轴方向的英文

• direction of principal axis

• 轴    axle; shaft
• 方向    direction; orientation
• t轴方向    direction of t axis
• x轴方向    x direction; x-direction
• y轴方向    y direction
• 横轴方向    x direction
• 经轴方向    transaxial direction
• 摄影轴方向    bearing of exposing axis
• 视野轴方向    visual field axis orientation
• 向轴方向    axially
• 纵轴方向    y direction
• 向螺旋轴方向的    inspin
• 沿横轴方向运动    sideslip
• 在y轴方向运动    y motion; y-motion
• 机翼向螺旋轴方向倾斜    in-spin wing
• 轴向增强，轴铁筋，轴方向铁筋    axial reinforcement
• 推式镗孔(镗杆背主轴方向进刀)    push boring
• 傍轴方程    paraxial equation
• 前轴方形转向架    steering axle quadrangle
• 旋转轴方位误差    rotation axis orientation error
• 方向；方向型    directional
• 向…方向,在…方向    in the direction of
• y方向    y direction; y position; y-direction
• 表方向    apparent bearing

• The longitudinal distortion , lateral displacement and luminance difference of the reconstructed images at different depth of field in multiple - exposure rainbow holography for 3 - d synthesis of multiple ct slides were analyzed , for which the effective compensation methods were presented , and the experimental results were given
  分析了应用彩虹全息术实现ct图片的立体合成过程中再现像在轴向及垂轴方向上的畸变以及再现像亮度的失真,分别提出了相应的补偿方法，并给出了相应的实验结果
• As neural network has the ability of self - learning , that utilizes prior output data of uncertain system to estimate iteratively the static state property of system in order to achieve ideal approaching precision for identification of the positive model , a robust iterative learning control scheme on the basis of better positive model is designed . the neural network is used to identify the positive model of nonlinear system on iterative axis , which can give feed - forward action of iterative learning controller to reduce the effects of nonlinear properties and model uncertainties . meanwhile , feedback action of iterative learning controller make joint movement follow the desired trajectory on time axis by using controlled parameters derived by the neural network
  由于神经网络具有自学习能力，它可利用不确定性系统的历史输出数据对系统的稳态特性进行估计，使得对系统正向模型的辨识达到理想的逼近精度，然后在此正向模型的基础上进行学习控制律的设计：即采用神经网络辨识非线性系统的正向模型，并消除系统不确定性和外部干扰的影响，使关节运动沿迭代轴方向逼近期望轨迹；迭代学习控制器在线学习控制参量，使关节运动沿时间轴方向跟踪期望轨迹。