数据流上的预测聚集查询处理算法

实时数据流未来趋势的预测具有重要的实际应用意义.例如,在环境监测传感器网络中,通过对感知数据流进行预测聚集查询,观察者可以预测网络覆盖的区域在未来一段时间内的平均温度和湿度,以确定是否会发生异常事件.目前的研究工作多数集中在数据流上当前数据的查询,数据流上预测查询的研究工作还很少.采用多元线性回归方法,给出了数据流上的聚集值预测模型,提出了一种数据流预测聚集查询处理方法.当预测失败的次数大于预先给定的阈值时,给出了一种预测模型自动调整策略,以降低预测误差.还提出了滑动窗口的更新周期、数据流的流速对预测精度影响的数学模型.理论分析与实验结果表明,提出的预测聚集查询处理算法具有较高的性能,并且能够返回满足用户精度要求的预测查询结果.在实验中,采用TPC-H国际标准测试数据和TAO(tropical atmosphere ocean)测量的海洋表面空气温度数据来构造数据流.     

Modeling of a greenhouse prototype using PSO and differential evolution algorithms based on a real-time LabView™ application

In general, to obtain an adequate mathematical model of a greenhouse is a difficult task due to the complexity of the involved equations that describe the dynamics of the system, and the required high number of physical parameters, which are complicated or even impossible to measure. In these situations, estimation methods are commonly used to obtain a suitable approximation for those parameters. This paper presents the application and comparison of a collection of methods based on Particle Swarm Optimization (PSO) and Differential Evolution (DE), using them as the tools to identify the parameters that complete a proposed mathematical model for a greenhouse. These parameters are sought aiming to approximate the dynamic behavior of a greenhouse physical prototype building in CINVESTAV Campus Guadalajara, by using the heuristic algorithms in order to minimize a proposed error function, which considers as arguments estimations and measurements of the two more representative dynamics of the climate conditions inside a greenhouse: namely, the air temperature and relative humidity. Different forms of PSO and DE algorithms are considered and applied in order to select the one that achieves the set of parameters with the lowest evaluation error. The comparison of the selected algorithms is carried out in an offline optimization schedule using real data recorded through the LabView™ SignalExpress application, and a real-time implementation in a LabView™ code, implemented to optimize the model in a sample to sample execution. The proposed model, with its corresponding computed parameters, is validated comparing its results against the real dynamic behavior of the temperature and relative humidity, that are measured directly from the greenhouse prototype, showing a good agreement between real and estimated values. Several tests were executed in order to find PSO and DE best calibration conditions. Experimental results allow us to propose an efficient way to deal with numerical optimization problems of high complexity, applying a two stages scheme based on a first offline pre-identification, where the obtained results are used as initial condition for an online, real-time refinement process.     

基于数据ADP算法的一类带有执行器饱和的未知离散时间系统最优跟踪控制

针对一类带有执行器饱和的未知动态离散时间非线性系统, 提出了一种新的最优跟踪控制方案. 该方案基于迭代自适应动态规划算法, 为了实现最优控制, 首先建立了未知系统动态的数据辨识器. 通过引入M网络, 获得了稳态控制的精确表达式. 为了消除执行器饱和的影响, 提出了一个非二次的性能指标函数. 然后提出了一种迭代自适应动态规划算法获得最优跟踪控制的解, 并给出了收敛性分析. 为了实现最优控制方案, 神经网络被用来构建数据辨识器、计算性能指标函数、近似最优控制策略和求解稳态控制. 仿真结果验证了本文所提出的最优跟踪控制方法的有效性.     

无线温湿度测量传感器网络设计

无线传感器网络是当前信息领域中研究的热点之一,可用于特殊环境实现信号的采集、处理和发送;文中介绍了一种无线温湿度传感器网络的设计,以PIC单片机为核心,利用集成湿度传感器和数字温度传感器设计出了温湿度传感器网络节点的硬件电路,并通过无线收发模块与控制中心通信,讨论和提出了传感器节点的低功耗设计以及数据通信的可靠性方法;该系统稳定性好,通信效率高,可广泛应用于环境检测.     

水冷型PEMFC温湿度建模与智能控制

当负载电流一定时,质子交换膜燃料电池(PEMFC)的工作温度和质子膜的相对湿度是影响电池输出性能的主要因素.分析电池电流密度与最优工作温湿度的关系,建立基于温湿度耦合模型的最优温湿度操作条件的电压模型.通过对冷却水流量和阴阳极气体加湿度进行综合控制,保持电堆的工作温湿度在最佳状态,不仅保证了电池最优的输出性能,还可以延...     

