Query：
学者姓名：陈志聪
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Reliable identification of gunshot events is crucial for reducing gun violence and enhancing public safety. However, current gunshot detection and recognition methods are still affected by complex shooting scenarios, various nongunshot events, diverse firearm types, and scarce gunshot datasets. To address these issues, based on triaxial acceleration of guns, a novel general deep transfer learning approach is proposed for gunshot detection and recognition, which combines a temporal deep learning model with transfer learning and automated machine learning (AutoML) to improve the accuracy, reliability and generalization performance. First, a new gunshot recognition model named as MobileNetTime is proposed for the two-class gunshot event detection, three-class coarse firearm recognition, and 15-class fine firearm recognition, which utilizes 1-D convolution and inverted residual modules to autonomously extract higher-level features from the time series acceleration data. Second, considering the impact of nongunshot events, the AutoML is employed for model fine tuning, to transfer the pretrained MobileNetTime from the handgun to various firearm types. In addition, we propose a low-power versatile gunshot recognition system framework employing a triaxial accelerometer for both of wrist-worn and gun-embedded scenarios, which adopts a two-stage wake-up mechanism that selectively monitors gunshot events using temporal and spectral energy features. The experimental results on the two gunshot datasets DGUWA and GRD show that the proposed model can achieve up to 100% accuracy on the DGUWA dataset and 98.98% accuracy on the GRD dataset for the two-class gunshot detection. Moreover, the proposed deep transfer learning approach achieves a 98.98% accuracy for 16-class firearm classification, which is 6.21% higher than the model without transfer learning.
Keyword ：
Accelerometers Accelerometers Accuracy Accuracy Adaptation models Adaptation models Automated machine learning (AutoML) Automated machine learning (AutoML) Data models Data models deep transfer learning deep transfer learning Feature extraction Feature extraction gunshot detection and recognition gunshot detection and recognition Internet of Things Internet of Things Monitoring Monitoring Real-time systems Real-time systems Training Training Transfer learning Transfer learning tri-axial acceleration tri-axial acceleration
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Zhicong , Zheng, Haoxin , Wu, Lijun et al. Deep-Transfer-Learning-Based Intelligent Gunshot Detection and Firearm Recognition Using Tri-Axial Acceleration [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) : 5891-5900 . |
| MLA | Chen, Zhicong et al. "Deep-Transfer-Learning-Based Intelligent Gunshot Detection and Firearm Recognition Using Tri-Axial Acceleration" . | IEEE INTERNET OF THINGS JOURNAL 12 . 5 (2025) : 5891-5900 . |
| APA | Chen, Zhicong , Zheng, Haoxin , Wu, Lijun , Huang, Jingchang , Yang, Yang . Deep-Transfer-Learning-Based Intelligent Gunshot Detection and Firearm Recognition Using Tri-Axial Acceleration . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (5) , 5891-5900 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Under the carbon neutrality goal, the projects to develop zero-carbon microgrids are emerging all over the world. However, the categories, trends, challenges, and future research prospects of the zero-carbon microgrid are still unclear. To deal with this problem, this research first reviews the real-world and simulation cases of zero-carbon microgrids in recent years and classifies them into two categories, i.e., on-grid mode and off-grid mode. Then, three development trends of the zero-carbon microgrid are discussed, including an extremely high ratio of clean energy, large-scale energy storage, and an extremely high ratio of power electronic devices. Next, the challenges in achieving the zero-carbon microgrids in terms of feasibility, flexibility, and stability are discussed in detail. Finally, future research prospects in long-term low-cost energy storage, power/energy balancing, and stability control, are emphasized. © 2024 Elsevier Ltd
Keyword ：
Carbon Carbon Electric power systems Electric power systems Energy storage Energy storage Renewable energy Renewable energy
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Lei , Gao, Lingyun , Xing, Shuping et al. Zero-carbon microgrid: Real-world cases, trends, challenges, and future research prospects [J]. | Renewable and Sustainable Energy Reviews , 2024 , 203 . |
| MLA | Chen, Lei et al. "Zero-carbon microgrid: Real-world cases, trends, challenges, and future research prospects" . | Renewable and Sustainable Energy Reviews 203 (2024) . |
| APA | Chen, Lei , Gao, Lingyun , Xing, Shuping , Chen, Zhicong , Wang, Weiwei . Zero-carbon microgrid: Real-world cases, trends, challenges, and future research prospects . | Renewable and Sustainable Energy Reviews , 2024 , 203 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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. © 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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the proposal of the "dual carbon'' goal, distributed energy resources are widely used in carbon reduction and transformation in urban and rural areas of China. However, due to the characteristics of distributed energy generation, there are still some problems and challenges in its application. So far, few papers have commented on the new development obstacles and challenges of distributed smart grids. In order to clarify the characteristics, challenges, and future research prospects of distributed smart grids more clearly, this article first focuses on the real cases and characteristic analysis of distributed smart grids in rural and urban park scenarios in China. Next, the key analysis and control technologies that have been applied or can be applied to distributed smart grids in rural and park scenarios are summarized. Finally, a detailed discussion is conducted on the current issues, challenges, and future research directions of distributed smart grids in China in terms of stability, security, rural infrastructure, and consumption. © 2024 IEEE.
Keyword ：
analysis technology analysis technology carbon reduction carbon reduction control technology control technology distributed smart grid distributed smart grid parks parks rural areas rural areas
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gao, L. , Chen, L. , Chen, Z. et al. Overview of Key Technologies for Distributed Smart Grid in Parks and Rural Areas: A Case Study in China [未知]. |
| MLA | Gao, L. et al. "Overview of Key Technologies for Distributed Smart Grid in Parks and Rural Areas: A Case Study in China" [未知]. |
| APA | Gao, L. , Chen, L. , Chen, Z. , Wang, W. , Meng, Q. . Overview of Key Technologies for Distributed Smart Grid in Parks and Rural Areas: A Case Study in China [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
