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学者姓名：王美清
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Abstract ：
This paper utilizes a coupled model of the lattice Boltzmann method and discrete element method (LBM-DEM) to simulate the flow field and particle motion in a three-dimensional porous medium. It investigates the impact of different microstructural characteristics of filter media on filtration performance under the same porosity conditions. The model not only considers the mechanisms of inertial deposition and electrostatic forces but also incorporates the effects of Brownian diffusion and dielectrophoresis, providing a preliminary comparison of various filtration mechanisms. The research results indicate that: (1) under identical porosity conditions, the filtration efficiency of unidirectional interlaced fibers significantly surpasses that of other fiber models with different microstructures; (2) the model integrating four filtration mechanisms demonstrates higher filtration efficiency. © Published under licence by IOP Publishing Ltd.
Keyword ：
Computational fluid dynamics Computational fluid dynamics High modulus textile fibers High modulus textile fibers Hydraulics Hydraulics Infiltration Infiltration Kinetic theory Kinetic theory Microfiltration Microfiltration Micropores Micropores Porosity Porosity Porous materials Porous materials
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| GB/T 7714 | Chen, Xueyan , Wang, Jingyue , Wang, Meiqing . Numerical simulation of different microstructures on fiber filtration efficiency [C] . 2025 . |
| MLA | Chen, Xueyan 等. "Numerical simulation of different microstructures on fiber filtration efficiency" . (2025) . |
| APA | Chen, Xueyan , Wang, Jingyue , Wang, Meiqing . Numerical simulation of different microstructures on fiber filtration efficiency . (2025) . |
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Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.
Keyword ：
attention mechanism attention mechanism Attention mechanisms Attention mechanisms convolutional neural network convolutional neural network Discrete wavelet transforms Discrete wavelet transforms Feature extraction Feature extraction feature fusion feature fusion Image edge detection Image edge detection Image manipulation localization Image manipulation localization Location awareness Location awareness Neural networks Neural networks Noise Noise Semantics Semantics Splicing Splicing Transformers Transformers
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| GB/T 7714 | Chen, Yun , Cheng, Hang , Wang, Haichou et al. EAN: Edge-Aware Network for Image Manipulation Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) : 1591-1601 . |
| MLA | Chen, Yun et al. "EAN: Edge-Aware Network for Image Manipulation Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 2 (2025) : 1591-1601 . |
| APA | Chen, Yun , Cheng, Hang , Wang, Haichou , Liu, Ximeng , Chen, Fei , Li, Fengyong et al. EAN: Edge-Aware Network for Image Manipulation Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) , 1591-1601 . |
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Existing vision-language pre-training models typically extract region features and conduct fine-grained local alignment based on masked image/text completion or object detection methods. However, these models often design independent subtasks for different modalities, which may not adequately leverage interactions between modalities, requiring large datasets to achieve optimal performance. To address these limitations, this paper introduces a novel pre-training approach that facilitates fine-grained vision-language interaction. We propose two new subtasks — image filling and text filling — that utilize data from one modality to complete missing parts in another, enhancing the model's ability to integrate multi-modal information. A selector mechanism is also developed to minimize semantic overlap between modalities, thereby improving the efficiency and effectiveness of the pre-trained model. Our comprehensive experimental results demonstrate that our approach not only fosters better semantic associations among different modalities but also achieves state-of-the-art performance on downstream vision-language tasks with significantly smaller datasets. © 2024 Elsevier Ltd
Keyword ：
Cross-modal Cross-modal Image captioning Image captioning Partial auxiliary Partial auxiliary Pre-training Pre-training
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| GB/T 7714 | Cheng, H. , Ye, H. , Zhou, X. et al. Vision-language pre-training via modal interaction [J]. | Pattern Recognition , 2024 , 156 . |
| MLA | Cheng, H. et al. "Vision-language pre-training via modal interaction" . | Pattern Recognition 156 (2024) . |
| APA | Cheng, H. , Ye, H. , Zhou, X. , Liu, X. , Chen, F. , Wang, M. . Vision-language pre-training via modal interaction . | Pattern Recognition , 2024 , 156 . |
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Federated Learning, as a typical paradigm of collaborative learning, effectively mediates the contradictions between model training and data privacy. However, the data heterogeneity and privacy leakage problems undermine the availability of federated learning. Existing studies focus solely on one of the problems. In this work, we manage to handle these two challenges simultaneously, by utilizing contrastive learning and differential privacy in local training, the federated learning system can stay safe and robust. The contrastive loss item pushes the local model towards the global model and a random noise generated by differential privacy is added to the model to protect the sensitive information hidden in the models. Extensive experiment results demonstrate that our method can exceed the baseline around 1%2% in term of test accuracy. © 2024 IEEE.
