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学者姓名：吴丽君
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Abstract ：
为解决生成对抗网络训练过程中因损失简单加权导致的图像感知质量下降问题,提出损失自适应调整的生成对抗超分辨率网络(LA-GAN).首先,该方法设计通过计算角点分布的相关强度大小,区分规则纹理区域与不规则纹理区域.其次,基于不同区域,设计了区域自适应生成对抗学习框架.在该框架中,网络只在不规则纹理区域中进行对抗学习,提高感知质量.此外,基于下采样图像和图像块相似性的重组图像取代训练集中的高分辨率图像,实现平均绝对损失在不规则纹理区域弱约束网络,在规则纹理区域强约束网络,保证图像信号保真度.最后,通过实验证明经过优化的网络在信号保真度和感知质量方面皆有提升.
Keyword ：
区域自适应 区域自适应 损失函数 损失函数 生成对抗网络 生成对抗网络 超分辨率 超分辨率
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| GB/T 7714 | 林旭锋 , 吴丽君 , 陈志聪 et al. 损失自适应的高感知质量生成对抗超分辨率网络 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (1) : 26-34 . |
| MLA | 林旭锋 et al. "损失自适应的高感知质量生成对抗超分辨率网络" . | 福州大学学报（自然科学版） 53 . 1 (2025) : 26-34 . |
| APA | 林旭锋 , 吴丽君 , 陈志聪 , 林培杰 , 程树英 . 损失自适应的高感知质量生成对抗超分辨率网络 . | 福州大学学报（自然科学版） , 2025 , 53 (1) , 26-34 . |
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Video super-resolution is capable of recovering high-resolution images from multiple low-resolution images, where loop structures are a common frame choice for video super-resolution tasks. BasicVSR employs bidirectional propagation and feature alignment to efficiently utilize information from the entire input video. In this work, we improved the performance of the network by revisiting the role of the various modules in BasicVSR and redesigning the network. Firstly, we will maintain centralized communication with the reference frame through the reference-based feature enrichment module after optical flow distortion, which is helpful for handling complex motion, and at the same time, for the selected keyframe, according to the degree of motion deviation of the adjacent frame relative to the keyframe, it is divided into two different regions, and the model with different receptive fields is adopted for feature extraction to further alleviate the accumulation of alignment errors. In the feature correction module, we modify the simple residual block stack to RIR structure, and fuse different levels of features with each other, which can make the final feature information more comprehensive and abundant. In addition, dense connection are introduced in the reconstruction module to promote the full use of hierarchical feature information for better reconstruction. Experimental verification is carried out on two public datasets: Vid4 and REDS4, and the comparative results show that compared with BasicVSR, the PSNR quantitative indexes of the proposed improved model on the two datasets are improved by 0.27dB and 0.33dB, respectively. In addition, from the point of view of visual perception, the model can effectively improve the clarity of the image and reduce artifacts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Bidirectional propagation Bidirectional propagation Densely connected residual Densely connected residual Feature enrichment module Feature enrichment module Time difference Time difference Video super-resolution Video super-resolution
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| GB/T 7714 | Wu, L. , Ma, Y. , Chen, Z. . Dense video super-resolution time-differential network with feature enrichment module [J]. | Signal, Image and Video Processing , 2024 , 18 (11) : 7887-7897 . |
| MLA | Wu, L. et al. "Dense video super-resolution time-differential network with feature enrichment module" . | Signal, Image and Video Processing 18 . 11 (2024) : 7887-7897 . |
| APA | Wu, L. , Ma, Y. , Chen, Z. . Dense video super-resolution time-differential network with feature enrichment module . | Signal, Image and Video Processing , 2024 , 18 (11) , 7887-7897 . |
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Cross-domain object detection is challenging because object detection models are significantly susceptible to domain style. As a popular semi-supervised learning method, the teacher-student framework (pseudo labels from the teacher model supervise the student model) achieves significant accuracy gains in cross-domain object detection. However, it suffers from the domain shift and prone to generate low-quality pseudo labels, which limits the performance. To mitigate this problem, we propose a teacher-student framework that utilizes style transfer method, augmentation strategies, and adversarial learning to address domain shift. Specifically, we design a Fourier style transfer method to reduce the gap between source and target domainswithout altering the semantic information of the objects. Furthermore, we improve the data augmentation strategy, by weakly augmenting the images from the target domain, to avoid the teacher model biased to the source domain. Finally, we employ feature-level adversarial training in the student model which is trained based on images from all domains, allowing features derived from all domains to share similar distributions. This process ensures that the student model produces domain-invariant features. Our approach achieves state-of-the-art performance in several benchmark tests. For example, it achieved 51.6% and 49.9% mAP on Foggy Cityscapes and Clipart1K, respectively.
