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学者姓名：林培杰
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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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Short-term water demand forecasting is essential for ensuring the sustainable use of water resources. The accuracy of water demand forecasting directly impacts the rationality of water resources management and the effectiveness of scheduling. Therefore, it is vital to accurately forecast water demand across various timescales. Based on this motivation, we propose an improved patch time series Transformer (PatchTST) model to forecast the multi-timescale short-term water demand. By introducing relative positional encoding (RPE), the model effectively learns the relationships between tokens. The model combines the global token information capture ability of the self-attention mechanism with the local token information capture ability of the convolutional network to enhance feature extraction abilities. Additionally, the model integrates the advantages of patch-wise and series-wise representation, enabling it to simultaneously capture both local and global dependencies in time series. We utilize historical data collected from district metering area to experimentally validate the effectiveness of the proposed model. Compared with one-dimensional convolutional neural network (1D-CNN), long shortterm memory (LSTM), Transformer, DLinear, and PatchTST models, our model demonstrates superior performance across all five forecasting scales. Finally, the effectiveness of the proposed design is further validated through ablation experiments.
Keyword ：
Deep learning Deep learning Multi-timescale Multi-timescale PatchTST PatchTST Water demand forecasting Water demand forecasting
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| GB/T 7714 | Lin, Peijie , Zhang, Xiangxin , Gong, Longcong et al. Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model [J]. | JOURNAL OF HYDROLOGY , 2024 , 651 . |
| MLA | Lin, Peijie et al. "Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model" . | JOURNAL OF HYDROLOGY 651 (2024) . |
| APA | Lin, Peijie , Zhang, Xiangxin , Gong, Longcong , Lin, Jingwei , Zhang, Jie , Cheng, Shuying . Multi-timescale short-term urban water demand forecasting based on an improved PatchTST model . | JOURNAL OF HYDROLOGY , 2024 , 651 . |
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提出一种基于高效通道注意(ECA)网络和双向长短期记忆神经网络(BILSTM)的自适应智慧路灯边缘计算模型.首先,在BILSTM的基础上,融合布谷鸟算法、通道注意力机制,构建CS-ECA-BILSTM能见度预测模型,实现道路能见度预测;其次,为普通路灯控制因子单一的问题引入照度和色温因子,提高控制效率并降低路灯能耗;最后,在边缘端引入云原生理念,使用KubeEdge框架与容器技术部署路灯控制模型到边缘端,从而实现多路灯控制.实验结果表明,所提出CS-ECA-BILSTM模型的性能优于其他对比模型,可有效提高路灯能源利用率,且能实现在边缘端的运行.
Keyword ：
双向长短期记忆神经网络 双向长短期记忆神经网络 容器技术 容器技术 智慧路灯 智慧路灯 注意力机制 注意力机制 边缘计算 边缘计算
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| GB/T 7714 | 郭泽鑫 , 林培杰 , 程树英 et al. 基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (6) : 667-673 . |
| MLA | 郭泽鑫 et al. "基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型" . | 福州大学学报（自然科学版） 52 . 6 (2024) : 667-673 . |
| APA | 郭泽鑫 , 林培杰 , 程树英 , 陈志聪 , 吴丽君 . 基于CS-ECA-BILSTM与KubeEdge的自适应智慧路灯边缘计算模型 . | 福州大学学报（自然科学版） , 2024 , 52 (6) , 667-673 . |
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为提高光伏发电的预测精确度,提出一种结合主成分分析(PCA)、双通道注意力(CSA)机制和Informer的短期光伏发电量预测新模型.采用Spearman相关分析方法对光伏发电的多元时间序列进行分析,并结合PCA提取时序特征,构建输入数据集.同时,引入CSA机制模块,提取光伏发电历史数据的时间维度和空间维度的特征,然后输入Informer模型进行预测.采用以 30 min为分辨率的光伏电站公开数据集进行实验验证和对比分析.实验结果表明,本研究所提出的预测模型在 4 步预测中的平均绝对误差为 0.061 5,均方误差为0.0205,均方根误差为 0.1435,R2 为 0.9872,均优于其他比较模型,有望为光伏短期发电量预测提供更好的预测精确度.
Keyword ：
Informer模型 Informer模型 主成分分析 主成分分析 光伏发电预测 光伏发电预测 双通道注意力机制 双通道注意力机制 短期发电量 短期发电量
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| GB/T 7714 | 蔡伟雄 , 陈志聪 , 吴丽君 et al. 采用PCA-CSA-Informer模型的光伏短期发电量预测 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (6) : 681-690 . |
| MLA | 蔡伟雄 et al. "采用PCA-CSA-Informer模型的光伏短期发电量预测" . | 福州大学学报（自然科学版） 52 . 6 (2024) : 681-690 . |
| APA | 蔡伟雄 , 陈志聪 , 吴丽君 , 程树英 , 林培杰 . 采用PCA-CSA-Informer模型的光伏短期发电量预测 . | 福州大学学报（自然科学版） , 2024 , 52 (6) , 681-690 . |
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Bruising is one of the key factors that causes postharvest losses, which decreases the economic efficiency of fruit. Nevertheless, the detection of bruises still relies mainly on manual work, which is strongly subjective with long labor time and low efficiency. Accordingly, it is necessary to design an efficient fruit bruise detection approach. Thermal imaging (TI) is a fast and effective nondestructive testing technology. However, the commonly applied thermal excitation TI-based bruise detection may lead to a decrease in the shelf life of the fruit. Therefore, this study uses apple as the research object, introduces cold excitation to improve the sensitivity of bruise detection, and then constructs a simple longwavelength infrared range (7.5-13 mu m) TI system to acquire the thermal image of bruised apples. In addition, the low signal-to-noise ratio of thermal images also leads to detection performance degradation. Thus, the YOLOv5s network is applied and improved to achieve better detection. The specific methods are described as follows: (1) Since the thermal images have the problem of duplicated RGB data, group convolution is used to reduce the feature duplication computation. (2) The bottleneck structure of YOLOv5s is replaced by the ghost bottleneck (GB), and the number of bottlenecks is reduced to decrease the computational quantity of extracting redundant features of thermal images. (3) The shrinkage module is inserted into the GB, and the threshold is automatically obtained through two fully connected layers without relevant professional knowledge to eliminate noise in the features that may cause performance degradation. The F2 score, mAP and mAP50 of the proposed model are 97.76%, 86.24% and 98.08%, respectively, which are better than those of YOLOv5s. Moreover, the computation and the FPS of the proposed model are 1.31 GFLOPs and 160, which are 31.95% and 121.21% of those of the YOLOv5s, respectively.