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| GB/T 7714 | Liu, P. , Xu, Y. , Wang, M. et al. Model Level Contrastive Federated Learning with Differential Privacy [未知]. |
| MLA | Liu, P. et al. "Model Level Contrastive Federated Learning with Differential Privacy" [未知]. |
| APA | Liu, P. , Xu, Y. , Wang, M. , Cao, P. . Model Level Contrastive Federated Learning with Differential Privacy [未知]. |
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由于细粒度图像之间存在小的类间方差和大的类内差异,现有分类算法仅仅聚焦于单张图像的显著局部特征的提取与表示学习,忽视了多张图像之间局部的异构语义判别信息,较难关注到区分不同类别的微小细节,导致学习到的特征缺乏足够区分度.本文提出了一种渐进式网络以弱监督的方式学习图像不同粒度层级的信息.首先,构建一个注意力累计目标定位模块(Attention accumulation object localization module, AAOLM),在单张图像上从不同的训练轮次和特征提取阶段对注意力信息进行语义目标集成定位.其次,设计一个多张图像异构局部交互图模块(Heterogeneous local interactive graph module, HLIGM),提取每张图像的显著性局部区域特征,在类别标签引导下构建多张图像的局部区域特征之间的图网络,聚合局部特征增强表示的判别力.最后,利用知识蒸馏将异构局部交互图模块产生的优化信息反馈给主干网络,从而能够直接提取具有较强区分度的特征,避免了在测试阶段建图的计算开销.通过在多个数据集上进行的实验,证明了提出方法的有效性,能够提高细粒度分类的精度.
Keyword ：
图神经网络 图神经网络 弱监督目标定位 弱监督目标定位 深度学习 深度学习 知识蒸馏 知识蒸馏 细粒度图像分类 细粒度图像分类
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| GB/T 7714 | 陈权 , 陈飞 , 王衍根 et al. 融合目标定位与异构局部交互学习的细粒度图像分类 [J]. | 自动化学报 , 2024 , 50 (11) : 2219-2230 . |
| MLA | 陈权 et al. "融合目标定位与异构局部交互学习的细粒度图像分类" . | 自动化学报 50 . 11 (2024) : 2219-2230 . |
| APA | 陈权 , 陈飞 , 王衍根 , 程航 , 王美清 . 融合目标定位与异构局部交互学习的细粒度图像分类 . | 自动化学报 , 2024 , 50 (11) , 2219-2230 . |
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高强度的高斯噪声往往会模糊或破坏图像的细节和结构,导致边缘信息的丢失.为此,提出基于逐像素强化学习的边缘保持图像复原算法.首先,为每个像素构建一个像素层智能体并设计针对边缘处的侧窗均值滤波器到动作空间中,所有的像素层智能体共享优势行动者-评论家算法的参数,因此模型可以同时输出所有位置的状态转移概率并选择合适的策略进行状态转移,从而复原图像;其次,在特征提取共享网络中结合协调注意力,聚焦所有像素位置在特征通道间的全局信息,并保留位置嵌入信息;然后,为了缓解稀疏奖励问题,设计一个基于图拉普拉斯正则的辅助损失,关注图像的局部平滑信息,对局部不平滑区域加以惩罚,从而促进像素层智能体更加有效地学习到正确的策略以实现边缘保持.实验结果表明,所提的算法在Middlebury2005数据集和MNIST数据集上的峰值信噪比(PSNR)分别达到32.97 dB和28.26 dB,相比于Pixel-RL算法分别提升了 0.23 dB和0.75 dB,参数量和训练总时间分别减少了 44.9％和18.2％,在实现边缘保持的同时有效降低了模型的复杂度.
Keyword ：
协调注意力 协调注意力 图像复原 图像复原 图拉普拉斯 图拉普拉斯 深度强化学习 深度强化学习 边缘保持 边缘保持 逐像素强化学习 逐像素强化学习
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| GB/T 7714 | 江敏 , 陈飞 , 程航 et al. 基于逐像素强化学习的边缘保持图像复原 [J]. | 计算机工程 , 2024 , 50 (12) : 224-232 . |
| MLA | 江敏 et al. "基于逐像素强化学习的边缘保持图像复原" . | 计算机工程 50 . 12 (2024) : 224-232 . |
| APA | 江敏 , 陈飞 , 程航 , 王美清 . 基于逐像素强化学习的边缘保持图像复原 . | 计算机工程 , 2024 , 50 (12) , 224-232 . |
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Abstract ：
Existing vision-language pre-training models typically extract region features and conduct fine-grained local alignment based on masked image/text completion or object detection methods. However, these models often design independent subtasks for different modalities, which may not adequately leverage interactions between modalities, requiring large datasets to achieve optimal performance. To address these limitations, this paper introduces a novel pre-training approach that facilitates fine-grained vision-language interaction. We propose two new subtasks - image filling and text filling - that utilize data from one modality to complete missing parts in another, enhancing the model's ability to integrate multi-modal information. A selector mechanism is also developed to minimize semantic overlap between modalities, thereby improving the efficiency and effectiveness of the pre-trained model. Our comprehensive experimental results demonstrate that our approach not only fosters better semantic associations among different modalities but also achieves state-of-the-art performance on downstream vision-language tasks with significantly smaller datasets.
