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Your search keyword '"Jiang, Yonghua"' showing total 539 results
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539 results on '"Jiang, Yonghua"'

1. Bulk-edge correspondence for the nonlinear eigenvalues problem of the Haldane model

Author

Cheng, Shujie, Jiang, Yonghua, and Xianlong, Gao

Subjects
Condensed Matter - Disordered Systems and Neural Networks
Abstract

Recently, there is an interest in studying the bulk-edge correspondence for nonlinear eigenvalues problems in a two-dimensional topological system with spin-orbit coupling. By introducing auxiliary eigenvalues, the nonlinear bulk-edge correspondence was established. In this paper, taking the Haldane model as an example, we address that such a correspondence will appear in two dimensional topological system without spin-orbit coupling. The resulting edge states are characterized by the Chern number of the auxiliary energy band. A full phase diagram containing topological nontrivial phase, topological trivial phase, and metallic phase is obtained. Our work generalizes the study of the bulk-edge correspondence for nonlinear eigenvalue problems in two-dimensional system., Comment: 5 pages, 4 figures

Published
2023

9. A New Strategy for Mental Fatigue Detection Based on Deep Learning and Respiratory Signal

Author

Wang, Jie, Shi, Jilong, Xu, Yanting, Zhong, Hongyang, Li, Gang, Tian, Jinghong, Xu, Wanxiu, Gao, Zhao, Jiang, Yonghua, Jiao, Weidong, Tang, Chao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Liu, Qi, editor, Liu, Xiaodong, editor, Chen, Bo, editor, Zhang, Yiming, editor, and Peng, Jiansheng, editor

Published
2022
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21. The reference genome and transcriptome of the limestone langur, Trachypithecus leucocephalus, reveal expansion of genes related to alkali tolerance

Author

Que, Tengcheng, Wang, Huifeng, Yang, Weifei, Wu, Jianbao, Hou, Chenyang, Pei, Surui, Wu, Qunying, Li, Liu Ming, Wei, Shilu, Xie, Xing, Huang, Hongli, Chen, Panyu, Huang, Yiming, Wu, Aiqiong, He, Meihong, Nong, Dengpan, Wei, Xiao, Wu, Junyi, Nong, Ru, Huang, Ning, Zhou, Qingniao, Lin, Yaowang, Lu, Tingxi, Wei, Yongjie, Li, Shousheng, Yao, Jianglong, Zhong, Yanli, Qin, Huayong, Tan, Luohao, Li, Yingjiao, Li, Weidong, Liu, Tao, Liu, Sanyang, Yu, Yongyi, Qiu, Hong, Jiang, Yonghua, Li, Youcheng, Liu, Zhijin, Huang, Cheng Ming, and Hu, Yanling

Published
2021
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29. Adaptive Dynamic Threshold Graph Neural Network: A Novel Deep Learning Framework for Cross-Condition Bearing Fault Diagnosis.

Author

Zheng, Linjie, Jiang, Yonghua, Jiang, Hongkui, Tang, Chao, Jiao, Weidong, Shi, Zhuoqi, and Rehman, Attiq Ur

Subjects
DEEP learning, FAULT diagnosis, DIAGNOSIS methods
Abstract

Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on the amplitude information of samples, neglecting the rich relational information between samples. To address these issues, this paper proposes a novel cross-condition few-shot fault diagnosis method based on an adaptive dynamic threshold graph neural network (ADTGNN). The aim of the proposed method is to rapidly identify fault types after they occur only a few times or even once. The adaptive threshold computation module (ATCM) in ADTGNN dynamically assigns thresholds to each edge based on edge confidence, optimizing the graph structure and effectively alleviating the over-smoothing issue. Furthermore, a dynamic threshold adjustment strategy (DTAS) is introduced to gradually increase the threshold with the training iterations, preventing the model from prematurely discarding crucial edges due to insufficient performance. The proposed model's effectiveness is demonstrated using three bearing datasets. The experimental results indicate that the proposed approach significantly outperforms other comparison methods in cross-condition bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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30. An improved method for signal de‐noising based on multi‐level local mean decomposition.

