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学者姓名：陈志聪
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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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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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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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Recently, CNN-Transformer hybrid network has been proposed to resolve either the heavy computational burden of CNN or the difficulty encountered during training the Transformer-based networks. In this work, we design an efficient and effective CNN-Transformer hybrid network for human pose estimation, namely CTHPose. Specifically, Polarized CNN Module is employed to extract the feature with plentiful visual semantic clues, which is beneficial for the convergence of the subsequent Transformer encoders. Pyramid Transformer Module is utilized to build the long-term relationship between human body parts with lightweight structure and less computational complexity. To establish long-term relationship, large field of view is necessary in Transformer, which leads to a large computational workload. Hence, instead of the entire feature map, we introduced a reorganized small sliding window to provide the required large field of view. Finally, Heatmap Generator is designed to reconstruct the 2D heatmaps from the 1D keypoint representation, which balances parameters and FLOPs while obtaining accurate prediction. According to quantitative comparison experiments with CNN estimators, CTHPose significantly reduces the number of network parameters and GFLOPs, while also providing better detection accuracy. Compared with mainstream pure Transformer networks and state-of-the-art CNN-Transformer hybrid networks, this network also has competitive performance, and is more robust to the clothing pattern interference and overlapping limbs from the visual perspective.
Keyword ：
Human pose estimation Human pose estimation Long-range dependency Long-range dependency Transformer Transformer
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| GB/T 7714 | Chen, Danya , Wu, Lijun , Chen, Zhicong et al. CTHPose: An Efficient and Effective CNN-Transformer Hybrid Network for Human Pose Estimation [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT V , 2024 , 14429 : 327-339 . |
| MLA | Chen, Danya et al. "CTHPose: An Efficient and Effective CNN-Transformer Hybrid Network for Human Pose Estimation" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT V 14429 (2024) : 327-339 . |
| APA | Chen, Danya , Wu, Lijun , Chen, Zhicong , Lin, Xufeng . CTHPose: An Efficient and Effective CNN-Transformer Hybrid Network for Human Pose Estimation . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT V , 2024 , 14429 , 327-339 . |
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To address the problem of incomplete segmentation of large objects and miss-segmentation of tiny objects that is universally existing in semantic segmentation algorithms, PACAMNet, a real-time segmentation network based on short-term dense concatenate of parallel atrous convolution and fusion of attentional features is proposed, called PACAMNet. First, parallel atrous convolution is introduced to improve the short-term dense concatenate module. By adjusting the size of the atrous factor, multi-scale semantic information is obtained to ensure that the last layer of the module can also obtain rich input feature maps. Second, attention feature fusion module is proposed to align the receptive fields of deep and shallow feature maps via depth-separable convolutions with different sizes, and the channel attention mechanism is used to generate weights to effectively fuse the deep and shallow feature maps. Finally, experiments are carried out based on both Cityscapes and CamVid datasets, and the segmentation accuracy achieve 77.4% and 74.0% at the inference speeds of 98.7 FPS and 134.6 FPS, respectively. Compared with other methods, PACAMNet improves the inference speed of the model while ensuring higher segmentation accuracy, so PACAMNet achieve a better balance between segmentation accuracy and inference speed.
Keyword ：
Atrous convolution Atrous convolution Attention mechanism Attention mechanism Feature fusion Feature fusion Real-time semantic segmentation Real-time semantic segmentation
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| GB/T 7714 | Wu, Lijun , Qiu, Shangdong , Chen, Zhicong . Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion [J]. | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2024 , 21 (3) . |
| MLA | Wu, Lijun et al. "Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion" . | JOURNAL OF REAL-TIME IMAGE PROCESSING 21 . 3 (2024) . |
| APA | Wu, Lijun , Qiu, Shangdong , Chen, Zhicong . Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion . | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2024 , 21 (3) . |
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为了解决局部阴影下传统最大功率点追踪(maximum power point tracking, MPPT)算法容易陷入局部最优从而降低光伏系统发电效率的问题，本研究提出融合正弦余弦算法和自适应策略的布谷鸟优化算法(cuckoo search algorithm fusing sine cosine algorithm and adaptive strategy, AFCS),并应用于光伏全局MPPT控制中，以改善其收敛速度与追踪精度.设置多种光照情况，并与扰动观察法、花朵授粉算法和粒子群算法进行对比.经过Matlab/Simulink仿真验证，表明本算法拥有较快的收敛速度和较高的追踪精度，在各个光照条件下均能快速追踪到光伏阵列最大功率点，可以有效提高光伏系统的发电效率.
