A novel dynamic modelling approach for parallel mechanisms analysis

A novel approach (recursive matrix method), which is used for kinematic and dynamic analysis of a 3-DOF parallel mechanism with revolute actuators, is established in this paper. The active links of the mechanism are actuated by three electric motors and have three independent motions. Knowing the evolution of movable platform, first we develop the positions, velocities and accelerations of all elements of the mechanism. An inverse dynamic problem is solved using the principle of virtual work. Finally, recursive relations and graphs for the torques of three actuators are determined. It showed the efficiency of the proposed method by the example.     

Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model

Recent developments in multivariate volatility modeling suggest that the conditional correlation matrix can be described by a time series recursion, where the total number of parameters grows by the power-of-two of the dimension of financial returns. The power of two computational requirement makes high-dimensional multivariate volatility modeling very time consuming. In this paper, we propose two simplified specifications in a multivariate autoregressive conditional heteroscedasticity model. The first specification computes an unconditional correlation matrix from standardized residuals of the model. The second specification restricts the sum of the weights in a time-varying conditional correlation equation to be one. Applying a Bayesian sampling scheme allows the number of parameters to be reduced from the power of two of the dimension to the linear order of the dimension only and simultaneously provides us a framework for model comparison. We test our simplified specifications using simulated and real data from three sectoral indices in Hong Kong, three market indices and four exchange rates. The results suggest that our simplified specifications are more effective than the original formulation.     

仿真技术的现在与未来（综述）

本文对仿真技术的现在与未来进行了简要综述：仿真的地位,目前最热门的仿真领域,仿真软件发展到什么阶段,仿真技术正在发生一个革命,仿真的研究前沿,一体化仿真环境,分布式仿真与仿真模型的并行处理,图形技术与仿真,人工智能技术与仿真．     

Determination of wheel–rail contact points with semianalytic methods

The multibody simulation of railway vehicle dynamics needs a reliable and efficient method to determine the location of the contact points between wheel and rail that represent the application points of the contact forces and influence their directions and intensities. In this work, two semi-analytic procedures for the detection of the wheel–rail contact points (named the DIST and the DIFF methods) are presented. Both the methods consider the wheel and the rail as two surfaces whose analytic expressions are known. The first method is based on the idea that the contact points are located in the point in which the distance between the contact surfaces has local maxima, and is equivalent to solve an algebraic 4D-system. The second method is based on the idea that in the contact points the difference between the surfaces has local minima and is equivalent to solve an algebraic 2D-system. In both cases, the original problem can be reduced analytically to a simple 1D-problem that can be easily solved numerically.     

集成3DS,AutoCAD实现机械设计自动化及机构运行的计算机仿真

本文给出了集成３ＤＳ、ＡｕｔｏＣＡＤ、ＡｕｔｏＬｉｓｐ和ＴｕｒｂｏＣ进行机械设计自动化及机构运行仿真的一种方法。利用这种方法,我们开发了链传动机构的自动设计及动态仿真。     

软件集成环境下的精馏塔动态仿真

化工过程动态仿真的关键问题是物性数据的计算及非线性微分方程和代数方程的求解,商品化的过程稳态模拟软件能够精确计算纯组分及混合物的物性,而MATLAB/SIMULINK具有强大计算及仿真功能。该文通过将Matlab／Simulink与Aspen Plus模拟软件物性和热力学数据库集成,对精馏塔进行了动态仿真研究。文中阐述了软件集成的方法,建立了精馏塔的通用动态数学模型,在Simulink中以S-函数的方式进行了实现。通过软件集成进行过程的仿真研究,充分利用了现有软件的优势,减少了编程工作量,提高了开发效率。     

Locally recurrent neural networks for wind speed prediction using spatial correlation

This paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. Owing to the temporal complexity of the problem, we employ local recurrent neural networks with internal dynamics, as advanced forecast models. To improve the prediction performance, the training task is accomplished using on-line learning algorithms based on the recursive prediction error (RPE) approach. A global RPE (GRPE) learning scheme is first developed where all adjustable weights are simultaneously updated. In the following, through weight grouping we devise a simplified method, the decoupled RPE (DRPE), with reduced computational demands. The partial derivatives required by the learning algorithms are derived using the adjoint model approach, adapted to the architecture of the networks being used. The efficiency of the proposed approach is tested on a real-world wind farm problem, where multi-step ahead wind speed estimates from 15 min to 3 h are sought. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that the suggested learning algorithms outperform three gradient descent algorithms, in training of the recurrent forecast models.     

虚拟试衣系统研究与实现

虚拟衣物仿真为网络购物提供了很大便利。首先通过建立衣物的物理模型,将柔性衣物离散为分布均匀的质点,进而进行动力学受力分析。为获得高效且稳定的算法,采用隐式积分方法对微分方程进行数值求解。碰撞检测问题是布料仿真研究中的重要内容。针对具体碰撞对象设计了高效的碰撞检测方法,采用建立AABB层次包围盒树作为碰撞响应算法。最后进行布料产生碰撞后趋于稳定及滑落的动态仿真演示和穿衣过程仿真演示。该方法在达到较高计算效率的基础上,确保了仿真系统的稳定性。