Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.     

Stability of a class of switched stochastic nonlinear systems under asynchronous switching

The stability of a class of switched stochastic nonlinear retarded systems with asynchronous switching controller is investigated. By constructing a virtual switching signal and using the average dwell time approach incorporated with Razumikhin-type theorem, the sufficient criteria for pth moment exponential stability and global asymptotic stability in probability are given. It is shown that the stability of the asynchronous stochastic systems can be guaranteed provided that the average dwell time is sufficiently large and the mismatched time between the controller and the systems is sufficiently small. This result is then applied to a class of switched stochastic nonlinear delay systems where the controller is designed with both state and switching delays. A numerical example illustrates the effectiveness of the obtained results.     

一种改进的基于多项式滤波的稳态检测方法

化工过程的数据中常常含有较多的随机误差和粗差干扰,导致传统的稳态检测方法无法得到准确的结果,从而降低系统故障诊断的可靠性。针对实际的工业过程数据,提出一种融合自适应平滑技术的稳态检测方法,该方法首先以基于导数分析的自适应平滑算法进行降噪处理,消除随机误差的影响,然后引入阈值拟合技术进一步抑制粗差干扰,以多项式滤波方法对数据进行稳态检测,根据测量信号的趋势特征确定过程是否处于稳态。仿真实验研究表明:融合自适应平滑技术的稳态检测方法能够克服传统稳态检测方法中随机误差和粗差干扰对检测结果的影响,进而显著提高稳态检测处理的准确性,检测结果明显优于传统的基于多项式滤波的检测方法。     

水下柔性臂动力学建模方法研究

将柔性臂应用到水下环境,探索一种新型的水下作业工具。在空间柔性臂动力学建模方法的基础上,考虑水动力做功以及水动力对柔性臂变形的影响,首先基于Hamilton原理建立中心刚体-水下柔性臂组成的刚柔耦合系统的动力学模型,然后采用假设模态法对动力学模型进行离散化,导出了考虑刚柔耦合作用的水下柔性臂有限维离散化动力学方程。最后给出仿真算例,验证了动力学模型对于研究水下柔性臂末端变形的有效性,能够准确反映水下柔性臂动力学特性,可用于水下柔性臂的主动控制研究。     

一种用于图形设备接口的笔画模型方法

视频监控的在屏交互过程中,需要快速地对交互控件、感兴趣区域、活动标记等图形和图像进行绘制,为避免画面的停滞感,要求图形设备接口能快速进行绘制.为此,提出了一种笔画模型方法.利用 DirectX的原始绘制能力,该方法将绘制任务转换成笔画对象,使视频系统在实时运行过程中,只需将已转换的笔画对象的绘制数据提交给 DirectX 进行渲染和显示即可,降低重复绘制时间,满足系统的快速绘制要求.基于笔画模型方法,开发了一种可快速绘制的图形设备接口.     

On a method of logic design with branched cores

The maximum evaluation for variations of a signal proportional to a threshold element input composition is calculated. A design procedure is developed for a logical element with an absolute magnitude of its output composition independent of the input code. The method suggested makes it possible to design a complete binary summer on one multiaperture magnetic core.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Static output feedback control via PDE boundary and ODE measurements in linear cascaded ODE–beam systems

This paper addresses the problem of feedback control design for a class of linear cascaded ordinary differential equation (ODE)–partial differential equation (PDE) systems via a boundary interconnection, where the ODE system is linear time-invariant and the PDE system is described by an Euler–Bernoulli beam (EBB) equation with variable coefficients. The objective of this paper is to design a static output feedback (SOF) controller via EBB boundary and ODE measurements such that the resulting closed-loop cascaded system is exponentially stable. The Lyapunov’s direct method is employed to derive the stabilization condition for the cascaded ODE–beam system, which is provided in terms of a set of bilinear matrix inequalities (BMIs). Furthermore, in order to compute the gain matrices of SOF controllers, a two-step procedure is presented to solve the BMI feasibility problem via the existing linear matrix inequality (LMI) optimization techniques. Finally, the numerical simulation is given to illustrate the effectiveness of the proposed design method.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.