Hybrid genetic algorithm for the identification of high-order synchronous machine two-axis equivalent circuits

A hybrid genetic algorithm is used to find high-order equivalent circuits (ECs) of synchronous machines using standstill frequency response (SSFR) data. The algorithm performs satisfactorily despite the great deal of local minima surrounding the optimal solution of high-order ECs. It gives circuit parameters that simultaneously fit the three independent transfer functions given by the d-axis two-port network of the synchronous machine. It is found that as the order of the EC is increased, the optimization index used in the identification procedure is enhanced in a clear fashion. This leads to a new way for determining the right number of rotor branches required to correctly reproduce the SSFR data. The q-axis network is also analyzed with the hybrid algorithm. The so-called Canay's inductances are included in this one-port network to test if the fitting properties of the q-axis EC can be improved. The SSFR data used in this work is generated by a finite element model of a turbine generator, but actual data can also be readily handled.     

A tool for extracting synchronous machines parameters from the dc flux decay test

A novel computer system tool for the parameter estimation of mathematical synchronous-machine models is presented. It uses state-of-the-art information technology software. The tool allows the electrical power engineers to obtain the electrical parameters of a synchronous machine model. The practical usefulness of the developed system is demonstrated by obtaining the parameter set for conventional and high order d and q axis models from the dc flux decay test. The two-axis model parameters are estimated with the well-known maximum likelihood algorithm. The user can also validate its set of estimated parameters against experimental data. Consequently, a better behavior prediction of synchronous generators within a power system can be achieved. The tool was developed using LabVIEW as a software environment and a low cost data acquisition card, which has a user friendly interface. A solid salient-pole synchronous generator of 7 kVA was used to illustrate its practical impact of the proposed system, where acceptable accuracy in the set of estimated parameters was achieved.     

Adaptive control of permanent magnet synchronous machines with disturbance estimation

In this paper,an adaptive control scheme is introduced for permanent magnet synchronous machines (PMSMs)as an alternative to classical control techniques.The adaptive control strategy capitalizes on the machine’s inverse dynamics to achieve accurate tracking by using an observer to approximate disturbance in the form of friction and load torque.The controller’s output is then fed to a space vector pulse width modulation(SVPWM)algorithm to produce duty cycles for the inverter.The control scheme is validated through a set of simulations on an experimentally validated PMSM model.Results for different situations highlight its high speed tracking accuracy and high performance in compensating for friction and load disturbances of various magnitudes.     

Estimation of parameters in fractional subdiffusion equations by the time integral characteristics method

This paper presents an extension to the time integral characteristics method for estimation of parameters in fractional subdiffusion equations containing Riemann-Liouville and Caputo fractional time derivatives. The explicit representations of the fractional diffusion coefficient and order of fractional differentiation via a Laplace transform of the concentration field are obtained. A technique of optimal Laplace parameter determination by minimization of relative errors bounds is described. The effectivity of the proposed approach is illustrated by numerical example.     

基于梯度算法的支持向量机参数优化方法

首先介绍最近出现的参数优化方法,概括了高效率的参数优化算法应具备的若干特点.然后提出了一种新的支持向量机参数优化方法.该方法先在局域内用混沌优化搜索局域最优点,再将此最优点作为梯度方向,通过改变局域范围跳出局部寻优区域.该方法降低了对性能函数连续且可微的要求,收敛速度快,最终优化解与支持向量机的参数初始值无关.最后,通过仿真实验表明了该方法具有更高的分类和回归准确率.     

Multi-server machine repair model with standbys and synchronous multiple vacation

This paper investigates a machine repair problem with homogeneous machines and standbys available, in which multiple technicians are responsible for supervising these machines and operate a (R, V, K) synchronous vacation policy. With such a policy, if any V idle technicians exist in the system, these V (V < R) technicians would take a synchronous vacation. Upon returning from vacation, they would take another vacation if there is no broken machine waiting in the queue. This pattern continues until at least one failed machine arrives. It is assumed that the number of teams/groups on vacation is less than or equal to K (0 ≦ KV < R). The matrix analytical method is employed to obtain a steady-state probability and the closed-form expression of the system performance measures. Efficient approaches are performed to deal with the optimization problem of the discrete/continuous variables while maintaining the system availability at a specified acceptable level.     

