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Chenglin Wen

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2 papers

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EAAI Journal 2023 Journal Article

A dynamic graph aggregation framework for 3D point cloud registration

Feilong Cao
Jiatong Shi
Chenglin Wen

Currently, most of the existing point cloud registration methods use stacked edge convolution as feature extractor, ignoring the importance of deep semantic information, meanwhile, the use of attention mechanism tends to increase the computational cost of the model and limits the matching effect. This paper proposes a dynamic graph aggregation framework for point cloud registration by building a dynamic deep network, which can capture richer semantic information and shape properties of point clouds. First, a hybrid feature extractor, which fully fuses local graph information and global graph information, is designed to obtain more discriminative feature descriptors. Second, a local graph neighborhood scoring module to update local graph features and achieve feature enhancement is built by learning the difference of similar point neighborhood features. Finally, a dual-constrained matching module is designed, which is used to measure the similarity of point pairs from the two aspects of Euclidean distance and affinity between features. The proposed framework achieves excellent registration performance, as demonstrated by experimental results on challenging 3D point cloud benchmarks. Especially in partial point cloud registration, MAE( R ) achieved excellent results of 0. 2614 and 0. 2619 under unseen shapes and unseen categories.

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EAAI Journal 2023 Journal Article

Attention gate guided multiscale recursive fusion strategy for deep neural network-based fault diagnosis

Zhiqiang Zhang
Funa Zhou
Hamid Reza Karimi
Hamido Fujita
Xiong Hu
Chenglin Wen
Tianzhen Wang

Rolling bearings are crucial for ensuring the safe and stable operation of electromechanical systems. Although deep learning has been widely used in fault diagnosis of rolling bearings, it is unable to accurately diagnose faults when the system operates under multiple working conditions. Therefore, it is essential to conduct research on fault diagnosis of rolling bearings under multiple working conditions to ensure the reliable operation of electromechanical systems. The potential features related to working conditions may be reflected in the different layers of the deep neural network (DNN). However, information loss during the process of layer-by-layer feature extraction may result in the loss of potential features related to changes in working conditions, which in turn affects the fault diagnosis results. This study focused on developing a multiscale recursive fusion strategy for a DNN by designing a new attention model with a lower computational burden. The proposed multiscale recursive fusion strategy guided by the attention mechanism can help correctly characterize the potential features related to variations in working conditions by allocating more attention to useful information and less attention to useless information on the adjacent layers of the DNN. Experimental tests for fault diagnosis of rolling bearings verified that the proposed method is superior to existing methods for fault diagnosis when the system is operated under multiple working conditions.

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