基于GM（1，1）模型的水热式孵化机温度控制装置设计

介绍了一种新型水热式孵化机温度控制装置,该系统以AT89S51为控制器,通过数字式温度传感器SHT11采集孵化箱温度,应用GM（1,1）灰色模型建立温度的数学模型,根据监测到的孵化箱温度数据进行预测,判断温度的变化趋势,计算得出下一时刻的温度预测值。控制器根据预测结果,产生相应控制信号调节孵化箱温度,最终达到使孵化箱温度维持在键盘输入的设定值水平的要求。     

基于PSO-BP神经网络的湿度传感器温度补偿

针对自动气象站采用的HMP45D型温湿一体化传感器在实际应用过程中易受温度影响的问题,提出了基于粒子群优化算法(PSO)的BP神经网络温度补偿模型,利用粒子群优化算法对BP神经网络的初始权值阈值进行全局寻优,将粒子群优化算法优化好的权值阈值赋给BP神经网络,对BP神经网络进行训练。根据不同温度条件下测得的多组湿度传感器数据,通过建立模型,实现温度补偿,与传统BP神经网络补偿结果进行比较。实验表明,与传统BP神经网络模型相比,利用PSO-BP神经网络模型进行温度补偿后所得的误差绝对值之和降低了10.3887%RH,PSO-BP神经网络可以克服传统BP神经网络易陷入局部极值的局限,补偿精度更高,能更加有效地补偿温度对湿度传感器的影响。     

Prediction of indoor temperature and relative humidity using neural network models: model comparison

The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.     

Stabilization of sampled-data nonlinear systems by receding horizon control via discrete-time approximations

Results on stabilizing receding horizon control of sampled-data nonlinear systems via their approximate discrete-time models are presented. The proposed receding horizon control is based on the solution of Bolza-type optimal control problems for the parametrized family of approximate discrete-time models. This paper investigates both situations when the sampling period T is fixed and the integration parameter h used in obtaining approximate model can be chosen arbitrarily small, and when these two parameters coincide but they can be adjusted arbitrary. Sufficient conditions are established which guarantee that the controller that renders the origin to be asymptotically stable for the approximate model also stabilizes the exact discrete-time model for sufficiently small integration and/or sampling parameters.     

KM6环控系统的湿度调节过程的数字仿真

KM6载入试验容器环控系统是在地面实验室为宇航员提供一定的氧氮比率和一定温湿度气体的系统,该系统采用超声波雾化器为系统加湿。该文介绍了环控系统的湿度调节方法,并建立了湿度调节过程中,加湿装置的的动态数学模型、气体湿度在管路中变化的数学模型及湿度传感器的数学模型,并用这些模型在Matrixx软件上进行仿真。仿真结果表明在试验舱温度较低、压力较高的情况下,能调节的最大相对湿度较大,反之较小。在试舱温验度高于一定值、压力底于一定值的情况下,系统调节的相对湿度达不到70％,这只能通过降低试验舱温度、升高其压力;或通过降低微调室的压力来达到。     

Robust stochastic moment control via genetic-pole placement in communication network parameter setting

In this paper, the problems of stochastic robust approximate covariance assignment and robust covariance feedback stabilization, which are applied to variable parameters of additive increase/multiplicative decrease (AIMD) networks, are considered. The main idea of the developed algorithm is to use the parameter settings of an AIMD network congestion control scheme, where parameters may assign the desired network’s window covariance, with respect to the current network conditions. The aim is to search for the optimal AIMD parameters of a feedback gain matrix such that the objective functions defined via appropriate robustness measures and covariance assignment constraints can be optimized using an adaptive genetic algorithm (AGA). It is shown that the results can be used to develop tools for analyzing the behavior of AIMD communication networks. Quality of service (QoS) and other performance measures of the network have been improved by using the proposed congestion control. The accuracy of the controller is demonstrated by using MATLAB and NS software programs.     

基于单片机的培养箱温湿度数字解耦控制系统设计

针对培养箱温、湿度控制过程具有时变、非线性、参数强耦合等特点,提出基于系数解耦的数字PID培养箱温、湿度控制方法;文章采用AT89S51单片机为主控芯片、温度传感器AD590、湿度传感器IH3605为检测元件设计了一个温、湿度解耦控制系统,实现了培养箱温、湿度的实时控制与测量;实验和经验表明,该系统自适应能力强,控制精度高;经实验对比测试,系统温度绝对误差可控制在0.2℃、湿度绝对误差可控制在2%RH范围内,满足了培养箱温、湿度高精度控制要求。     

Analysis of Closed Queuing Networks with Periodic Servers

A periodic network is a queuing network whose steady-state behavior is not constant in time, but repeats itself in a cycle. This behavior may be caused by the introduction of periodic servers, e.g., paging drums. The model presented is a generalization of some other models of queuing networks, and provides a more general definition of steady-state behavior. A theoretical solution is presented. Examples of theoretical and approximate solutions are presented for a well-known queuing network model of a computer system.     