序贯实验设计方法在变换反应动力学方程模型参数估值研究中的应用

根据序贯实验设计原理,提出用于 B_(110-2)催化剂的动力学模型参数估值程序,并提出B_(110-2)催化剂的中温变换反应本征动力学方程。     

基于相关向量机的小世界神经元网络拓扑估计

采用相关向量机从含噪时间序列中估计小世界神经元网络的节点动力学方程和拓扑结构。在具有多项式结构或能以幂级数展开的动力学系统中,将未知方程写成统一多项式形式,原动力学方程的项在统一多项式中是稀疏的,利用稀疏贝叶斯学习估计出稀疏项从而实现动力学方程和拓扑结构的估计。利用该方法对FHN小世界神经元网络进行节点动力学方程和拓扑估计,结果表明,该方法能快速准确地估计节点动力学方程结构和网络拓扑,对动力学方程系数和网络耦合强度有很高的估计精度,而且对噪声有强鲁棒性。     

离散HMM的参数估计

给出了两种离散HMM参数估计，一种是首先估计HMM的转移概率和直接估计离散符号集的分布，然后假设各状态都是正态分布，再从符号集的离散概率分布，来估计正态概率分布的参数(GMM)；另一种是将离散符号设定为正态分布，用与连续HMM相似的方法，来估计离散HMM正态概率分布的参数。     

A tuner that accelerates parameters

We propose a tuner, suitable for adaptive control and (in its discrete-time version) adaptive filtering applications, that sets the second derivative of the parameter estimates rather than the first derivative as is done in the overwhelming majority of the literature.     

参数估计法实现的互相关流量测量系统

由于传统相关流量计测量精度易受外界条件影响,故系统采用参数估计实现渡越时间的计算。其主要思想是把测量值不断地逼近理论值,从而不断地修改数学模型中的参数;当参数确定后,计算流量值。系统采用多核处理器OMAP3530,提高系统的实时性。试验数据表明,采用参数估算法实现的互相关流量计具有精度高、实时性好等优点,并具有实际的工程应用价值。     

基于参数估计的多传感器数据融合

探讨具有随机扰动的环境中同一特征的不同传感器测量值的融合问题，并在理论上给出了基于参数估计的多传感器融合算法，分析了正态分布数据的融合计算公式，并提出以数据探讨技术中的分布图方法来获得一致的传感器数据，从而提高了算法的鲁棒性。     

参数估计法检测与诊断铁路运输车减振装置故障

本文利用参数估计法检测与诊断铁路运输车减振装置故障,并分别对6自由度和4自由度模型进行了仿真和模拟实验.     

参数估计算法在汽车动态称重中的应用

介绍了PC机平台上汽车动态称重系统,主要使用数字滤波方法滤除干扰,对滤波后波形调整后使用参数估计算法进行信号处理,准确的提取出静态轴重信号。实测数据的处理结果证明了方法的有效性。     

两相流流速测量的参数估计法研究

为了解决相关法流速测量分辨率较低、实时性较差的问题，介绍利用极性导向式自适应算法测量两相流流速的参数估计新方法。文中详细论述实现这种算法的硬件电路结构及其工作过程，并给出气水两相流垂直管段与极性相关法进行对比实验的结果。新仪器测量结果的分辨率和实时性优于相关法。     

Modeling of a synchronous reluctance machine accounting for space harmonics in view of torque ripple minimization

The paper deals with modeling of synchronous reluctance motor (SynRM) accounting for all phenomena responsible for torque ripple. Based on winding function approach, the proposed model consists in computing self and mutual inductances considering no sinusoidal distribution of stator windings, slotting effect and no sinusoidal reluctance variation caused by the rotor saliency. Then, optimal current waveforms are determined for each rotor position by solving a second order equation to reduce torque ripple. These currents are used within a vector control scheme. Satisfactory agreement between simulation and experimental results is obtained.     

基于强化学习和机器翻译质量评估的中朝机器翻译研究

针对目前机器翻译模型存在的曝光偏差和译文多样性差的问题,提出一种基于强化学习和机器翻译质量评估的中朝神经机器翻译模型QR-Transformer.首先,在句子级别引入评价机制来指导模型预测不完全收敛于参考译文;其次,采用强化学习方法作为指导策略,实现模型在句子级别优化目标序列;最后,在训练过程中融入单语语料并进行多粒度数据预处理以缓解数据稀疏问题.实验表明,QR-Transformer有效提升了中朝神经机器翻译性能,与Transformer相比,中—朝语向BLEU值提升了5.39,QE分数降低了5.16,朝—中语向BLEU值提升了2.73,QE分数下降了2.82.     

Camera Motion Estimation Through Planar Deformation Determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that, provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion which allows to separate the image deformation in a similarity and a “purely” projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediately deduce camera motion parameters. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.