Adaptive Control of the Structural Balance for a Class of Complex Dynamical Networks

In social networks, the structural balance is a state of a group of individuals(nodes) with established mutual relationships(connection relationships) between them. It is easy to see that a social network can be described by a complex dynamical network model composed of the nodes subsystem(NS) and the connection relationships subsystem(CS), where the two subsystems are usually coupled with each other. It implies that the dynamic changes of nodes' states may cause the structural balance in CS. However, few papers have discussed the relationship between the structural balance and the specific dynamic changes of the nodes' states. This paper proposes a model of complex dynamical networks, and mainly focuses on the dynamic changes of states in NS which can lead to the structural balance in CS. It is proved that if each state in NS is doing a specific dynamic motion via the controller with the parameter adaptive law, then the CS can track a given structural balance matrix via the effective coupling and the structural balance can be achieved. Such a result can be regarded as an explanation of the relationship between the structural balance and the specific dynamic changes of the nodes' states. Finally, the simulations verify the effectiveness of the proposed method.     

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.

     

物联网中区块链技术的应用与挑战

随着物联网技术的快速发展和广泛应用，分布式网络环境下的身份验证、数据隐私、网络安全等问题变得更加重要，而这些问题可以通过引入区块链技术来得到有效解决.该文简述了区块链的基本概念，介绍了区块链在物联网中的应用场景，并对当前一些相关工作的成果进行了综述；介绍了针对物联网场景下区块链底层技术的研究以及区块链物联网所面临的问题与挑战，希望能对未来区块链物联网的研究有一定的参考价值.     

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.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

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.