Keyword ：
Adversarial Learning Adversarial Learning Cross-Domain Object Detection Cross-Domain Object Detection Style Transfer Style Transfer Unsupervised Domain Adaptation Unsupervised Domain Adaptation
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| GB/T 7714 | Wu, Lijun , Cao, Zhe , Chen, Zhicong . Teacher-Student Cross-Domain Object Detection Model Combining Style Transfer and Adversarial Learning [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT X , 2024 , 14434 : 334-345 . |
| MLA | Wu, Lijun et al. "Teacher-Student Cross-Domain Object Detection Model Combining Style Transfer and Adversarial Learning" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT X 14434 (2024) : 334-345 . |
| APA | Wu, Lijun , Cao, Zhe , Chen, Zhicong . Teacher-Student Cross-Domain Object Detection Model Combining Style Transfer and Adversarial Learning . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT X , 2024 , 14434 , 334-345 . |
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Abstract ：
Video super-resolution is capable of recovering high-resolution images from multiple low-resolution images, where loop structures are a common frame choice for video super-resolution tasks. BasicVSR employs bidirectional propagation and feature alignment to efficiently utilize information from the entire input video. In this work, we improved the performance of the network by revisiting the role of the various modules in BasicVSR and redesigning the network. Firstly, we will maintain centralized communication with the reference frame through the reference-based feature enrichment module after optical flow distortion, which is helpful for handling complex motion, and at the same time, for the selected keyframe, according to the degree of motion deviation of the adjacent frame relative to the keyframe, it is divided into two different regions, and the model with different receptive fields is adopted for feature extraction to further alleviate the accumulation of alignment errors. In the feature correction module, we modify the simple residual block stack to RIR structure, and fuse different levels of features with each other, which can make the final feature information more comprehensive and abundant. In addition, dense connection are introduced in the reconstruction module to promote the full use of hierarchical feature information for better reconstruction. Experimental verification is carried out on two public datasets: Vid4 and REDS4, and the comparative results show that compared with BasicVSR, the PSNR quantitative indexes of the proposed improved model on the two datasets are improved by 0.27dB and 0.33dB, respectively. In addition, from the point of view of visual perception, the model can effectively improve the clarity of the image and reduce artifacts.
Keyword ：
Bidirectional propagation Bidirectional propagation Densely connected residual Densely connected residual Feature enrichment module Feature enrichment module Time difference Time difference Video super-resolution Video super-resolution
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| GB/T 7714 | Wu, Lijun , Ma, Yong , Chen, Zhicong . Dense video super-resolution time-differential network with feature enrichment module [J]. | SIGNAL IMAGE AND VIDEO PROCESSING , 2024 , 18 (11) : 7887-7897 . |
| MLA | Wu, Lijun et al. "Dense video super-resolution time-differential network with feature enrichment module" . | SIGNAL IMAGE AND VIDEO PROCESSING 18 . 11 (2024) : 7887-7897 . |