Keyword ：
Cross-modal Cross-modal Image captioning Image captioning Partial auxiliary Partial auxiliary Pre-training Pre-training
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| GB/T 7714 | Cheng, Hang , Ye, Hehui , Zhou, Xiaofei et al. Vision-language pre-training via modal interaction [J]. | PATTERN RECOGNITION , 2024 , 156 . |
| MLA | Cheng, Hang et al. "Vision-language pre-training via modal interaction" . | PATTERN RECOGNITION 156 (2024) . |
| APA | Cheng, Hang , Ye, Hehui , Zhou, Xiaofei , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Vision-language pre-training via modal interaction . | PATTERN RECOGNITION , 2024 , 156 . |
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针对一般深度学习方法求解非线性偏微分方程时泛化能力差的问题，提出一种使用改进欧拉法联通网络模块的长短期卷积循环神经网络.该神经网络的构建运用改进欧拉法和有限差分法，通过改进欧拉法实现网络中模块之间的有效连接.基于有限差分法构建的卷积核实现偏微分方程中涉及的导数项的精确近似，并在Burgers方程和λ-ω反应扩散方程上进行仿真实验.实验结果证明，该方法不但在训练数据上具有很高的精度，而且在外推到新领域时也表现出较强的泛化能力.
Keyword ：
偏微分方程 偏微分方程 改进欧拉法 改进欧拉法 有限差分法 有限差分法 深度学习 深度学习 长短期记忆 长短期记忆
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| GB/T 7714 | 黄冠男 , 王靖岳 , 王美清 . 基于改进欧拉法的非线性偏微分方程神经网络求解器 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (04) : 396-403 . |
| MLA | 黄冠男 et al. "基于改进欧拉法的非线性偏微分方程神经网络求解器" . | 福州大学学报(自然科学版) 52 . 04 (2024) : 396-403 . |
| APA | 黄冠男 , 王靖岳 , 王美清 . 基于改进欧拉法的非线性偏微分方程神经网络求解器 . | 福州大学学报(自然科学版) , 2024 , 52 (04) , 396-403 . |
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Image denoising is a fundamental tool in the fields of image processing and computer vision. With the rapid development of multimedia and cloud computing, it has become popular for resource-constrained users to outsource the storage and denoising of massive images. However, it may cause privacy concerns and response delays. In this scenario, we propose an efFicient privAcy-preseRving Image deNoising schEme (FARINE) for outsourcing digital images. By introducing a key conversion mechanism, FARINE allows removing noise from a given noisy image using a non-local mean way without leaking any information about the plaintext content. Due to its low computational latency/communication cost, edge computing is considered to improve the user experience. To achieve a dynamic user set efficiently, we design a fine-grained access control mechanism to support user authorization and revocation in multi-user scenarios. Extensive experiments over several benchmark data sets show that FARINE obtains comparable performance to plaintext image denoising.
Keyword ：
access control access control edge computing edge computing homomorphic encryption homomorphic encryption image denoising image denoising Privacy-preserving Privacy-preserving
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| GB/T 7714 | Huang, Yibing , Xu, Yongliang , Cheng, Hang et al. Edge-based secure image denoising scheme supporting flexible user authorization [J]. | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY , 2024 , 18 . |
| MLA | Huang, Yibing et al. "Edge-based secure image denoising scheme supporting flexible user authorization" . | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 18 (2024) . |
| APA | Huang, Yibing , Xu, Yongliang , Cheng, Hang , Chen, Fei , Wang, Meiqing . Edge-based secure image denoising scheme supporting flexible user authorization . | JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY , 2024 , 18 . |
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Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.
Keyword ：
anti-collusion anti-collusion Deep neural networks Deep neural networks digital fingerprinting digital fingerprinting digital watermarking digital watermarking
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| GB/T 7714 | Cheng, Hang , Li, Xibin , Wang, Huaxiong et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) : 97-109 . |
| MLA | Cheng, Hang et al. "DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 1 (2024) : 97-109 . |
| APA | Cheng, Hang , Li, Xibin , Wang, Huaxiong , Zhang, Xinpeng , Liu, Ximeng , Wang, Meiqing et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) , 97-109 . |
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