Electrical bearing failures in electric vehicles

In modern electric equipment, especially electric vehicles, inverter control systems can lead to complex shaft voltages and bearing currents. Within an electric motor, many parts have electrical failure problems, and among which bearings are the most sensitive and vulnerable components. In recent years, electrical failures in bearing have been frequently reported in electric vehicles, and the electrical failure of bearings has become a key issue that restricts the lifetime of all-electric motor-based power systems in a broader sense. The purpose of this review is to provide a comprehensive overview of the bearing premature failure in the mechanical systems exposed in an electrical environment represented by electric vehicles. The electrical environments in which bearing works including the different components and the origins of the shaft voltages and bearing currents, as well as the typical modes of electrical bearing failure including various topographical damages and lubrication failures, have been discussed. The fundamental influence mechanisms of voltage/current on the friction/lubrication properties have been summarized and analyzed, and corresponding countermeasures have been proposed. Finally, a brief introduction to the key technical flaws in the current researches will be made and the future outlook of frontier directions will be discussed. Document type: Articl

G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System

Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in the delivery address, which considerably trims down the performance of delivery-related tasks in logistic systems such as Cainiao. To tackle the above problem, we propose a domain-specific pre-trained model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in Logistics field. G2PTL combines the semantic learning capabilities of text pre-training with the geographical-relationship encoding abilities of graph modeling. Specifically, we first utilize real-world logistics delivery data to construct a large-scale heterogeneous graph of delivery addresses, which contains abundant geographic knowledge and delivery information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive experiments are conducted to demonstrate the effectiveness of G2PTL through four downstream tasks in logistics systems on real-world datasets. G2PTL has been deployed in production in Cainiao's logistics system, which significantly improves the performance of delivery-related tasks

Non-invasive color imaging through scattering medium under broadband illumination

Due to the complex of mixed spectral point spread function within memory effect range, it is unreliable and slow to use speckle correlation technology for non-invasive imaging through scattering medium under broadband illumination. The contrast of the speckles will drastically drop as the light source's spectrum width increases. Here, we propose a method for producing the optical transfer function with several speckle frames within memory effect range to image under broadband illumination. The method can be applied to image amplitude and color objects under white LED illumination. Compared to other approaches of imaging under broadband illumination, such as deep learning and modified phase retrieval, our method can provide more stable results with faster convergence speed, which can be applied in high speed scattering imaging under natural light illumination