回转分离器内气固两相流的理论分析

详细地推导了回转分离器内颗粒的运动方程,对分离器内颗粒的运动轨迹.特别是在考虑颗粒形状对阻力影响的情况下作了一定程度的计算,将计算结果与球形颗粒运动轨迹进行了比较,并作了适当的分析.此外,还较为详细地介绍了此类问题的数值求解方法.     

基于加速度传感器的机器人最优学习控制

为工业机器人提出了一种最优学习控制法。这种控制法用加速度误差校正驱动器运动。并提出了一种基于几何级数的极限条件估计学习控制过程收敛条件的理论方法。所提出学习控制法的有效性通过PUMA562机器人的计算机仿真结果得到了证实。     

真空开关在分断过程中电弧引燃过程研究

为了深入了解真空电弧的引燃过程，测量了直流真空电弧起弧阶段的电弧电压，屏蔽罩电位以及屏蔽罩收集的离子电流等参数，分析了真空电弧的引燃过程，起弧后等离子体的扩散过程，以及纵磁场对等离子体扩散的影响。     

半实物仿真中的期望频率特性控制研究

根据半实物仿真系统中期望输出预知的特点,讨论了相应的离散控制问题。首先提出了给定带宽的零相位滞后离散模型,对不可抵销零点的补偿方法进行了研究,并按该模型设计了相应的利用未来信号的前馈控制器。根据该方法可以获得近似的理想闭环模型。     

油田措施规划的随机期望值模型

措施规划对延长油田增产稳产年限,降低开采成本,提高采油速度和采收率,实现措施优化配置十分必要.以油田措施增产最大化为目标,兼顾成本、工作量等目标,建立了油田措施规划的随机期望值模型,并给出了模型的混合智能算法.实例表明,该模型理论基础严格,求解方法科学有效,并具有一定的智能性,为油田措施规划提供了新的决策依据.     

多品种随机量—本—利分析

讨论了在产品市场价格、单位变动成本、产销量、固定成本和社会对企业产销多品种产品的货币投入量为不确定性情况下,随机量一本一利模型的建立,分析了模型下的企业决策问题,并通过实例将其结果加以应用。     

非完整移动机器人的自适应滑模轨迹跟踪控制

针对动力学模型描述的非完整移动机器人系统，研究了其在未知参数和不确定性干扰下的轨迹跟踪控制问题，基于反演技术和自适应滑模控制的思想设计了具有全局渐近稳定的轨迹跟踪控制律。谊控制律将系统分解为低阶子系统来处理，利用中间虚拟控制量和部分Lyapunov函数简化了控制器设计，并具有直观的稳定性分析．满足了移动机器人的轨迹跟踪要求，且具有设计方法简单、鲁棒性强的特点。仿真结果验证了所设计控制器的有效性和正确性。     

随机序及其随机序之间的关系

文章先介绍了几种主要的随机序,其中包括期望效用准则、随机占优、单调风险率条件、单调似然率条件和均值-方差准则,然后在此基础上研究了它们之间的关系。     

A Statistical Method for Analysis of Technical Data of a Badminton Match Based on 2-D Seriate Images

The use of computer vision technology to collect and analyze statistics during badminton matches or training sessions can be expected to provide valuable information to help coaches to determine which tactics should be used by a player in a given game or to improve the player＇s tactical training. A method based on 2-D seriate images by which statistical data of a badminton match can be obtained is presented. Image capture and analysis were performed synchronously using a multithreading technique. The regions of movement in the images were detected using a temporal difference method, and the trajectories of the movement regions were analyzed using sedate images. The shuttlecock trajectory was extracted from all detected trajectories using various characteristic parameters. The stroke type was determined by comparing the shuttlecock trajectory data with a set of stroke definition data. The algorithm was tested at a training center, and the results were compared with baseline data obtained by expert visual inspection using four video samples, which included approximately 10 000 frames. The shuttlecock trajectory and stroke type were detected correctly in almost 100% of the analyzed video sequences. The average speed of the automated analysis was approximately 40 frames/s, indicating that the method can be used for real-time analysis during a badminton match. The system is convenient for use by a sports coach.