基于ANSYS的直流励磁电机电磁场分析与计算

应用有限元数值分析软件（ANSYS）对直流串励电机内部电磁场进行分析计算,获得电动机负载运行时的电枢反应磁场,并通过ANSYS的后处理功能得到负载运行气隙磁密分布图,在此基础上对直流励磁电机的电磁转矩进行了分析计算,通过样机实验验证了计算结果,证明了理论分析的正确性。为直流励磁电机的性能分析及设计提供了一定的参考。     

线性时滞系统前馈-反馈次优控制: Taylor 级数法

研究线性时滞系统最优控制的前馈反馈近似设计问题.基于Taylor级数法,将系统的二次型最优控制问题转化为线性代数方程组的求解问题,给出了系统前馈反馈次优控制律的存在唯一性条件和Taylor级数表示形式.仿真算例验证了方法的有效性.     

An artificial neural network (p, d, q) model for timeseries forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.     

稀土萃取过程组分含量的RBF软测量建模

稀土萃取分离过程的自动控制一直是稀土工业急待解决的关键难题,要实现稀土萃取分离过程的自动控制,首先必须解决串级萃取槽中各稀土组分含量的在线检测,而传统的检测又有很大的问题,软测量技术在稀土萃取过程控制中的应用研究开始活跃起来。该文是基于RBF网络的稀土萃取过程组分含量进行软测量建模的研究。     

Numerical solutions of the one-phase classical Stefan problem using an enthalpy green element formulation

The enthalpy method is exploited in tackling a heat transfer problem involving a change of state. The resulting governing equation is then solved with a hybrid finite element - boundary element technique known as the Green element method (GEM). Two methods of approximation are employed to handle the time derivative contained in the discrete element equation. The first involves a finite difference method, while the second utilizes a Galerkin finite element approach. The performance of both methods are assessed with a known closed form solution. The finite element based time discretization, despite its greater challenge, yields less reliable numerical results. In addition a numerical stability test of both methods based on a Fourier series analysis explain the dispersive characters of both techniques, and confirms that replication of correct results is largely attributed to their ability to handle the harmonics of small wavelengths which are usually dominant in the vicinity of a front.     

VTS特征补偿算法在语音识别中的实用性研究

在语音识别实际应用中,由于噪声的多样性,会造成训练和测试的失配,导致系统性能下降.特征补偿作为鲁棒性语音识别的一种重要方法,通过对训练和测试环境之间差异的研究,在特征空间中修正语音特征,使得修正后的测试语音特征能够更加接近训练语音特征.本文介绍一种实用的基于环境模型矢量泰勒级数(VTS)近似的特征补偿算法.首先验证传统的VTS离线算法在实际车载环境下的有效性;其次由于离线算法本身运算量很大,为了使其实用化,本文对算法进行改进,使其在提高效率的同时又能够保证与离线时相当的性能.通过实验结果验证,本文提出的实用化VTS算法在识别性能上相当接近离线时最好的性能.     

快速高精度除法算法的FPGA实现

为在现场可编程门阵列(FPGA)中实现快速高精度除法,在传统的倒数除法的基础上,提出一种改进算法.对倒数求解采用泰勒级数展开结合优化搜索逼近,求出各个分区间内的拟合一次两项式,再通过一次牛顿迭代提高精度.时序仿真结果表明,以该算法构建的除法器易于在FPGA上实现,时延仅为6个时钟周期,能达到2(-34)的有效精度和86...     

基于NSCT和泰勒级数的图像超分辨率重建

结合非下采样Contourlet变换(NSCT)的多尺度、方向特性、各向异性、平移不变性以及泰勒级数高效逼近的优点,提出一种利用泰勒级数插值代替双线性插值的超分辨率图像重建算法.实验结果显示,该算法可以较好地恢复图像的细节信息和纹理特征,有效抵抗高斯噪声的干扰.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.     

An approach for temporal analysis of email data based on segmentation

Many kinds of information are hidden in email data, such as the information being exchanged, the time of exchange, and the user IDs participating in the exchange. Analyzing the email data can reveal valuable information about the social networks of a single user or multiple users, the topics being discussed, and so on. In this paper, we describe a novel approach for temporally analyzing the communication patterns embedded in email data based on time series segmentation. The approach computes egocentric communication patterns of a single user, as well as sociocentric communication patterns involving multiple users. Time series segmentation is used to uncover patterns that may span multiple time points and to study how these patterns change over time. To find egocentric patterns, the email communication of a user is represented as an item-set time series. An optimal segmentation of the item-set time series is constructed, from which patterns are extracted. To find sociocentric patterns, the email data is represented as an item-setgroup time series. Patterns involving multiple users are then extracted from an optimal segmentation of the item-setgroup time series. The proposed approach is applied to the Enron email data set, which produced very promising results.