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学者姓名：陈志聪
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Abstract ：
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 等. "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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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 等. "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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针对目前大部分光伏功率预测模型采用批量离线训练方式，且新建光伏电站训练数据较少的问题，本文提出一种基于增量学习的卷积神经网络（CNN）和长短期记忆（LSTM）网络结合的光伏功率预测模型。首先，采用CNN对气象数据进行特征提取，并通过LSTM网络进行功率预测，以此CNN-LSTM混合模型进行背景学习，训练出可用于增量学习的基准模型。其次，根据不同的时间跨度进行增量学习训练，实现模型的在线更新。针对增量学习中的灾难性遗忘问题，采用弹性权重整合（EWC）算法和在线弹性整合（Online＿EWC）算法进行缓解。实验结果表明，相较于无约束的增量学习，采用EWC和Online＿EWC方法的增量学习可以明显缓解灾难性遗忘问题，降低预测平均绝对误差（MAE）和均方根误差（RMSE）；同时，在保证预测精度的前提下，增量学习的耗时大幅低于传统的批量学习。
Keyword ：
光伏功率预测 光伏功率预测 增量学习 增量学习 弹性权重整合(EWC)算法 弹性权重整合(EWC)算法 长短期记忆(LSTM)网络 长短期记忆(LSTM)网络
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| GB/T 7714 | 严璐晗 , 林培杰 , 程树英 et al. 基于增量学习的CNN-LSTM光伏功率预测 [J]. | 电气技术 , 2024 , 25 (05) : 31-40 . |
| MLA | 严璐晗 et al. "基于增量学习的CNN-LSTM光伏功率预测" . | 电气技术 25 . 05 (2024) : 31-40 . |
| APA | 严璐晗 , 林培杰 , 程树英 , 陈志聪 , 卢箫扬 . 基于增量学习的CNN-LSTM光伏功率预测 . | 电气技术 , 2024 , 25 (05) , 31-40 . |
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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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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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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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光伏电站功率数据存在随机性和波动性的特征，研究精准的光伏电站功率预测模型成为未来电力发展中灵活的电力调度和管理的必要条件。对此提出一种基于混合DBSCAN聚类、PCA主成分分析和改进自注意力机制的光伏功率预测模型。首先采用DBSCAN聚类将分布较为分散和密集的历史数据进行分类，得到波动数据集和平稳数据集；其次利用PCA提取波动数据的主要成分序列，得到便于模型获得关键信息的时间序列；最后提取关键气象参数与具有感知上下文信息的改进自注意力机制模型进行互助式的动态建模。实验运用RMSE和MAE两个指标说明本文所提模型在每个季节下的平稳数据和波动数据都有较高的预测精度。
Keyword ：
DBSCAN聚类 DBSCAN聚类 PCA分析法 PCA分析法 光伏功率预测 光伏功率预测 自注意力机制 自注意力机制
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| GB/T 7714 | 李祺彬 , 卢箫扬 , 林培杰 et al. 基于DBSCAN-PCA和改进自注意力机制的光伏功率预测 [J]. | 电气开关 , 2024 , 62 (01) : 6-12 . |
| MLA | 李祺彬 et al. "基于DBSCAN-PCA和改进自注意力机制的光伏功率预测" . | 电气开关 62 . 01 (2024) : 6-12 . |
| APA | 李祺彬 , 卢箫扬 , 林培杰 , 程树英 , 陈志聪 . 基于DBSCAN-PCA和改进自注意力机制的光伏功率预测 . | 电气开关 , 2024 , 62 (01) , 6-12 . |
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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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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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