Author

Tang, Chao, Chen, Heng, Jiang, Yonghua, Jiao, Weidong, Sun, Jianfeng, Xu, Cui, Wang, Chen, and Xia, Haicheng

Subjects
HILBERT-Huang transform, OUTLIER detection, NOISE control, WAVELET transforms, ROLLER bearings
Abstract

The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy‐concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)‐based and empirical mode decomposition (EMD)‐based de‐noising methods is no longer applicable. To address this issue, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection, and waveform smoothing (OD‐WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well‐known methods like WT‐based and EMD‐based de‐noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Associations of circulation levels of cytokines with birthweight, preterm birth, spontaneous miscarriages, and stillbirth: A Mendelian randomization analysis

Author

Wang, Honghong, primary, Jiang, Jinghang, additional, Jin, Tingwei, additional, Wang, Yifu, additional, Li, Mingli, additional, Huang, Shengzhu, additional, Xie, Juanjuan, additional, Chen, Zhongyuan, additional, Guo, Yi, additional, Zheng, Jie, additional, Jiang, Yonghua, additional, and Mo, Zengnan, additional

Published
2023
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41. A General Deep Learning Framework Guided by Sparse Matching for Disparity Estimation in High-Resolution Satellite Stereo Imagery

Author

Zhang, Guangbin, Jiang, Yonghua, Wang, Jingyin, Wang, Yunming, Wei, Shaodong, Du, Bin, and Tan, Meilin

Abstract

In the field of photogrammetry and remote sensing, the task of satellite stereo image disparity estimation (SSIDE) has long been recognized as both challenging and important. Currently, deep-learning methods are gaining prominence in the SSIDE domain. However, the inconsistency between stereo images and ground truth makes the fine training and accurate inference of SSIDE networks extremely difficult. Furthermore, the existence of textureless and repeated texture areas in satellite images complicates the execution of end-to-end SSIDE networks, especially in areas with variable illumination conditions. In this study, a sparse matching point-guided disparity estimation (SMP-DE) general framework was introduced to address such concerns. SMP-DE employed sparse matching point-guided data evaluation and distillation (SMP-DED) for fault-tolerant training and ensuring unbiased guidance training as well as reliable reasoning. In addition, SMP-DE executed optimization for the disparity estimation network across various feature spaces by integrating sparse matching point-guided feature contrastive registration (SMP-FCR) and matching cost uniqueness constraint (MCUC) modules. Therefore, SMP-DE can mine homogenous features and model low-entropy matching costs in challenging regions. Experimental results demonstrated that SMP-DE has outstanding disparity estimation accuracy and generalization compared with other advanced methods. Furthermore, the proposed SMP-DED exhibited excellent flexibility and generality, since it can be combined with various disparity estimating networks, giving the networks an accuracy boost on a range of datasets. In summary, SMP-DE provides a novel perspective for end-to-end SSIDE research.

Published
2024
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42. Building Height Extraction Based on Joint Optimal Selection of Regions and Multiindex Evaluation Mechanism

Author

Chang, Jingxin, Jiang, Yonghua, Li, Ji, Tan, Meilin, Wang, Yunming, and Wei, Shaodong

Abstract

Building height is one of the important data for understanding urban development and changes. Building height estimation using a digital surface model (DSM) based on the difference between the roof elevation and the ground elevation of the building is commonly utilized. However, owing to the limitations of existing DSM techniques, invalid values may exist in the DSM. Existing DSM-based methods for estimating building heights typically use the interpolated DSM; however, when there are many invalid values, there may be errors in the interpolation results, which can mislead the selection of ground elevation values. Therefore, we propose a building-height extraction method that combines an optimal selection region and a multiindex evaluation mechanism to reduce the impact of invalid values and complex terrains. First, the optimal area for the ground elevation search was obtained based on the spatial relationship between the target building and surrounding buildings. Second, a joint multiindicator weighted evaluation mechanism was used to obtain the optimal ground elevation value. Finally, the building height was determined based on the difference between the roof and the ground elevations. Four build-up areas were used to test the effectiveness of the proposed method. The results exhibit high accuracy in complex areas with variable ground elevations, with a mean absolute error (MAE) of 1.16 m in building height. In areas with many invalid values and large shadow coverage of the surface areas, the MAE in building height is 0.92 m. Additionally, we verified the accuracy of the ground elevation estimated after interpolation. It is evident that the performance of the original DSM is satisfactory, with a high tolerance for input data and ability to be used in different building scenarios, providing new ideas for studying building height estimates.

Published
2024
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45. A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men

Author

Hu, Yanling, Tan, Aihua, Yu, Lei, Hou, Chenyang, Kuang, Haofa, Wu, Qunying, Su, Jinghan, Zhou, Qingniao, Zhu, Yuanyuan, Zhang, Chenqi, Wei, Wei, Li, Lianfeng, Li, Weidong, Huang, Yuanjie, Huang, Hongli, Xie, Xing, Lu, Tingxi, Zhang, Haiying, Yang, Xiaobo, Gao, Yong, Li, Tianyu, Jiang, Yonghua, and Mo, Zengnan

Published
2019
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