模糊广义最小偏差法的强保序性条件

依据模糊一致矩阵中元素与权重的关系,得到了模糊互补判断矩阵的一类广义最小偏差法,并给出在增加一个或一组新元素时的强保序性条件.     

非负不可约模式的最小秩

设A是n阶非负不可约模式.A的最小秩定义为与A有相同符号模式的全体实矩阵的秩的最小值.考虑矩阵序列A,A2,…,A3,….设Ab是第一个重复的矩阵,且记p＞0是最小的正整数使得Ab=Ab+p.称b为A的基.p为A的周期.本文研究了非负不可约模式的基与最小秩的关系.     

关于球罐球片包装架的结构分析

球片包装架是球罐产品包装及运输过程中必需的结构件，然而在实际使用过程中，包装架系列过于安全。从而产生材料消耗较大、铁路运输成本升高等因素。用力学方法，通过对包装架进行合理的受力分析和计算，并以此为理论根据，本着安全、合理及高效的原则，可以降低包装架主体构件——承重梁(槽钢)型号，或增加包装架承重量的方式，对球片包装架构件进行重新设计，从而可以节省材料,增加效益。     

两种广义逆矩阵A~-的理论证明

给出了秩为ｒ的分块矩阵的两个性质，从而给出了两种广义逆矩阵Ａ－的理论证明     

煤种变化对四角切圆燃烧煤粉锅炉燃烧的影响

针对某电厂四角切圆燃烧煤粉锅炉在变煤种工况下出现的问题,讨论了煤种变化对四角切圆燃烧煤粉锅炉燃烧的影响,通过计算分析,得出了煤种变化与减温水量增大和排烟温度升高的定量关系以及燃烧器布置高度与炉膛出口烟气温度的关系,提出了较为合理的解决方案。     

浙江大学获国家科技奖励情况的统计分析

根据近5年来数据,分析了浙江大学获国家科技奖励情况,并与国内部分主要高校从获奖总数、奖种分布等方面进行了比较,同时对浙江大学获国家科技奖典型案例进行分析,提出争取多获国家科技奖励的几点建议.     

基于神经网络和对称秩的特定谐波消除开关角生成算法

利用查表法实现三相级联11电平逆变器特定谐波消除面临存储开关角多、占用内存大的问题;而利用小规模神经网络拟合则面临生成开关角精度低的问题。针对这些问题,文中提出基于小规模神经网络和对称秩迭代相结合的特定谐波消除开关角生成算法。首先,利用小规模神经网络预测开关角的迭代初值,降低对神经网络训练精度的要求;其次,利用对称秩算法迭代开关角初值得到精确解。与查表法和Hopfield神经网络算法相比,所提算法可以减少数据存储空间,同时生成精度较高的开关角。仿真和实验结果表明:所生成的开关角能有效地消除5次、7次、11次、13次谐波,同时保证了期望的基波幅值。     

Some Systematic Supersaturated Designs

Supersaturated designs are factorial designs in which the number of factors exceeds the number of observations. Satterthwaite has suggested constructing such designs by a randomization procedure (random balance designs). In the present paper systematic designs are constructed which are, in a certain sense, m nearly orthogonal M possible. Examples of such designs are given constructed on an electronic computer and some properties of the designs are discussed.     

Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

In this paper, we provide a sequential rank-based adaptive nonparametric cumulative sum control chart for detecting a range of shifts in the location parameter. This chart is a self-starting scheme, and thus can be used to monitor processes at the start-up stages rather than having to wait for the accumulation of sufficiently large calibration samples. It does not require any prior knowledge of the underlying distribution. The choice of the chart parameters is studied and a simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also efficiently detects various magnitudes of shifts. An illustrative example is also given to introduce the implementation of this proposed control chart.     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.