPMC) molecular parameters on cement hydration. It was concluded that the degree of substitution is the most relevant factor on cement hydration kinetics, contrary to the molecular weight. Nevertheless, the major role played by the substitution degree has not been verified for other types of cellulose ethers such as hydroxyethyl cellulose (HEC), which generally possesses a higher hydration retarding capacity compared to HPMC and HEMC. In this frame, a study of the impact of HEC molecular parameters on cement hydration was performed. A negligible influence of the molecular weight was observed. Moreover, the results emphasize that the hydroxyethyl group content mainly determines the delay of cement hydration.     

Selecting the forgetting factor in subset autoregressive modelling

Abstract. Conventional methods to determine the forgetting factors in autoregressive (AR) models are mostly based on arbitrary or personal choices. In this paper, we present two procedures which can be used to select the forgetting factor in subset AR modelling. The first procedure uses the bootstrap to determine the value of a fixed forgetting factor. The second procedure starts from this base and applies the time-recursive maximum likelihood estimation to a variable forgetting factor. In one illustration using real exchange rates, we demonstrate the effect of the forgetting factor in subset AR modelling on ex ante forecasting of non-stationary time series. In a second illustration, these two procedures are applied to time-update forecasts for a stock market index. Subset AR models not including a forgetting factor act as a set of benchmarks for assessing ex ante forecasting performance, and consistently improved forecasting performance is demonstrated for these proposed procedures.     

Particle filtering for sensor fault diagnosis and identification in nonlinear plants

We propose a novel method for sensor monitoring and fault-tolerant estimation in systems described by general stochastic nonlinear and/or non-Gaussian state-space models. Faults are defined as abruptly occurring calibration errors, causing the sensor readings to be biased or scaled. Actuators and the process itself are assumed to be fault free. The main novelty of the work is an adaptive particle filter, whose configuration changes in order to diagnose sensor faults and to compensate for their effects. The presence, type and magnitude of sensor faults are determined through hypothesis testing and maximum likelihood estimation, based on the difference between the measurements and the particle filter estimates. The validity of the proposed approach was demonstrated through simulations on a drum-boiler model, although its effectiveness is not conditioned on any particular feature of the considered example.     

无约束非线性lp问题的区间极大熵方法

针对信号处理、系统识别等领域中涉及到的无约束非线性lp问题,为减小由于二进制编码的舍入误差对该问题计算结果的影响,对求解该问题的极大熵方法进行了区间扩张.证明了区间扩张后的极大熵函数至少具有二阶收敛性,并设计了具有多项式时间复杂度的区间算法进行求解,举例进行了数值计算.数值计算结果显示,该区间算法可靠,计算结果与区间扩张前相比,结果更加精确.     

云数据库中基于极大熵差分进化的负载评估算法

由于分布式关系型数据库基于两阶段提交协议的设计方式,使得系统如出现单节点瓶颈问题,数据库事务将全部回滚,从而造成巨大的系统开销,影响数据库在大数据环境下的应用。针对这一现状,提出一种基于极大熵差分进化的负载评估算法,利用评价函数法,将多目标优化问题转化为不可微的单目标优化问题,再利用极大熵函数,将不可微优化问题转化为一个带有参数的无约束优化问题,最后用差分进化算法对其进行求解,找出节点资源最优集,从而为过载节点的数据迁移提供了理论依据,也进一步实现了对云数据库的设计。实验结果表明,该算法能够提高系统的整体性能,有效避免单节点瓶颈问题。     

机械激活矿渣水泥

用于式高能振动磨机械激活矿渣（炉渣和／或硅灰）水泥。研究表明：经机械激活后．矿渣水泥的颗粒粒度降低，颗粒的比表面积增大。在掺合了矿渣的水泥中硅微粒聚团经机械激活后可有效地分散并作用于水泥中．使矿渣水泥的水化反应括性增强，促进水泥水化反应，降低水泥制品的孔隙度．从而可以提高水泥制品的力学性能。通过Fourier变换红外光谱和核磁共振等方法分析了机械激活作用的机理。     

一种检测单晶炉热场温度的新方法

为去除单晶炉热场温度测量信号中的低频干扰、提高温度信号的估计精度,本文提出了一种基于跨维模拟退火(trans dimensional simulated annealing,TDSA)的单晶炉热场温度检测方法.该方法首先将AIC(Akaike information criterion)信号个数判断准则纳入采样机制中以便对干扰个数采样估计,进而利用所设计的基于信号频谱和轮盘赌思想的Metropolis Hastings机制对相应个数的干扰频率进行采样,最后设计了混合均匀和高斯采样机制对单晶炉热场温度进行采样估计.仿真和单晶炉工程实验结果表明,该方法在干扰个数未知的情况下,能够有效地抑制低频干扰、准确检测单晶炉热场的温度.