Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

Robotic Manipulators (RM) are nonlinear and coupling system with time-variant and model uncertainties. In addition, RM are subject to different types of disturbances in practice, such as joint frictions, unknown payloads, and interferences from external systems. In this paper, these adverse factors are regarded as disturbance, and classifies them into internal disturbances and external disturbances. In order to achieve high-precision control, a Nonlinear Disturbance Observer (NDO) is designed to suppress external disturbances, and a Fuzzy Logic System (FLS) is designed to compensate internal disturbances. The scheme can effectively suppress the disturbance and improve the control accuracy. The validity of the scheme is shown by computer simulations of a two-link robot manipulator.

Aiming at a high-precision tracking performance of the control of a machine tool moving axis, this study established a system mathematical model considering the elastic deformation of the ball screw. Then, a sliding mode controller was designed to suppress the influence of uncertainty on the control performance. Next, an extended state observer was designed to observe the system state and disturbance and provide feedback to the sliding mode controller for position control. Finally, the correctness of the designed sliding mode control and extended state observer were proved by MATLAB simulation analysis.

Title: A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory | Keywords: stratigraphy identification, information fusion, BP neural network, DS evidence theory, three-zone | Author: Yuqi Feng, Wangyong He, and Yun Liu