| APA | Wu, Lijun , Ma, Yong , Chen, Zhicong . Dense video super-resolution time-differential network with feature enrichment module . | SIGNAL IMAGE AND VIDEO PROCESSING , 2024 , 18 (11) , 7887-7897 . |
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针对户外运行的光伏阵列常见的复合故障问题，提出了一种融合残差网络与视觉Transformer的混合网络模型，并使用迁移学习方法对其优化，以提高故障诊断模型在复合故障场景下的可靠性。首先，从光伏阵列的静态I-V曲线和环境参数中提取有效特征作为输入，然后，利用仿真数据进行预训练，最后，通过迁移学习验证模型在诊断真实实验数据时的可靠性。实验结果表明，该混合模型在应对复合故障场景时具有较高的收敛速度和准确率。
Keyword ：
I-V曲线 I-V曲线 光伏阵列 光伏阵列 故障诊断 故障诊断 迁移学习 迁移学习
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| GB/T 7714 | 王鑫 , 陈志聪 , 吴丽君 . 基于迁移学习的光伏阵列复合故障诊断研究 [J]. | 电气开关 , 2024 , 62 (04) : 17-22 . |
| MLA | 王鑫 et al. "基于迁移学习的光伏阵列复合故障诊断研究" . | 电气开关 62 . 04 (2024) : 17-22 . |
| APA | 王鑫 , 陈志聪 , 吴丽君 . 基于迁移学习的光伏阵列复合故障诊断研究 . | 电气开关 , 2024 , 62 (04) , 17-22 . |
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提出一种基于图注意力网络(graph attention network,GAT)的光伏阵列故障诊断模型,以解决光伏阵列中因故障导致的发电效率降低、正常运行受阻等问题.通过离散小波变换和滑窗算法截取故障后稳态时序信号并将其分割成子区间,将子区间视为图节点.使用K邻近构图法将故障后稳态电压、电流数据转变成图结构,构建节点级GAT模型.通过多头注意力机制自动提取电压、电流图结构的故障特征.通过实验室光伏阵列获取实验数据集,对所提模型进行测试.结果表明,本模型能准确诊断光伏阵列的不同故障状态,平均准确率达到99.790％,效果优于所对比的其他网络模型.
Keyword ：
光伏阵列 光伏阵列 图神经网络 图神经网络 图结构 图结构 故障诊断 故障诊断 注意力机制 注意力机制
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| GB/T 7714 | 董浪灿 , 卢箫扬 , 林培杰 et al. 利用GAT的光伏阵列故障诊断方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 505-512 . |
| MLA | 董浪灿 et al. "利用GAT的光伏阵列故障诊断方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 505-512 . |
| APA | 董浪灿 , 卢箫扬 , 林培杰 , 程树英 , 陈志聪 , 吴丽君 . 利用GAT的光伏阵列故障诊断方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 505-512 . |
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为提高户外光伏电站现场退化评估的准确性和可靠性,提出一种物理和数据驱动的光伏组件性能退化模型.研究户外光伏组件受静态温度、循环温度、相对湿度和紫外线影响的特性,并综合动态应力函数,利用累积损失模型对多应力下光伏电站性能退化进行建模.此外,退化模型的未知参数通过遗传算法来提取.使用美国国家太阳辐射数据库的长期数据对该模型进行训练和测试.将性能退化实际值和模型计算值进行对比,结果表明,本研究所提出模型的相对误差更低,验证了该方法的可行性.
Keyword ：
优化算法 优化算法 光伏电站 光伏电站 光伏退化 光伏退化 数据驱动 数据驱动
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| GB/T 7714 | 王宇钖 , 陈志聪 , 吴丽君 et al. 利用物理和数据驱动的光伏性能退化建模方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 513-519 . |
| MLA | 王宇钖 et al. "利用物理和数据驱动的光伏性能退化建模方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 513-519 . |
| APA | 王宇钖 , 陈志聪 , 吴丽君 , 俞金玲 , 程树英 , 林培杰 . 利用物理和数据驱动的光伏性能退化建模方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 513-519 . |
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Super-resolution (SR) algorithms have been broadly applied to improve the visual quality of images. However, the unstable SR results and the difficulty of collecting high-resolution (HR) and low-resolution (LR) image pairs still greatly block its application in the position-sensitive downstream tasks in real world. To address these difficulties, we propose an unpaired image-based cross-domain supervised SR method for position-sensitive downstream tasks (CDS PosSR), which greatly improve the fidelity of geometric positions in the image based on the geometric consistency of the image. Since the different semantic information and root-mean-square error cannot constraint unpaired images during the degradation process, an unpaired image cross-domain supervised hierarchical degradation model is elaborated. Meanwhile, randomly distributed input is adopted, so as to alleviate the problem that the dataset cannot fully cover real-world LR images. According to the experimental results, CDS PosSR not only improves the visual and quantitative performance of the generated images but also outperforms other SR reconstruction algorithms in terms of the fidelity of feature point location and geometry, which can provide support for position-sensitive downstream tasks.
