Dynamic evolution of collective emotions in social networks: a case study of Sina weibo

Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in OSNs, which provides wonderful opportunities to gain insight into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we can classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. By analyzing the collective emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence for that human emotion can be spread from one person to another. Furthermore, we find that the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self-evolving process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self-evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network by selecting seed users according to k-core rather than degree.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.     

Dynamic Output Feedback Consensualization of Uncertain Swarm Systems with Time Delays

Consensus analysis and design problems of high‐dimensional discrete‐time swarm systems in directed networks with time delays and uncertainties are dealt with by using output information. Two subspaces are introduced, namely a consensus subspace and a complement consensus subspace. By projecting the state of a swarm system onto the two subspaces, a necessary and sufficient condition for consensus is presented, and based on different influences of time delays and uncertainties, an explicit expression of the consensus function is given which is very important in applications of swarm systems. A method to determine gain matrices of consensus protocols is proposed. Numerical simulations are presented to demonstrate theoretical results.     

Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking

This work presents a nonlinear adaptive dynamic surface air speed and a flight path angle control design procedure for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed design scheme takes into account the magnitude, rate, and bandwidth constraints on the actuator signals. A new approach is used to enhance tracking performance and avoid a large initial control signal. The uncertain nonlinear functions in the flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed controllers shows that all the signals of the closed‐loop system are uniformly ultimately bounded. The robust performance of the design scheme is verified through numerical simulations of the flight vehicle model for various parameter variation test cases. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

动态与静态取证

文章介绍了动态取证与静态取证的定义与区别,结合实际工作中的案件取证过程,从几个现实案例出发,分析动态与静态取证状态之间的差异,并提出了一种融合两种取证状态的通用取证流程。     

瓦斯突变预警上限值确定方法的研究

针对现有煤矿安全监控系统采用超限原理,以瓦斯传感器测量值为判断依据,无法及时发现预定报警值以下范围内瓦斯异常变化的问题,提出了一种不超限瓦斯突变预警方法,介绍了静态预警和动态预警两种不超限瓦斯突变报警方法。结合余吾煤业N2202工作面探头数据,分别对不超限瓦斯突变预警基础数据的静态数据和动态数据进行了分层抽样及分布的验证和拟合,得出静态预警和动态预警的基础数据基本符合正态分布的结论。提出采用假设检验和区间估计等实时计算静态预警和动态预警上限值的方法,并采用N2202工作面探头数据进行了验证。     

回采作业对冲击地压电磁辐射测量的影响分析

为了分析工作面回采过程中各作业工序对电磁辐射测量的影响,在长沟峪煤矿回采工作面进行跟班测试,并对各回采工序的电磁辐射监测信号进行了定量分析。结果表明,绞车、刮板输送机、电铃等电气设备对电磁辐射测量的影响最大,是在线式电磁辐射监测的主要干扰源;支柱加压、人员走动等非涉电作业活动对电磁辐射测量的影响较小。研究结果为现场使用电磁辐射临界值法判别煤岩动力灾害危险时排除干扰源的影响提供了可靠依据。     

基于矿山物联网的设备动态管理系统

针对现有的台帐式矿山设备管理系统存在人为因素影响大、过维修或欠维修状况明显、无法及时了解运行设备状态等问题,提出了一种基于矿山物联网的设备动态管理系统的设计方案。该系统在原有台帐式设备管理系统的基础上增加了设备运行状况的分析处理功能,实现了矿山设备的动态管理;基于矿山物联网的开放式架构,只需在感知层接入相应的传感器即可在应用平台上获得数据;采用LabVIEW软件实现数据采集与处理,采用Silverlight技术实现信息发布,增强了系统的友好性及易用性。     

有源电力滤波器的自抗扰控制研究

针对采用PI控制器控制有源电力滤波器时存在谐波或基波电流指令而无法实现无静差调节,且数字化后会进一步增大稳态误差,使有源电力滤波器的补偿性能降低的问题,提出一种有源电力滤波器的自抗扰控制策略。该策略利用自抗扰控制器的非线性跟踪-微分器和扩张状态观测器分别处理参考输入和输出,并选择适当的状态误差,实现了对滤波的自动、精确补偿。仿真和实验结果表明,有源电力滤波器采用自抗扰控制策略后具有很强的鲁棒性、稳定性和适应性,控制性能有较大改善。     

一种用于动态电压恢复器的电压跌落检测算法

针对传统的电压跌落d-q检测算法和Hilbert检测算法在补偿快速性和抗干扰性方面存在矛盾的问题,提出了一种将d-q检测算法与Hilbert检测算法相结合的电压跌落检测新算法:对电网侧电压信号采样后,经d-q检测单元得到谐波补偿波形;然后滤除3次谐波,滤波后的信号再经Hilbert检测单元得到电压幅值补偿波形;叠加该2种补偿波形可确定最终的补偿波形。仿真结果表明,该检测算法对于含3次谐波的电压跌落补偿效果较好,兼具补偿快速性和抗干扰能力。