This paper focuses on the reduction of the radial force harmonics of a consequent-pole permanent magnet vernier machine by current harmonic injection. The current harmonic injection has been applied based on dq-axis frame. In the proposed method, a sixth harmonic of d-axis current is injected to reduce a sixth harmonic of the radial force with zero order mode. The effectiveness of the current harmonic injection is verified by finite element analysis. The simulation results show that sixth harmonic of radial force is reduced by 45.7%, resulting in the reduction of a vibration at the respective harmonic by 42%. To demonstrate the controllability of the current regulator for the proposed method, a circuit simulation has been carried out with a proportional, integral, and resonance (PIR) controllers. The simulation results show that the PIR controller can realize superior performance of the current harmonic regulation compared with the conventional PI controller.

This article investigates the application of star-delta hybrid concentrated-winding (CW) in permanent magnet vernier machines (PMVMs), with main focus on power factor and field-weakening capability. The analysis results show that the hybrid CW exhibits low-coupling property, namely both the mutual inductances between the comprising coils of each single phase and that among three-phase windings are eliminated. As a result, the q-axis flux linkage and required terminal voltage are reduced substantially, which contributes to improving power factor and field-weakening property. When operating below base speed, higher voltage margins are obtained, thus the proposed PMVM exhibits a higher power factor and wider constant torque region. With speed over base speed, the field-weakening control strategy is adopted, allowing the proposed PMVM to employ a higher q-axis current to generate torque. As a consequence, the output capability and power factor under high-speed field-weakening region are improved. In particular, the maximum achievable output power is improved by 19%, and the constant power speed range (CPSR) ratio is improved from 2 to almost 10.

Permanent magnet vernier machines (PMVMs) are recognized as the most promising candidates for high-torque direct-drive applications. However, their market penetration is hindered by the low power factor. This paper aims to address the low-power-factor challenge, by employing the concept of low-coupling winding. As compared with two conventional PMVMs, the proposed PMVM with low-coupling winding exhibits a substantially improved power factor. To objectively evaluate the potential of the proposed PMVM, a comprehensive comparison between the proposed PMVM and the conventional permanent magnet synchronous machine (PMSM) is conducted. The no-load back-EMF, output torque, power factor, core losses, and efficiency are compared under various operating conditions, encompassing the entire torque-speed range. The strengths and weaknesses of the PMVM are analyzed, and the most promising application scenarios are suggested. Finally, a prototype of the proposed PMVM is fabricated to validate the analysis and comparison results.

We bring the novel idea of exploiting motifs into network embedding, in a dual-level network representation learning model called RUM (network Representation learning Using Motifs). Towards the leveraging of graph motifs that constitute higher-order organizations in a network, we propose two strategies, namely MotifWalk and MotifRe-weighting for learning motif-aware network embeddings. Motif-based and node-based representations are simultaneously generated, so that both the high-order structures and each node's individual properties are preserved in the final embeddings. We demonstrate that RUM has strong and well-balanced capability of preserving lowerorder proximities while discovering and capturing higher-order network structures. In empirical evaluation, RUM is tested on multiple public datasets, that range from small to medium citation networks to a large social network with more than a million nodes. Results show that the use of motifs in the representation learning process brings substantial benefits in reallife tasks, resulting in up to 12% microF1 and 8% macroF1 relative gains for node classification performance over the bestperforming competing methods.

Person re-identification(ReID) is a task of associating persons that cross the non-overlapping camera views at different locations and times. It is a challenging task due to the large variations in person pose, background, luminance, occlusion, low resolution, etc. How to extracting a powerful features representation is the prime problem in ReID and is still unsolved. In this paper, we propose a cascade network architecture combined with a generative adversarial networks(GANs) and a convolutional neural network(CNN) to improve the performance of person re-identification. The GANs first generates the person body parts segmentation from the person image, and then inputs the segmentation label into the connected CNN together with the original person image. Finally obtain a discriminative and robust feature representation for ReID task. The body parts segmentation partitioning the person image into multiple segments, such as background, head, face, arms, lags, etc. The body parts segmentation information contains accurate borders and category attributes for body parts, which makes the our model more accurate compared to other predefined rigid parts alignment models. Experiments are conduced on the CUHK03, Market1501, DukeMTMC-ReID datasets and the results demonstrate that this approach outperforms several existing state-of-the-art methods.