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To identify parameters of electromagnetic actuators with analytical models, there are problems such as poor model accuracy, multiple physical fields coupling of model, and slow convergence. Based on error sensitivity numerical analysis, a parameter semi-analytical identification method for electromagnetic actuators is proposed. In this article, a diesel engine speed governing electromagnetic actuator is taken as the research object. First, with finite element method, a numerical simulation model of the electromagnetic actuator is established, and sensitivity of main geometrical parameters relative to electromagnetic force is analyzed. Secondly, with theoretical deduction, a difference model of the nominal and measurement electromagnetic force is built, and the electromagnetic actuators geometrical parameter identification formula is gotten. Thirdly, different numerical methods to construct a system error sensitivity matrix are compared, and the compared result is the accuracy of central difference better. Finally, the average static characteristics error of the electromagnetic actuator is reduced from 3.3174 to 1.0182. Therefore, the identification method is verified effective and feasible.

By taking transmission delay into account, the optimization of distributed rendering environment (DRE) was concerned in this paper. Considering distributed animation rendering environment as the object of study, two Generalized Stochastic Petri Nets (GSPN) models for “completely distributed storage” infrastructure (CDSI) and “hybrid distributed storage” one (HDSI) were constructed respectively. Moreover, by choosing average time delay as the performance index, analysis was conducted by applying the equivalent formulas for solving Stochastic Petri Nets (SPN) models of serial and parallel types to these two GSPN models. Based on comparison of such models’ numerical simulation results, conclusion on these two infrastructures’ advantages and disadvantages is deprived thereby. It offers important quantitative foundations for choosing appropriate storage schemes and scheduling algorithms.

Based on a three-dimensional MHD model, a relatively real interplanetary solar wind background of Jan. 1997 event is obtained by using a set of initial and boundary conditions near the real space environment derived from some observations. Results show that the magnetic field at the source surface is equatorially asymmetric and is obviously tilted and wrapped. In addition, the plasma variables are asymmetric in relation to the event location. The numerical experiment about the different schemes of Bθ show that under an obviously tilted current sheet like this event Bθ can not be assumed to be zero. Based on these background results the propagation process of disturbance is obtained and the comparison with the WIND observations is also provided here.

A high-resolution,nonhydrostatic, three-dimensionalplanetary boundary-layer (PBL) model wasestablished considering the characteristic features of an open-pit mine, which is only (2 km × 2 km) wide and more than 100 m deep. The (E-∈) closure schemes was used in the model. Physical processes such as shortwave radiation of the sun and its uneven distribution on the ground,longwave radiation of earth-atmosphere systems, sensible heat and latent heat and heat flowing into the substrate of the earth, were involved in the model. Using this model PBL structures over this kind of microscale concave terrain (an open-pit mine) were fully studied and numerical experiments on the effects of parameters including H, depth of the mine, α, slope angle of the mine, u, incidcent airflow velocity, and P, indicating shear of the initial velocity, on the PBL were also made. Finally, the evolution process of the PBL was simulated. The results showed that a characteristic and major feature of the PBL is re-circulation, which is greatly influenced by the topography of the mine and the meteorolgical conditions therein. Thermal and mechanical forcing play different but important roles in the evolution process of the PBL.

Opinion dynamics focuses on the opinion evolution in a social community. Recently, some models of continuous opinion dynamics under bounded confidence were proposed by Deffuant and Krause, et al. In the literature, agents were generally assumed to have a homogeneous confidence level. This paper proposes an extended model for a group of agents with heterogeneous confidence levels. First, a social differentiation theory is introduced and a social group is divided into opinion subgroups with distinct confidence levels. Second, a multi-level heterogeneous opinion formation model is formulated under the framework of bounded confidence. Finally, computer simulations are conducted to study the collective opinion evolution, focusing on three key factors: the fractions of heterogeneous agents, the initial opinions, and the group size. The simulation results demonstrate that the number of final opinions depends on the fraction of close-minded agents when the group size and the initial opinions are fixed; the final opinions converge more easily when the initial opinions are closer; and the number of final opinions can be approximately modeled by a linear increasing function of the group size and the increasing rate is the fraction of close-minded agents.

In this paper, we propose a new research problem on active learning from data streams, where data volumes grow continuously, and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. To tackle the technical challenges raised by the dynamic nature of the stream data, i.e., increasing data volumes and evolving decision concepts, we propose a classifier-ensemble-based active learning framework that selectively labels instances from data streams to build a classifier ensemble. We argue that a classifier ensemble's variance directly corresponds to its error rate, and reducing a classifier ensemble's variance is equivalent to improving its prediction accuracy. Because of this, one should label instances toward the minimization of the variance of the underlying classifier ensemble. Accordingly, we introduce a minimum-variance (MV) principle to guide the instance labeling process for data streams. In addition, we derive an optimal-weight calculation method to determine the weight values for the classifier ensemble. The MV principle and the optimal weighting module are combined to build an active learning framework for data streams. Experimental results on synthetic and real-world data demonstrate the performance of the proposed work in comparison with other approaches. © 2010 IEEE.

In this paper we propose a preconditioned lattice Boltzmann (LB) method for steady incompressible flows. For steady flows, the macroscopic equations derived from this LB model are equivalent to those from the standard LB model, but with an improved eigenvalue system. The proposed model can be viewed as an explicit solver for preconditioned compressible Navier-Stokes equations. Linear stability analysis is performed and the results show that the stability of the model is the same as that of the standard LB model for low Mach numbers. The proposed model retains the structure of the standard LB model and, hence, possesses all the advantages. Numerical tests show that the convergence rate can be enhanced as much as an order of magnitude compared to the standard lattice Boltzmann method. The accuracy of the solutions is improved as well.