随机环境下带有私人信息的单机能力分配策略

本文研究随机环境下考虑私人信息的单机能力分配问题.该问题中各部门的单位产品收益、需求信息及加工设备各时段的能力为私人信息,且各时段中产品的加工时间为随机变量.本文采用设备方与各部门协商的方法对问题进行求解,首先利用随机规划理论将能力分配问题清晰化,然后利用拉格朗日松弛和泰勒级数展开方法进行协商机制设计,接着给出基于偏转次梯度方法的协商参数更新法则,最后综合上述过程给出最终的问题求解算法.数值算例验证了算法的有效性并分析了关键参数对能力分配结果的影响.     

REVERSED RESIDUALS IN AUTOREGRESSIVE TIME SERIES ANALYSIS

Abstract. Both linear and non-linear time series can have directional features which can be used to enhance the modelling and investigation of linear or non-linear autoregressive statistical models. For this purpose, reversed p th-order residuals are introduced. Cross-correlations of residuals and squared reversed residuals allow extensions of current model identification ideas. Quadratic types of partial autocorrelation functions are introduced to assess dependence associated with non-linear models which nevertheless have linear autoregressive correlation structures. The use of these residuals and their cross-correlation functions is exemplified empirically on some deseasonalized river flow data for which a first-order autoregressive model is a satisfactory second-order fit. Parallel theoretical computations are undertaken for the non-linear first-order random coefficient autoregressive model and comparisons are made. While the data are shown to be strongly non-linear, their correlational signatures are found to be convincingly different from those of a first-order autoregressive model with random coefficients.     

新一代水管式沉降仪控制模块的研制

新一代低功耗水管式沉降仪控制模块是基于16单片机MSP430F5436为处理器的低功耗装置.该文详细介绍了其硬件结构和工作原理,分别从电源系统、电磁阀控制电路、控制流程、数据采集等部分阐述了其特点,诠释了水管式沉降仪控制模块在人机交互、工作稳定性等多方面所具备的优势.新一代水管式沉降仪控制模块已在实际工程中得到了成功应用,其稳定性和精确性为大坝监控系统提供了准确的实时数据.     

染料释放化合物及一当量成色剂浅谈

简述了染料释放化合物的发展过程 ,并由此引出一当量成色剂的概念 ,叙述了一当量成色剂应用和发展 ,对一当量成色剂应用前景做了预测。     

Downscaling of the flood discharge in a probabilistic framework

Many modeled and observed data are in coarse resolution, which are required to be downscaled. This study develops a probabilistic method to downscale 3-hourly runoff to hourly resolution. Hourly data recorded at the Poldokhtar Stream gauge (Karkheh River basin, Iran) during flood events (2009–2019) are divided into two groups including calibration and validation. Statistical tests including Chi-Square and Kolmogorov–Smirnov test indicate that the Burr distribution is proper distribution functions for rising and falling limbs of the floods’ hydrograph in calibration (2009–2013). A conditional ascending/descending random sampling from the constructed distributions on rising/falling limb is applied to produce hourly runoff. The hourly-downscaled runoff is rescaled based on observation to adjust mean three-hourly data. To evaluate the efficiency of the developed method, statistical measures including root mean square error, Nash–Sutcliffe efficiency, Kolmogorov-Smirnov, and correlation are used to assess the performance of the downscaling method not only in calibration but also in validation (2014–2019). Results show that the hourly downscaled runoff is in close agreement with observations in both calibration and validation periods. In addition, cumulative distribution functions of the downscaled runoff closely follow the observed ones in rising and falling limb in two periods. Although the performance of many statistical downscaling methods decreases in extreme values, the developed model performs well at different quantiles (less and more frequent values). This developed method that can properly downscale other hydroclimatological variables at any time and location is useful to provide high-resolution inputs to drive other models. Furthermore, high-resolution data are required for valid and reliable analysis, risk assessment, and management plans.     

多孔介质内部热质传递的等效耦合扩散模型的推导及其应用

基于Whitaker的体积平均方程，在不附加任何新的假设的基础上，对多孔介质内部热质传递的等效耦合扩散模型进行推导，得出了多孔介质内部热质传递的等效耦合扩散模型。并应用该模型对瓷质砖体干燥过程进行了数值模拟，模拟结果与实验结果十分吻合。     

SIMOP: Efficient reactive distillation optimization using stochastic optimizers

A methodology to improve the efficiency of stochastic methods applied to the optimization of chemical processes with a large number of equality constraints is presented. The methodology is based on two steps: (a) the optimization of the simulation step, which involves the optimum choice of design variables and subsystems to be simultaneously solved; (b) the optimization of the nonlinear programming (NLP) problem using stochastic methods. For the first step a flexible tool (SIMOP) is used, whereby different numerical procedures can be easily obtained, taking into account the problem formulation and specific characteristics, the need for specific initialization schemes and the efficient solution of systems of nonlinear equations. This methodology was applied to the optimization of a reactive distillation process for the production of ethylene glycol. Due to the complexity of the mathematical model, several different numerical procedures were generated, and their influence on the computational burden and on the reliability and accuracy of the optimization to reach the global optimum were studied. The results obtained suggest that in addition to the choice of design variables, the structure of subsystems associated to numerical procedures has a considerable impact on the performance of the optimizers.     

Optimal linear filter design for systems with correlation in the measurement matrices and noises: recursive algorithm and applications

This paper addresses the optimal least-squares linear estimation problem for a class of discrete-time stochastic systems with random parameter matrices and correlated additive noises. The system presents the following main features: (1) one-step correlated and cross-correlated random parameter matrices in the observation equation are assumed; (2) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. Using an innovation approach and these correlation assumptions, a recursive algorithm with a simple computational procedure is derived for the optimal linear filter. As a significant application of the proposed results, the optimal recursive filtering problem in multi-sensor systems with missing measurements and random delays can be addressed. Numerical simulation examples are used to demonstrate the feasibility of the proposed filtering algorithm, which is also compared with other filters that have been proposed.     

Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.     

Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.