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    Simplified space vector modulation method for multi-level converter
    1.
    发明授权
    Simplified space vector modulation method for multi-level converter 有权

    公开(公告)号：US11121642B2

    公开(公告)日：2021-09-14

    申请号：US17060183

    申请日：2020-10-01

    申请人： NANCHANG INSTITUTE OF TECHNOLOGY

    发明人： Cui Wang ,  Junming Ouyang ,  Xuan Zeng ,  Xingwang Zhang ,  Zhikai Huang ,  Xiaopin Yang ,  Jianghua Gan ,  Sizhe Chen

    IPC分类号： H02M7/483 ,  H02M7/5387 ,  H02M7/5395

    摘要： The disclosure discloses a simplified space vector modulation method for multi-lever converters, and relates to the field of space vector modulation methods for multilevel converters, which solves problems that a redundant on-off state is greatly increased along with increase of a number of levels in a traditional SVM technology, and SVM is difficult to realize due to calculation of the redundant on-off state and selection of a proper on-off state. The method includes the following steps of: step 1: establishing a vector expression; step 2: establishing a reference vector trajectory model; step 3: respectively representing reference signals and level signals by coordinate components of a reference vector and a basic vector and corresponding component sums; step 4: constructing a star-connected multilevel converter; step 5: sampling a phase voltage reference vector trajectory model of the star-connected multilevel converter, and synthesizing the reference vector.

    METHOD AND SYSTEM FOR TRACKING OBJECT BY AGGREGATION NETWORK BASED ON HYBRID CONVOLUTION AND SELF-ATTENTION
    2.
    发明公开
    METHOD AND SYSTEM FOR TRACKING OBJECT BY AGGREGATION NETWORK BASED ON HYBRID CONVOLUTION AND SELF-ATTENTION 审中-公开

    公开(公告)号：US20240104772A1

    公开(公告)日：2024-03-28

    申请号：US18356315

    申请日：2023-07-21

    申请人： NANCHANG INSTITUTE OF TECHNOLOGY

    发明人： Jun WANG ,  Peng YIN

    IPC分类号： G06T7/73

    CPC分类号： G06T7/74 ,  G06T2207/20012 ,  G06T2207/20081 ,  G06T2207/20084

    摘要： Corresponding template features and search features are obtained by convolution operation, and respectively used as input features in aggregation modules. Intermediate features are obtained by performing convolution operation on the input features. The aggregation modules share the same convolution operation, and hybrid convolution in the aggregation module uses a depthwise convolution and a pointwise convolution to separate mixture between space and channel of the intermediate features. Redundancy in spatial and channel features is reduced while increasing receptive field. Self-attention module in the aggregation module learns intermediate features, and adaptively focuses on different regions to capture more global correlations. Output features of the hybrid convolution are added to output features of the self-attention module to pass through a drop-out layer to obtain final output features. The output features aggregate local and global context information. Overfitting of network is alleviated during training, thereby improving generalization ability of tracker.

    SIMPLIFIED SPACE VECTOR MODULATION METHOD FOR MULTI-LEVEL CONVERTER
    5.
    发明申请
    SIMPLIFIED SPACE VECTOR MODULATION METHOD FOR MULTI-LEVEL CONVERTER 有权

    公开(公告)号：US20210021208A1

    公开(公告)日：2021-01-21

    申请号：US17060183

    申请日：2020-10-01

    申请人： NANCHANG INSTITUTE OF TECHNOLOGY

    发明人： Cui WANG ,  Junming OUYANG ,  Xuan ZENG ,  Xingwang ZHANG ,  Zhikai HUANG ,  Xiaopin YANG ,  Jianghua GAN ,  Sizhe CHEN

    IPC分类号： H02M7/5395 ,  H02M7/5387

    摘要： The disclosure discloses a simplified space vector modulation method for multi-lever converters, and relates to the field of space vector modulation methods for multilevel converters, which solves problems that a redundant on-off state is greatly increased along with increase of a number of levels in a traditional SVM technology, and SVM is difficult to realize due to calculation of the redundant on-off state and selection of a proper on-off state. The method comprises the following steps of: step 1: establishing a vector expression; step 2: establishing a reference vector trajectory model; step 3: respectively representing reference signals and level signals by coordinate components of a reference vector and a basic vector and corresponding component sums; step 4: constructing a star-connected multilevel converter; step 5: sampling a phase voltage reference vector trajectory model of the star-connected multilevel converter, and synthesizing the reference vector.