To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

The harmonic reducer, with its advantages of high precision, low noise, light weight, and high speed ratio, has been widely used in aerospace solar wing deployment mechanisms, antenna pointing mechanisms, robot joints, and other precision transmission fields. Accurately predicting the performance of the harmonic reducer under various application conditions is of great significance to the high reliability and long life of the harmonic reducer. In this paper, a set of automatic harmonic reducer performance test systems is designed. By using the CANOpen bus interface to control the servo motor as the drive motor, through accurately controlling the motor speed and rotation angle, collecting the angle, torque, and current in real time, the life cycle test of space harmonic reducer was carried out in high vacuum and low temperature environment on the ground. Then, the collected data were automatically analyzed and calculated. The test data of the transmission accuracy, backlash, and transmission efficiency of the space harmonic reducer were obtained. It is proven by experiments that the performance data of the harmonic reducer in space work can be more accurately obtained by using the test system mentioned in this paper, which is convenient for further research on related lubricating materials.

Colonic endoscopy is the gold standard for detecting rectal polyps and rectal cancer. In which polyps are a major predisposing factor for colorectal cancer, the precise diagnosis of polyps within colorectal endoscopy is highly dependent on a physician of professional level. With the development of deep learning, some semantic segmentation methods have recently been applied to polyp detection, but there are problems with insufficient accuracy and segmentation speed. To this end, we propose a precision adaptive global context network (AGCNet) based on real-time colon endoscopy. Firstly, in order to adapt to the problem of large-scale variation of polyps, we designed a multi-scale semantic fusion module (MSFM), which enhances the representation capability by varieties of filters to collect contextual information at different scales, thus adapting to the problem of large variation of polyp size, especially smaller polyps. In addition, modelling long-range dependence by simply using complex spatial pixels tends to introduce more background noise and increase the computational effort. To this end, a context-aware pyramid aggregation module (CPAM) was designed, which internally includes a novel dual attention mechanism whereby the CPAM aggregates feature information across different regions to boost the network’s ability to utilize global context and model long-range dependency through dual attention to further reinforce the features information of important regions and efficiently suppress features in non-important regions, Thereby achieving precise polyp segmentation and taking real-time into account. Our proposed AGCNet performed extensive experimental studies on datasets Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB and ETIS-LaribPolypDB. Specifically, AGCNet achieved an IoU of 87.40% and a Dice score of 92.63% on the Kvasir dataset, achieving accurate segmentation results faster than many current state-of-the-art models.

Colorectal cancer, also known as rectal cancer, is one of the most common forms of cancer, and it can be completely cured with early diagnosis. The most effective and objective method of screening and diagnosis is colonoscopy. Polyp segmentation plays a crucial role in the diagnosis and treatment of diseases related to the digestive system, providing doctors with detailed auxiliary boundary information during clinical analysis. To this end, we propose a novel light-weight feature refining and context-guided network (FRCNet) for real-time polyp segmentation. In this method, we first employed the enhanced context-calibrated module to extract the most discriminative features by developing long-range spatial dependence through a context-calibrated operation. This operation is helpful to alleviate the interference of background noise and effectively distinguish the target polyps from the background. Furthermore, we designed the progressive context-aware fusion module to dynamically capture multi-scale polyps by collecting multi-range context information. Finally, the multi-scale pyramid aggregation module was used to learn more representative features, and these features were fused to refine the segmented results. Extensive experiments on the Kvasir, ClinicDB, ColonDB, ETIS, and Endoscene datasets demonstrated the effectiveness of the proposed model. Specifically, FRCNet achieves an mIoU of 84.9% and mDice score of 91.5% on the Kvasir dataset with a model size of only 0.78 M parameters, outperforming state-of-the-art methods. Models and codes are available at the footnote.¹

This paper introduces a distributed secondary control algorithm for automatic generation control (AGC) and automatic voltage control (AVC), which aims at matching area generation to area load and minimizing the total generation cost in an alternating current (AC) microgrids. Firstly, the control algorithm utilizes a continuous-time distributed algorithm to generate additional control variables to achieve frequency-voltage recovery for all distributed generators (DGs). Secondary, it solves the economic dispatch problem (EDP) by a distributed economic incremental algorithm in the secondary control level, which avoids the problem caused by communication speed inconsistency between secondary and tertiary control levels. This study also utilizes a fully distributed strategy based on secondary communication network to estimate the total load demand. In addition, the proposed algorithm can be used to realize a seamless handover from the islanded mode to the grid-connected mode, run under the condition of short time communication system out of action, and help to realize the plug and play function. Lastly, the stability of the proposed control algorithm is analyzed and proved, and the effectiveness of the method is verified in some case studies.

Agriculture is pivotal in national economies, with pest classification significantly influencing food quality and quantity. In recent years, pest classification methods based on deep learning have made progress. However, there are two problems with these methods. One is that there are few multi-scale pest detection algorithms, and they often lack effective global information integration and discriminative feature representation. The other is the lack of high-quality agricultural pest datasets, leading to insufficient training samples. To overcome these two limitations, we propose two methods called RS Transformer (a two-stage region proposal using Swin Transformer) and the Randomly Generated Stable Diffusion Dataset (RGSDD). Firstly, we found that the diffusion model can generate high-resolution images, so we developed a training strategy called the RGSDD, which was used to generate agricultural pest images and was mixed with real datasets for training. Secondly, RS Transformer uses Swin Transformer as the backbone to enhance the ability to extract global features, while reducing the computational burden of the previous Transformer. Finally, we added a region proposal network and ROI Align to form a two-stage training mode. The experimental results on the datasets show that RS Transformer has a better performance than the other models do. The RGSDD helps to improve the training accuracy of the model. Compared with methods of the same type, RS Transformer achieves up to 4.62% of improvement.