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Your search keyword '"Hong, Hanyu"' showing total 468 results
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468 results on '"Hong, Hanyu"'

1. Design of a New DCDC Converter with High Efficiency

Author

Zheng, Aoxiang, Tian, Bin, Hong, Hanyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wen, Fushuan, editor, and Aris, Ishak Bin, editor

Published
2024
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2. Dynamic Scene Deblurring Based on Continuous Cross-Layer Attention Transmission

Author

Hua, Xia, Li, Mingxin, Fei, Junxiong, Shi, Yu, Liu, JianGuo, and Hong, Hanyu

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The deep convolutional neural networks (CNNs) using attention mechanism have achieved great success for dynamic scene deblurring. In most of these networks, only the features refined by the attention maps can be passed to the next layer and the attention maps of different layers are separated from each other, which does not make full use of the attention information from different layers in the CNN. To address this problem, we introduce a new continuous cross-layer attention transmission (CCLAT) mechanism that can exploit hierarchical attention information from all the convolutional layers. Based on the CCLAT mechanism, we use a very simple attention module to construct a novel residual dense attention fusion block (RDAFB). In RDAFB, the attention maps inferred from the outputs of the preceding RDAFB and each layer are directly connected to the subsequent ones, leading to a CCLAT mechanism. Taking RDAFB as the building block, we design an effective architecture for dynamic scene deblurring named RDAFNet. The experiments on benchmark datasets show that the proposed model outperforms the state-of-the-art deblurring approaches, and demonstrate the effectiveness of CCLAT mechanism.

Published
2022

7. Unsupervised Encoder-Decoder Model for Anomaly Prediction Task

Author

Wu, Jinmeng, Shu, Pengcheng, Hong, Hanyu, Li, Xingxun, Ma, Lei, Zhang, Yaozong, Zhu, Ying, Wang, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dang-Nguyen, Duc-Tien, editor, Gurrin, Cathal, editor, Larson, Martha, editor, Smeaton, Alan F., editor, Rudinac, Stevan, editor, Dao, Minh-Son, editor, Trattner, Christoph, editor, and Chen, Phoebe, editor

Published
2023
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16. Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network.

Author

Wang, Lei, Peng, Lingmu, Gui, Rong, Hong, Hanyu, and Zhu, Shenghui

Subjects
IMAGE recognition (Computer vision), CENTROID, CLASSIFICATION, SYNTHETIC aperture radar
Abstract

Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain. Therefore, many unsupervised methods have been proposed for unsupervised PolSAR image classification. The classification maps of unsupervised methods contain many high-confidence samples. These samples, which are often ignored, can be used as supervisory information to improve classification performance on PolSAR images. This study proposes a new unsupervised PolSAR image classification framework. The framework combines high-confidence superpixel pseudo-labeled samples and semi-supervised classification methods. The experiments indicated that this framework could achieve higher-level effectiveness in unsupervised PolSAR image classification. First, superpixel segmentation was performed on PolSAR images, and the geometric centers of the superpixels were generated. Second, the classification maps of rotation-domain deep mutual information (RDDMI), an unsupervised PolSAR image classification method, were used as the pseudo-labels of the central points of the superpixels. Finally, the unlabeled samples and the high-confidence pseudo-labeled samples were used to train an excellent semi-supervised method, similarity matching (SimMatch). Experiments on three real PolSAR datasets illustrated that, compared with the excellent RDDMI, the accuracy of the proposed method was increased by 1.70%, 0.99%, and 0.8%. The proposed framework provides significant performance improvements and is an efficient method for improving unsupervised PolSAR image classification. [ABSTRACT FROM AUTHOR]

Published
2024
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24. High-Precision Grating Projection 3-D Measurement Method Based on Multifrequency Phases for Printed Circuit Board Inspection

Author

Hong, Hanyu, Zhou, Minghao, Zhu, Ying, Zhang, Xiuhua, Xi, Wenyi, and Chen, Ling

Abstract

The grating projection 3-D measurement method based on multifrequency heterodyne (MFH) can achieve the measurement of complex objects by obtaining absolute phase through differential phase. However, in the application of printed circuit board (PCB) inspection, the differential process amplifies phase errors introduced by factors, such as materials and environment, consequently significantly diminishing the measurement accuracy and posing serious hardware safety hazards. To avoid the impact of phase errors, this article proposes a high-precision grating projection 3-D measurement method based on multifrequency phases for PCB inspection. In this method, the high-frequency phase synthesis phase patterns are directly project onto the object to bypass the phase differential process to eliminate the influence of heterodynes. Then, phase errors are compensated by using the relationship between phases and phase errors to minimize the impact of noises on measurement. The results from measurement experiments of components on PCB show that the measurement errors obtained by this method are reduced to less than 0.03 mm and improved accuracy by at least twice compared to other method (0.06–0.09 mm).

Published
2024
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27. Nonuniformity Correction Method of Thermal Radiation Effects in Infrared Images

Author

Hong, Hanyu, Shi, Yu, Zhang, Tianxu, Liu, Zhao, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
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