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Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks
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作者 Jinxi Guo Kai Chen +5 位作者 Jiehui Liu Yuhao Ma Jie Wu Yaochun Wu Xiaofeng Xue Jianshen Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2619-2640,共22页
Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received in... Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels. 展开更多
关键词 Fault diagnosis transfer learning domain adaptation discriminative feature learning correlation alignment
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Learning a Discriminative Feature Attention Network for pancreas CT segmentation
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作者 HUANG Mei-xiang WANG Yuan-jin +2 位作者 HUANG Chong-fei YUAN Jing KONG De-xing 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2022年第1期73-90,共18页
Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In... Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However,cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2 D pancreas segmentation. We obtained average Dice Similarity Coefficient(DSC) of 82.82±6.09%, average Jaccard Index(JI) of 71.13± 8.30% and average Symmetric Average Surface Distance(ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value. 展开更多
关键词 attention mechanism Discriminative feature Attention Network Improved Refinement Residual Block pancreas CT segmentation
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Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern 被引量:1
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作者 Qicong WANG Binbin WANG +4 位作者 Xinjie HAO Lisheng CHEN Jingmin CUI Rongrong JI Yunqi LEI 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第6期1118-1129,共12页
To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting ... To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms. 展开更多
关键词 face recognition global feature local feature linear discriminant analysis local binary pattern decision fusion
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Learn Robust Pedestrian Representation Within Minimal Modality Discrepancy for Visible-Infrared Person Re-Identification
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作者 Yu-Jie Liu Wen-Bin Shao Xiao-Rui Sun 《Journal of Computer Science & Technology》 SCIE EI CSCD 2022年第3期641-651,共11页
Visible-infrared person re-identification has attracted extensive attention from the community due to its potential great application prospects in video surveillance.There are huge modality discrepancies between visib... Visible-infrared person re-identification has attracted extensive attention from the community due to its potential great application prospects in video surveillance.There are huge modality discrepancies between visible and infrared images caused by different imaging mechanisms.Existing studies alleviate modality discrepancies by aligning modality distribution or extracting modality-shared features on the original image.However,they ignore a key solution,i.e.,converting visible images to gray images directly,which is efficient and effective to reduce modality discrepancies.In this paper,we transform the cross-modality person re-identification task from visible-infrared images to gray-infrared images,which is named as the minimal modality discrepancy.In addition,we propose a pyramid feature integration network(PFINet)which mines the discriminative refined features of pedestrian images and fuses high-level and semantically strong features to build a robust pedestrian representation.Specifically,PFINet first performs the feature extraction from concrete to abstract and the top-down semantic transfer to obtain multi-scale feature maps.Second,the multi-scale feature maps are inputted to the discriminative-region response module to emphasize the identity-discriminative regions by the spatial attention mechanism.Finally,the pedestrian representation is obtained by the feature integration.Extensive experiments demonstrate the effectiveness of PFINet which achieves the rank-1 accuracy of 81.95%and mAP of 74.49%on the multi-all evaluation mode of the SYSU-MM01 dataset. 展开更多
关键词 person re-identification modality discrepancy discriminative feature
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