BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization

Vision Transformer (ViT) based Vision-Language Pre-training (VLP) models have demonstrated impressive performance in various tasks. However, the lengthy visual token sequences fed into ViT can lead to training inefficiency and ineffectiveness. Existing efforts address the challenge by either bottom-level patch extraction in the ViT backbone or top-level patch abstraction outside, not balancing training efficiency and effectiveness well. Inspired by text summarization in natural language processing, we propose a Bottom-Up Patch Summarization approach named BUS, coordinating bottom-level extraction and top-level abstraction to learn a concise summary of lengthy visual token sequences efficiently. Specifically, We incorporate a Text-Semantics-Aware Patch Selector (TSPS) into the ViT backbone to perform a coarse-grained visual token extraction and then attach a flexible Transformer-based Patch Abstraction Decoder (PAD) upon the backbone for top-level visual abstraction. This bottom-up collaboration enables our BUS to yield high training efficiency while maintaining or even improving effectiveness. We evaluate our approach on various visual-language understanding and generation tasks and show competitive downstream task performance while boosting the training efficiency by 50\%. Additionally, our model achieves state-of-the-art performance on many downstream tasks by increasing input image resolution without increasing computational costs over baselines.Comment: Accepted on ICCV202

Wavelet transform based differential protection for mvdc distribution network

A short-circuit fault on the DC side in a MVDC distribution network can cause the rapid drop of inter electrode voltage and the large overcurrent, which will have a serious impact on reliable power supply and cause severe damage to voltage source converters. Requirements of fast and accurate detection and isolation techniques against such DC faults presents a challenge. In this paper, a current differential protection scheme based on the wavelet transform is used to identify the faults. Fault currents are decomposed in order to identify the fault area. A corresponding model is built based on PSCAD/EMTDC, the effectiveness of the proposed protection scheme and the effects of transition resistance and noise are verified, the operation time also meets the requirement of protection scheme for rapidity

Harmonic suppression of charging station based on harmonic superposition

With the rapid development of electric vehicles, the high permeability of electric vehicles brings new challenges to the power grid, one of which is harmonic pollution. This paper first analyzes the harmonic characteristics of the two chargers. During the conventional charging process, the harmonic distortion of the two chargers will increase in the later stage of constant voltage, exceeding the requirement of harmonic grid connection. Then the principle and superposition method of harmonic cancellation are introduced, and the feasibility of harmonic cancellation between chargers is verified by simulation. Finally, through the harmonic counteracting function of different chargers, the ratio of chargers is determined, and the harmonic distortion of constant voltage stage is reduced as far as possible, which provides the reference for the harmonic control and the actual construction of charging station