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学者姓名：陈开志
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Currently, fabric image retrieval faces challenges such as the high cost of image annotation and its vulnerability to adversarial perturbations. To minimize manual supervision and enhance the robustness of the retrieval system, this study proposes a robust deep image retrieval algorithm using multi-view self-supervised product quantization for artificially generated fabric images. The method introduces a multi-view module, which includes two views enhanced by AutoAugment, an adversarial view and a high-frequency view of the unlabeled images. AutoAugment can generate more varied data variations, which allows the model to learn more about the different features and structures of the fabric texture; fabric images are usually of high complexity and diversity, and adding the adversarial sample into the model training can add more noise and variations, which is one of the best existing ways to defend against adversarial attacks; the high-frequency component can make the edges, details, and contrasts in the fabric image clearer. A robust cross quantized contrastive loss function is also designed to jointly learn codewords and deep visual descriptors by comparing multiple views, effectively increasing the model’s robustness and generalization. The method's effectiveness is demonstrated by experimental results on multiple datasets, which can significantly improve the robustness of the retrieval system compared to other state-of-the-art retrieval algorithms. Our method presents a new approach for fabric image retrieval and has great significance for improving its performance. © 2024 SPIE. All rights reserved.
Keyword ：
Image compression Image compression Image enhancement Image enhancement Image retrieval Image retrieval Search engines Search engines Textures Textures
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| GB/T 7714 | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval [C] . 2024 . |
| MLA | Zhuo, Yudan 等. "Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval" . (2024) . |
| APA | Zhuo, Yudan , Zhong, Shangping , Chen, Kaizhi . Multi-view Self-supervised Product Quantization for Robust Fabric Image Retrieval . (2024) . |
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Lace texture, as a manually designed texture image, needs to possess a series of essential aesthetic characteristics, such as periodicity, symmetry, and blank-leaving in artistic design creation. It requires human designers to spend a lot of time and effort, so it is necessary to apply generative models to generate lace images. In image generation tasks, compared to models such as DCGAN, CycleGAN, and ProGAN, although images generated using StyleGAN2 perform well in terms of resolution, texture details, and periodicity, they still perform poorly in terms of symmetry in lace images. To address the above issues, this article proposes an improved model SStyleGAN (Symmetry StyleGAN) based on StyleGAN2. In terms of discriminators, in order to enhance the attention of the proposed model to image symmetry, we have added a symmetry discriminator, that is, SStyleGAN adopts a dual discriminator structure; In terms of generator, in order to improve the similarity of the feature maps on the left and right sides of the lace image, this paper adds a mean square error loss term based on the loss function of StyleGAN2; In terms of noise input, in order to control the symmetry of the lace image at details such as lace edges, the noise of the StyleGAN2 model is modified to a symmetrical structure, so that the noise input itself has symmetry. In addition to the commonly used FID (Fréchet Insertion Distance) in the generative model, we also used the SSIM (Structural Similarity) metric for the evaluation of the experimental results in this article to detect the symmetry of the generated images. The experimental results show that compared to the lace images generated by the StyleGAN2 model, the lace images generated by the model proposed in this paper not only inherit the advantages of the former, but also have symmetry characteristics. © 2024 SPIE.
Keyword ：
Discriminators Discriminators Generative adversarial networks Generative adversarial networks Image enhancement Image enhancement Image texture Image texture Mean square error Mean square error Textures Textures
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| GB/T 7714 | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images [C] . 2024 . |
| MLA | Li, Jian 等. "SStyleGAN: a StyleGAN model for generating symmetrical lace images" . (2024) . |
| APA | Li, Jian , Zhong, Shangping , Chen, Kaizhi . SStyleGAN: a StyleGAN model for generating symmetrical lace images . (2024) . |
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To cope with the threat of image content tampering in real scenes, this paper develops a multi-view spatial-channel attention network (MSCA-Net), which can use multi-view features and multi-scale features to detect whether an image has been tampered with and predict tampered regions. By introducing the frequency domain view of the image, the model can use the noise distribution around the tampered region to learn semantically independent features and detect subtle tampering traces that are difficult to detect in the RGB domain. Secondly, a new Efficient Spatial-Channel Attention Module (ESCM) is proposed to capture the correlation between different channels and between global pixels. MSCA-Net improves the localization performance of tampered regions on real-scene images by generating segmentation masks step by step at multiple scales through a progressive guidance mechanism. MSCA-Net runs very fast and is capable of processing 1080P resolution images at 40FPS+. Extensive experimental results demonstrate the promising performance of MSCA-Net on both image-level and pixel-level tampering detection tasks. © 2024 SPIE. All rights reserved.