Application of soft computing models to hourly weather analysis in southern Saskatchewan,Canada

Accurate weather forecasts are necessary for planning our day-to-day activities. However, dynamic behavior of weather makes the forecasting a formidable challenge. This study presents a soft computing model based on a radial basis function network (RBFN) for 24-h weather forecasting of southern Saskatchewan, Canada. The model is trained and tested using hourly weather data of temperature, wind speed and relative humidity in 2001. The performance of the RBFN is compared with those of multi-layered perceptron (MLP) network, Elman recurrent neural network (ERNN) and Hopfield model (HFM) to examine their applicability for weather analysis. Reliabilities of the models are then evaluated by a number of statistical measures. The results indicate that the RBFN produces the most accurate forecasts compared to the MLP, ERNN and HFM.     

Fault-tolerant control for a class of nonlinear sampled-data systems via a Euler approximate observer

In this paper, a fault-tolerant control (FTC) scheme is developed for a class of nonlinear sampled-data systems. First, a Euler approximate discrete model is used to describe the plant under the sampling. Under this model, an observer-based fault estimation method is proposed. To guarantee the accuracy of both the state and fault estimation values, the conditions to make the approximate model consistent with the exact model are presented. Then, an active fault-tolerant controller, which has a constraint condition for the sampling time, is designed to make the faulty system stable. Finally, an inverted pendulum is used to show the efficiency of the proposed method.     

A Mathematical Cybersecurity Model of a Computer Network for the Control of Power Supply of Traction Substations

Based on the analysis of the problem of cybersecurity, it is shown that its solution is connected with the solution of a complex of interdependent problems whose features follow from the topology of a cyberspace. A graph is proposed that adequately reflects the topology of the computer network for the control of a system of power supply of traction substations and its mathematical model as the basis for the creation of modern cybersecurity models. The developed mathematical model of cybersecurity of a computer network is based on the theory of Pukhov’s differential transforms. The criterion of cybersecurity is formalized, and a minimax principle is proposed for the minimization of a functional in cases of worst combinations of intensities of flows of cyberattacks and protective actions. An intelligent method is developed to search for an optimal strategy for ensuring the cybersecurity of a computer network by the investigation of the functional formalized in this article for extremum.     

Media streaming via TFRC: An analytical study of the impact of TFRC on user-perceived media quality

TCP-Friendly Rate Control (TFRC) is being adopted in Internet standards for congestion control of streaming media applications. In this paper, we consider the transmission of prerecorded media from a server to a client by using TFRC, and analytically study the impact of TFRC on user-perceived media quality, which is roughly measured by calculating the rebuffering probability. A rebuffering probability is defined to be the probability that the total duration of all rebuffering events experienced by a user is longer than a certain threshold. Several approaches are presented to help an application determine an appropriate initial buffering delay and media playback rate in order to achieve a certain rebuffering probability under a given network condition. First, we derive a closed-form expression to approximate the average TFRC sending rate, which could be used as the maximum allowed playback rate of a media stream. Second, we develop a queueing model for a TFRC client buffer with the traffic described by a Markov-Renewal-Modulated Deterministic Process (MRMDP), which captures the fundamental behavior of TFRC that predicts the immediate future TCP sending rate based on the history of past loss intervals. We present a closed-form solution and a more accurate iterative method to solve the queueing model and calculate the rebuffering probability.     

Robustness design of nonlinear dynamic systems via fuzzy linearcontrol

This study introduces a fuzzy linear control design method for nonlinear systems with optimal H^∞ robustness performance. First, the Takagi and Sugeno fuzzy linear model (1985) is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, H^∞ performance design can be achieved in nonlinear control systems. In the proposed fuzzy linear control method, the fuzzy linear model provides rough control to approximate the nonlinear control system, while the H^∞ scheme provides precise control to achieve the optimal robustness performance. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. In the case that state variables are unavailable, a fuzzy observer-based H^∞ control is also proposed to achieve a robust optimization design for nonlinear systems. A simulation example is given to illustrate the performance of the proposed design method     

概率预测的网络数学模型及其应用

通过对网络结构进行分析,将网络模型用有向图进行表示,为其建立了基于概率预测的网络数学模型,并以解决交通拥堵问题为例,对该模型进行实际应用,为其设计出相应概率预测算法。通过对算法进行分析,进而抽象出一个更一般的网络数学模型,用线性规划算法给出这个模型的近似解法,以及迭代使用线性规划法的一个精确解法,通过精确解法的实验数据分析,得出贪婪算法在该模型中具有较高的效率。     

On the joint link dimensioning and routing metric assignment problem for reliable WDM networks

In this paper, we tackle the joint link dimensioning and routing metric assignment problem for reliable wavelength division multiplexing (WDM) networks. This design problem consists to find the number of wavelength channels on each link and the routing metrics (considering shortest-path routing) that ensure the routing of all virtual wavelength paths (VWPs) and the successful rerouting of the reliable VWPs for all failure scenarios of interest to the network planner. A mixed integer mathematical programming model is proposed for the problem. The model is adapted for the single link failure scenarios. Since the problem is NP-hard, we propose a tabu search algorithm to obtain good approximate solutions for real-size instances of the problem. Finally, a lower bound is proposed and numerical results are presented and analyzed.