This paper takes a Halbach permanent magnet linear motor (PMLM) without a core as the research subject; based on the traditional structure of a single-layer PM array, an improved PMLM with an optimized double-layer Halbach PM array is proposed to reduce the harmonic components of the air gap magnetic flux density (AGMFD) for direct-drive applications in the feeding system of the machine tool which need high force capability. The 3-D models of PMLMs with single-layer and double-layer Halbach PM arrays are established by using finite element (FE) software; the air gap magnetic field, the back electromotive force (EMF), and the electromagnetic thrust of the two types of motors are analyzed. The simulated results show that the PMLM with an optimized double-layer Halbach PM array can achieve better performances of the motor and reduce the cost of the PMLM. Finally, the experimental results verify that the proposed double-layer Halbach PMLM is effective and practical.

In this paper, we propose a measurement method for dynamic angles in large-space based on machine vision and servo control. The azimuth and pitch angles are obtained in real time by identifying and tracking a cooperative target in a high-precision servo system with a camera. Furthermore, the distance between the target and the camera is calculated by means of a monocular measurement. Based on the measured angles and distance, the dynamic angle of the rotating object in test is derived according to the transformational relations. In order to decrease measurement errors, an error correction model is established by the connection between the measurement distance and observation errors caused by the camera, dynamic tracking and pose changing of the target plate. A manual displacement platform with high precision in two-dimensions is conducted to validate the effectiveness of the proposed approach. The experimental results show that, compared with the existing measurement methods, the proposed method enlarges the measurement range of large-space angles to 18.788 m, and the measurement errors of the azimuth channel and pitch channel are 0.018° and 0.019°, respectively, which achieve a high measurement accuracy and verify the validity of the method.

Edge‐preserving denoising is important in image analysis. However, most existing methods suffer from smoothing and loss of high frequency detail at the edges. Aiming at this issue, a multi‐path residual network using edge prior information is proposed. Specifically, the edge is first recovered by a newly designed parallel edge extraction module. Then it is used as a priori to generate affine transform parameters to guide the weight distribution between noise and edge, so that the shallow feature extraction pays more attention to preserving edges. In the deep feature extraction stage, a multi‐scale attention unit is designed to provide the spatial neighbourhood information of the feature points to filter and activate the deep features, which further enhances the ability of the network to extract fine‐grained noisy features. Finally, by taking the difference between the noisy image and the noise feature obtained with residual learning, the denoised image is produced. Extensive experiments show that the PSNR is improved by 0.00–0.78 dB on grey image denoising and 0.00–2.69 dB on colour image denoising compared with others. The denoised image has a better ability to preserve image edges, high frequency details and visual perception.