下层以最优值反应上层的两层线性规则

本文证明了下层以最优值反应上层的两层线性规划可转化为一线性Max-min问题,进而得出其与一双线性规划问题等价。基于此结论可以讨论这种特殊两层问题的几何性质,最优性条件及算法设计。     

An inverse optimization model for imprecise data envelopment analysis

Inverse data envelopment analysis (InDEA) is a well-known approach for short-term forecasting of a given decision-making unit (DMU). The conventional InDEA models use the production possibility set (PPS) that is composed of an evaluated DMU with current inputs and outputs. In this paper, we replace the fluctuated DMU with a modified DMU involving renewal inputs and outputs in the PPS since the DMU with current data cannot be allowed to establish the new PPS. Besides, the classical DEA models such as InDEA are assumed to consider perfect knowledge of the input and output values but in numerous situations, this assumption may not be realistic. The observed values of the data in these situations can sometimes be defined as interval numbers instead of crisp numbers. Here, we extend the InDEA model to interval data for evaluating the relative efficiency of DMUs. The proposed models determine the lower and upper bounds of the inputs of a given DMU separately when its interval outputs are changed in the performance analysis process. We aim to remain the current interval efficiency of a considered DMU and the interval efficiencies of the remaining DMUs fixed or even improve compared with the current interval efficiencies.     

认识空间群

利用晶体学国际表简要介绍了空间群的有关知识及等效点系、不对称单位等概念。     

城市房产测量数据处理模型

提出房产单元及以平行体和点作为房产单元图形的概念推证了平行的主辅点函数关系,给出了房产单元图形测量方法及平差时的条件方程、满秩及非满秩时的法方程及解,解决了房产单元图形平差问题.     

基于复合神经网络的多元水质指标预测模型

长江流域在我国水资源配置体系中具有重要地位,对其进行水质预测尤为重要。基于现有研究结果,结合循环神经网络（recurrent neural network,RNN）中的门控循环单元（gate recurrent unit,GRU）模型与全连接神经网络（fully connected neural network,FCNN）,提出了改进的多元水质指标预测（MWQPP）模型,并用其预测长江流域水体的pH、溶解氧（DO）、高锰酸盐指数（CODMn）、氨氮（NH₃-N）。基于长江流域2011—2018年23个水质监测点7 566条原始数据,经对比实验,证明了用MWQPP模型预测得到的均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和决定系数（R²）均优于传统水质预测模型,有效提升了水质预测的精度,具有较好的鲁棒性,为水质预测和流域管理提供了科学支撑。     

非平稳INAR(1)过程估计量的极限分布研究

提出了整数值随机游走过程,推导出该随机游走过程的若干和式的极限分布.证明了无截距、无时间趋势的单位根过程的自回归系数的最小二乘估计量的极限分布不再是Weiner过程的泛函,而是依概率收敛到真值1,最后还用Monte Carlo模拟验证了该结论的合理性.     

双曲空间中全脐超曲面与高斯映照像

设Mⁿ是单位双曲空间形式Hⁿ⁺¹中定向的紧致无边超曲面.假设存在整数r（1≤r≤n-1）使得高阶平均曲率H_i>0,i=1,2,…,r,且H^r是常数.证明了：如果Mⁿ的高斯映照像包含在一个开半球面内,则Mⁿ全脐.     

微型惯性测量组合

本文论述了惯性测量器件和组合的发展简史、机遇，选择微型惯性测量组合牵引我国微米／纳米技术发展的依据，以及采纳的技术路线。     

Equilibrium of a Prestressed Strip Reinforced with Elastic Plates

The contact problem for a prestressed elastic strip reinforced with equally spaced elastic plates is considered. The Fourier integral transform is used to construct an influence function of a unit concentrated force acting on the infinite elastic strip with one edge constrained. The transmission of forces from the thin elastic plates to the prestressed strip is analyzed. On the assumption that the beam bending model and the uniaxial stress model are valid for an elastic plate subjected to both vertical and horizontal forces, the problem is mathematically formulated as a system of integro-differential equations for unknown contact stresses. This system is reduced to an infinite system of algebraic equations solved by the reduction method. The effect of the initial stresses on the distribution of contact forces in the strip under tension and compression is studied     

On the use of neural networks for identification of linear and nonlinear systems

A procedure based on neural networks for the classification of linear and nonlinear systems is presented, using excitation and response data under swept sine excitation. Special attention is paid to the classification and identification of linear and bilinear systems, the latter being considered since they exhibit typical characteristics of cracked systems. The computer simulations show that: (1) using the procedure presented in this paper the trained classification network can reliably classify a linear system and different nonlinear systems; (2) the output of the trained identification neural network for a linear system and a bilinear system can be used as a quantitative indicator of characteristics of bilinear systems having different stiffness ratios (k _(x>0)/k _(x<0)) with respect to the bilinear system used in the training stage; (3) for two-degree-of-freedom systems, the trained network can not only determine the existence of a bilinear stiffness and the magnitude of its stiffness ratio, but also specify which stiffness is bilinear, i.e. indicate its position. These results provide a possibility of using the trained neural networks to detect and locate structural cracks which have the characteristics of bilinear systems.Visiting scholar, from People's Republic of China.