Keyword ：
Behavioral research Behavioral research Feature extraction Feature extraction Frequency domain analysis Frequency domain analysis Image enhancement Image enhancement Image segmentation Image segmentation Pixels Pixels
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| GB/T 7714 | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention [C] . 2024 . |
| MLA | Liu, Hanquan 等. "Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention" . (2024) . |
| APA | Liu, Hanquan , Zhong, Shangping , Chen, Kaizhi . Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention . (2024) . |
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Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.
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| GB/T 7714 | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM [J]. | PLOS ONE , 2024 , 19 (3) . |
| MLA | Yang, Shaojun 等. "W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM" . | PLOS ONE 19 . 3 (2024) . |
| APA | Yang, Shaojun , Zhong, Shangping , Chen, Kaizhi . W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM . | PLOS ONE , 2024 , 19 (3) . |
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Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Benchmarking Benchmarking Supervised learning Supervised learning
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| GB/T 7714 | Zheng, Guolin , Li, Zuoyong , Hu, Wenkai et al. Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization [C] . 2023 : 144-154 . |
| MLA | Zheng, Guolin et al. "Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization" . (2023) : 144-154 . |
| APA | Zheng, Guolin , Li, Zuoyong , Hu, Wenkai , Fan, Haoyi , Ching, Fum Yew , Yu, Zhaochai et al. Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization . (2023) : 144-154 . |
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| GB/T 7714 | Zhu, Hansong , Chen, Si , Lu, Wen et al. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm (vol 22, 2335, 2022) [J]. | BMC PUBLIC HEALTH , 2023 , 23 (1) . |
| MLA | Zhu, Hansong et al. "Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm (vol 22, 2335, 2022)" . | BMC PUBLIC HEALTH 23 . 1 (2023) . |
| APA | Zhu, Hansong , Chen, Si , Lu, Wen , Chen, Kaizhi , Feng, Yulin , Xie, Zhonghang et al. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm (vol 22, 2335, 2022) . | BMC PUBLIC HEALTH , 2023 , 23 (1) . |
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BackgroundThis study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.MethodA distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.ResultsOverall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (>= 21 hPa) daily air pressure difference (PRSD) and low (< 7 degrees C) and high (> 12 degrees C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.ConclusionThis study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.
Keyword ：
Air temperature Air temperature DLNM DLNM HFMD HFMD LSTM LSTM Meteorological Meteorological Relative humidity Relative humidity
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| GB/T 7714 | Zhu, Hansong , Chen, Si , Liang, Rui et al. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China [J]. | BMC INFECTIOUS DISEASES , 2023 , 23 (1) . |
| MLA | Zhu, Hansong et al. "Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China" . | BMC INFECTIOUS DISEASES 23 . 1 (2023) . |
| APA | Zhu, Hansong , Chen, Si , Liang, Rui , Feng, Yulin , Joldosh, Aynur , Xie, Zhonghang et al. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China . | BMC INFECTIOUS DISEASES , 2023 , 23 (1) . |
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Machine learning has become an influential and effective tool in numerous civil engineering applications, especially in the field of structural health monitoring (SHM). Recently, the emergence of self-supervised learning has led to the development of many industries, and its accuracy and stability are superior to previous methods. Self-supervised learning learns generalizable information representation from unlabeled mixed data by solving pretext tasks, and this feature is exactly in line with the mixed and unlabeled data in the SHM field. This is of great significance to the improvement of detection accuracy in SHM practical applications. Therefore, this paper proposes a new self-supervised method for structural damage detection. The key to this method is that we use two self-supervised pretext tasks to learn the latent feature representation of the data, and we introduce homoscedastic uncertainty for automatically assigning weights to the two pretext tasks. The relative confidence between tasks is captured, the impact of noise on tasks is reduced, the pretext tasks can better learn data feature representation, and the purpose of improving the accuracy of damage detection is achieved. © 2023 SPIE.