Keyword ：
Degradation learning Degradation learning geometric consistency geometric consistency unpaired image super-resolution (SR) unpaired image super-resolution (SR)
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| GB/T 7714 | Wu, Lijun , Chen, Lanxin , Chen, Zhicong et al. CDS PosSR: Cross-Domain Supervised Unpaired Image Super-Resolution for Position-Sensitive Downstream Tasks [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Wu, Lijun et al. "CDS PosSR: Cross-Domain Supervised Unpaired Image Super-Resolution for Position-Sensitive Downstream Tasks" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Wu, Lijun , Chen, Lanxin , Chen, Zhicong , Cheng, Shuying , Chen, Zhaohui . CDS PosSR: Cross-Domain Supervised Unpaired Image Super-Resolution for Position-Sensitive Downstream Tasks . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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为了克服光伏发电固有的间断性和波动性对电网稳定性的负面影响，提出一种二维灰度关联分析-双向长短期记忆神经网络(two-dimensional grey relational analysis and bidirectional long short-term memory network, 2DGRA-BiLSTM)模型，用于实现日前光伏功率曲线预测，以更好指导电网调度.不同于以往的点预测，本研究将日功率曲线作为整体进行预测.首先用2DGRA实现最佳历史相似日数据的获取；其次，根据日功率曲线的波动性将总数据分为3类；最后，根据3种分类，分别训练3种BiLSTM模型对日功率曲线进行预测.所提出的预测模型通过沙漠知识澳大利亚太阳能中心历史气象和功率数据进行训练，并通过数值天气预报和功率数据进行测试.对比其他几种神经网络模型，实验表明所提出模型具有更好的综合预测性能，在晴空、轻度非晴空和重度非晴空条件下，决定系数(R~2)分别为0.994、0.940和0.782.
Keyword ：
二维灰度关联分析 二维灰度关联分析 光伏功率 光伏功率 双向长短期记忆神经网络 双向长短期记忆神经网络 日前预测 日前预测
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| GB/T 7714 | 陈柏恒 , 陈志聪 , 吴丽君 et al. 利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (01) : 20-28 . |
| MLA | 陈柏恒 et al. "利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法" . | 福州大学学报(自然科学版) 52 . 01 (2024) : 20-28 . |
| APA | 陈柏恒 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用2DGRA-BiLSTM模型的日前光伏功率曲线预测方法 . | 福州大学学报(自然科学版) , 2024 , 52 (01) , 20-28 . |
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行人重识别是跨摄像头追踪的关键环节之一,主流方法多采用ImageNet进行预训练,忽视了数据集的域间差异,且以结构庞大的多分支模型居多,模型复杂度较高.本文设计一种行人重识别方法,采用基于原始视频带噪声标签参与监督的方式进行预训练,减少域间差异以提升特征表达能力;以基于注意力的特征融合方式取代残差网络的跳接映射,增强网络的特征提取能力;在网络中嵌入坐标注意力机制,在低复杂度的情况下强化关键特征,抑制低贡献特征;采用随机擦除对输入数据做数据增强以提高泛化能力,联合分类损失、三元组损失和中心损失函数对网络进行监督训练.在公开数据集Market-1501和Duke-MTMC上完成了消融实验,与主流方法对比实验表明本方法在不需要复杂多分支逻辑结构的前提下,仍可达到较高的精度.
Keyword ：
残差网络 残差网络 注意力机制 注意力机制 特征融合 特征融合 行人重识别 行人重识别 预训练 预训练
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| GB/T 7714 | 南灏 , 吴丽君 . 基于视频预训练和注意力特征融合的行人重识别方法 [J]. | 智能计算机与应用 , 2024 , 14 (1) : 95-101 . |
| MLA | 南灏 et al. "基于视频预训练和注意力特征融合的行人重识别方法" . | 智能计算机与应用 14 . 1 (2024) : 95-101 . |
| APA | 南灏 , 吴丽君 . 基于视频预训练和注意力特征融合的行人重识别方法 . | 智能计算机与应用 , 2024 , 14 (1) , 95-101 . |
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