Equilibrium Customer Strategies in Markovian Queues with Setup Times and Partial Failures

This paper considers the equilibrium behavior of customers in a Markovian queue with setup times and partial failures, where the reactivated server must go through a period of setup time to reach the normal working state and a failure can occur at any time during the normal service. When a partial failure occurs, the server continues to serve the customers on spot at a low rate and does not admit a new arrival. Once the system becomes empty, an exponential repair time starts. Assuming that all the customers have the option of joining or balking based on a linear reward-cost structure, the authors analyze the equilibrium strategies of the customers and the average social benefits of the system in the fully observable case and the partially observable case, respectively. And on this basis, the effect of several parameters on customers' strategic behavior is presented by some numerical examples.     

Fault Detection for Uncertain Delta Operator Systems with Two-Channel Packet Dropouts via a Switched Systems Approach

This paper utilizes a switched systems approach to deal with the problem of fault detection(FD) for uncertain delta operator networked control systems(NCSs) with packet dropouts and timevarying delays. Uncertainties exist in the matrices of the systems and are norm-bounded time-varying.Two parts of packet dropouts are considered in this paper: From sensors to controllers, and from controllers to actuators. Two independent Bernoulli distributed white sequences are introduced to account for packet dropouts. Then an FD filter is designed under an arbitrary switching law. Furthermore, the sufficient conditions for the NCSs under consideration that are exponentially stable in the mean-square sense and satisfy H∞ performance are obtained in terms of linear matrix inequalities(LMIs), multiple Lyapunov functions(MLF) and average dwell-time(ADT) approach. The explicit expression of the desired filter parameters is given. Finally, a numerical example verifies the effectiveness of the proposed method.     

Estimation and Inference in Semi-Functional Partially Linear Measurement Error Models

Journal of Systems Science and Complexity - This article studies the estimation and statistical inference problems of semi-functional partially linear regression models when the covariates in the...     

A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method

Linear regression models for interval-valued data have been widely studied. Most literatures are to split an interval into two real numbers, i.e., the left- and right-endpoints or the center and radius of this interval, and fit two separate real-valued or two dimension linear regression models. This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix, based on a generalized linear model for interval-valued data. A three step estimation method is proposed. Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.

     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

非线性光机械系统中的弱力探测

从理论上研究了包含光学参量放大器的非线性腔光机械系统的弱力探测性能.一方面,发现通过调节光学参量放大器的非线性增益以及输入光场与腔场之间的有效失谐,可以减小压缩正交中约14 dB的量子噪声,从而将弱力探测灵敏度提高2倍以上.另一方面,分析了非线性腔结合零差测量产生的反作用噪声相消现象并且实现量子非破坏测量的方法.     

基于数字锁相的激光光谱早期火灾预警系统

为了实现火灾的快速准确早期预警,研制了一种基于分布式反馈激光器(DFB)的早期火灾预警系统.该系统采用片上系统(SoC)芯片STM32H743为主控制器,结合波长调制(WMS)和数字锁相放大技术(DLIA)实现了CO高灵敏探测.通过单片机内部集成的12位数模转换器和16位模数转换器实现光信号的调制和光电信号采集,并根据内部2倍参考信号进行数字相关运算、积分运算及一阶无限长冲击响应滤波算法实现DLIA算法,从而得到光谱信号的2次解调信号.采用静态配气法进行了系统的标定试验,其线性相关系数达到0.998 8.利用该系统进行榉木,PVC,棉布,聚氨酯泡沫及A4纸的阴燃实验结果表明,阴燃产生CO体积分数达到5×10~(-6)所需时间分别为340、250、250、150、110 s.结果表明此系统具有快速、准确的早期火灾预警功能.     

高速公路拼接技术及施工要点

道路拼接技术可以充分利用原路面结构,具有较好的经济性,在高速公路改扩建工程中得到广泛应用。本文从高速公路拼接方式和材料应用角度出发,重点对拼接技术中最为重要的层间粘结施工环节进行分析,并对基层、面层拼接的施工程序和技术要点进行了阐述。