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| GB/T 7714 | He, W. , Chen, K. , Zhong, S. . Self-supervised structural damage detection method based on pretext tasks learning feature representations [未知]. |
| MLA | He, W. et al. "Self-supervised structural damage detection method based on pretext tasks learning feature representations" [未知]. |
| APA | He, W. , Chen, K. , Zhong, S. . Self-supervised structural damage detection method based on pretext tasks learning feature representations [未知]. |
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超声检查具有无辐射、无创、低成本、高效的优点，是最常用的肝脏影像学检查方法。计算机视觉技术应用于超声图像智能分析已成为智慧医疗领域的研究热点。通过大规模数据训练，构建基于机器学习算法的超声组学智能分析模型，可辅助临床诊断与治疗，提高诊断的效率和准确性。笔者结合文献，评述计算机视觉技术辅助超声检查在评估肝脏弥漫性病变、肝脏局灶性病变、肝癌微血管侵犯、肝癌术后复发及肝动脉化疗栓塞术后治疗反应等方面的应用前景。
Keyword ：
人工智能 人工智能 影像学检查 影像学检查 肝疾病 肝疾病 计算机视觉技术 计算机视觉技术 超声 超声
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| GB/T 7714 | 方国旭 , 谢文婷 , 陈开志 et al. 计算机视觉技术辅助超声检查在肝脏疾病诊治中的应用前景 [J]. | 中华消化外科杂志 , 2023 , 22 (4) : 462-467 . |
| MLA | 方国旭 et al. "计算机视觉技术辅助超声检查在肝脏疾病诊治中的应用前景" . | 中华消化外科杂志 22 . 4 (2023) : 462-467 . |
| APA | 方国旭 , 谢文婷 , 陈开志 , 陈斯琦 , 陈敏泳 , 廖祥文 et al. 计算机视觉技术辅助超声检查在肝脏疾病诊治中的应用前景 . | 中华消化外科杂志 , 2023 , 22 (4) , 462-467 . |
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Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph representation learning and graph network models. Most GNNs work only in a single domain and cannot transfer the learned knowledge to other domains. Coronary Heart Disease (CHD) is a high-mortality disease, and there are non-public and significant differences in CHD datasets for current research, which makes it difficult to perform unified transfer learning. Therefore, in this paper, we propose a novel adversarial domain-adaptive multichannel graph convolutional network (DAMGCN) that can perform graph transfer learning on cross-domain tasks to achieve cross-domain medical knowledge transfer on different CHD datasets. First, we use a two-channel GCN model for feature aggregation using local consistency and global consistency. Then, a uniform node representation is generated for different graphs using an attention mechanism. Finally, we provide a domain adversarial module to decrease the discrepancies between the source and target domain classifiers and optimize the three loss functions in order to accomplish source and target domain knowledge transfer. The experimental findings demonstrate that our model performs best on three CHD datasets, and its performance is greatly enhanced by graph transfer learning.
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| GB/T 7714 | Lin, Huizhong , Chen, Kaizhi , Xue, Yutao et al. Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN) [J]. | SCIENTIFIC REPORTS , 2023 , 13 (1) . |
| MLA | Lin, Huizhong et al. "Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN)" . | SCIENTIFIC REPORTS 13 . 1 (2023) . |
| APA | Lin, Huizhong , Chen, Kaizhi , Xue, Yutao , Zhong, Shangping , Chen, Lianglong , Ye, Mingfang . Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN) . | SCIENTIFIC REPORTS , 2023 , 13 (1) . |
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