Keyword ：
光伏阵列 光伏阵列 局部阴影 局部阴影 最大功率点追踪 最大功率点追踪 自适应策略 自适应策略 融合算法 融合算法
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| GB/T 7714 | 张致用 , 陈志聪 , 吴丽君 et al. 利用改进布谷鸟优化算法的光伏全局MPPT方法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (02) : 139-146 . |
| MLA | 张致用 et al. "利用改进布谷鸟优化算法的光伏全局MPPT方法" . | 福州大学学报(自然科学版) 52 . 02 (2024) : 139-146 . |
| APA | 张致用 , 陈志聪 , 吴丽君 , 林培杰 , 程树英 . 利用改进布谷鸟优化算法的光伏全局MPPT方法 . | 福州大学学报(自然科学版) , 2024 , 52 (02) , 139-146 . |
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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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Obtaining the best performance of photovoltaic requires a proper model. This paper uses a new approach based on a modified social network search algorithm combined with the dichotomy method to produce the best parameters of a photovoltaic cell, module, and array. To improve the performance of the parameters to be estimated, a control parameter via a Gaussian and Cauchy distribution is randomly added in the search space to allow the agents to converge to the optimal solution. Then the dichotomy method is inserted into the objective function to compute the best-estimated currents. The application of the proposed model on three different systems, and the subsequent comparison with existing methods show the high accuracy of the proposed method, with the best root mean square error of 6.3554 × 10−4 for the RTC cell, 1.9096 × 10−3 for the Photowatt PWP module and 0.0134 for the 18 PV experimental field. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Keyword ：
Learning algorithms Learning algorithms Mean square error Mean square error Parameter estimation Parameter estimation Photoelectrochemical cells Photoelectrochemical cells Solar panels Solar panels Solar power generation Solar power generation
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| GB/T 7714 | Gnetchejo, Patrick Juvet , Daniel, Mbadjoun Wapet , Dadjé, Abdouramani et al. A new approach based on modified social network search algorithm combined with dichotomy method for solar photovoltaic parameter estimation [J]. | International Journal of Ambient Energy , 2024 , 45 (1) . |
| MLA | Gnetchejo, Patrick Juvet et al. "A new approach based on modified social network search algorithm combined with dichotomy method for solar photovoltaic parameter estimation" . | International Journal of Ambient Energy 45 . 1 (2024) . |
| APA | Gnetchejo, Patrick Juvet , Daniel, Mbadjoun Wapet , Dadjé, Abdouramani , Salomé, Ndjakomo Essiane , Pilario, Karl Ezra , Pierre, Ele et al. A new approach based on modified social network search algorithm combined with dichotomy method for solar photovoltaic parameter estimation . | International Journal of Ambient Energy , 2024 , 45 (1) . |
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为了减少电力信号传输时的数据量,本文提出了一种融合压缩感知和LZW编码的电力数据压缩算法,在保证重构精度不变的条件下对电力数据进行进一步的压缩,提高了整体的压缩率.首先,对压缩感知的各种观测矩阵进行仿真分析;其次,选择使用稀疏随机矩阵作为本文的观测矩阵,提出了一种能够快速完成压缩感知计算的硬件实现方法,并完成了硬件的设计和验证.实验表明,本设计在FPGA器件上的工作频率最高可达200 MHz;整个数据压缩过程的总延时约为16.11μs;在重构误差约为4.83％时,数据压缩率约为36.83％,比仅使用压缩感知提升了约13.17％.
Keyword ：
LZW编码 LZW编码 压缩感知 压缩感知 电力信号 电力信号
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| GB/T 7714 | 谢宇杰 , 陈志聪 , 吴丽君 . 融合压缩感知和LZW编码的电力数据压缩算法 [J]. | 智能计算机与应用 , 2024 , 14 (1) : 124-129 . |
| MLA | 谢宇杰 et al. "融合压缩感知和LZW编码的电力数据压缩算法" . | 智能计算机与应用 14 . 1 (2024) : 124-129 . |
| APA | 谢宇杰 , 陈志聪 , 吴丽君 . 融合压缩感知和LZW编码的电力数据压缩算法 . | 智能计算机与应用 , 2024 , 14 (1) , 124-129 . |
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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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