Keyword ：
Apple Apple Bruise detection Bruise detection Cold excitation Cold excitation Thermal imaging Thermal imaging YOLOv5s YOLOv5s
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| GB/T 7714 | Lin, Peijie , Yang, Hua , Cheng, Shuying et al. An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images [J]. | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
| MLA | Lin, Peijie et al. "An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images" . | POSTHARVEST BIOLOGY AND TECHNOLOGY 199 (2023) . |
| APA | Lin, Peijie , Yang, Hua , Cheng, Shuying , Guo, Feng , Wang, Lijin , Lin, Yaohai . An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images . | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2023 , 199 . |
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As photovoltaic (PV) arrays are exposed to the outdoors year-round, they are susceptible to various faults. The shading condition, degradation or dust coverage can make fault signals more complex, forming compound faults. These faults can lead to a large loss of power generation or irreversible damage to the PV modules, and even fires in severe cases. Moreover, unknown fault types that have never been seen in the training set may occur at actual working conditions. Therefore, accurate diagnosis of various types of single and compound faults (closed-set faults) by considering the identification of unknown faults, namely open-set faults diagnosis, is crucial to improve the efficiency of operation and maintenance. A 1D VoVNet-SVDD based open-set fault diagnosis model for PV arrays is proposed. The model is a two-stage network model consisting of a 1D VoVNet network and a multi-classification Support Vector Data Description (SVDD) in series. The 1D VoVNet network automatically extracts fault features from the input original I-V curve data. These extracted fault features are then combined with environmental parameters to construct the SVDD model. The SVDD identifies known fault types by con-structing a hypersphere for each fault type. Fault types that are not classified into any of the hyperspheres are considered as unknown faults, enabling open-set diagnosis. The experimental results show that the proposed model can accurately classify the closed-set faults among the three designed testing tasks while identify unknown type faults. The comparison demonstrates that the proposed algorithm is superior to the compared models.
Keyword ：
Compound faults Compound faults Deep learning Deep learning Fault diagnosis Fault diagnosis Open -set Open -set Photovoltaic arrays Photovoltaic arrays
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| GB/T 7714 | Lin, Peijie , Guo, Feng , Lu, Xiaoyang et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults [J]. | SOLAR ENERGY , 2023 , 267 . |
| MLA | Lin, Peijie et al. "A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults" . | SOLAR ENERGY 267 (2023) . |
| APA | Lin, Peijie , Guo, Feng , Lu, Xiaoyang , Zheng, Qianying , Cheng, Shuying , Lin, Yaohai et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults . | SOLAR ENERGY , 2023 , 267 . |
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Accurate faults diagnosis for photovoltaic (PV) array is one of the vital factors that guarantee the reliable operation of PV power plant. Artificial intelligence (AI) based fault detection and diagnosis (FDD) models are promising techniques. In order to automatically extract the faults features from the raw electrical data of PV array and create efficient FDD model with small dataset, a FDD scheme using Wasserstein generative adversarial network (WGAN) and convolutional neural network (CNN) is designed. The proposed FDD model is consisting of three modules, a discriminator, a generator and a classifier for fault diagnosis. By analyzing sequential PV data in a 2-Dimension way, the proposed discriminator and generator learn the distribution of PV data under various PV system operations. Then they are utilized to generate more labeled samples to improve the performance of the CNN based classifier. Thus, the proposed FDD model can be trained only requiring minor labeled samples. A laboratory grid-connected PV system is established to experimentally investigate the performance of the developed method. The results demonstrate that the designed FDD model can accurately diagnose line-line and open circuit faults.
Keyword ：
Convolutional Neural Network Convolutional Neural Network Deep Learning Deep Learning Faults Diagnosis Faults Diagnosis Generative Adversarial Network Generative Adversarial Network Photovoltaic Array Photovoltaic Array
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| GB/T 7714 | Lu, Xiaoyang , Lin, Yaohai , Lin, Peijie et al. Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset [J]. | SOLAR ENERGY , 2023 , 253 : 360-374 . |
| MLA | Lu, Xiaoyang et al. "Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset" . | SOLAR ENERGY 253 (2023) : 360-374 . |
| APA | Lu, Xiaoyang , Lin, Yaohai , Lin, Peijie , He, Xiangjian , Fang, Gengfa , Cheng, Shuying et al. Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset . | SOLAR ENERGY , 2023 , 253 , 360-374 . |
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本发明涉及一种基于长短期记忆神经网络的智慧路灯控制方法。从气象网站获取路灯所在地的湿度、风速、PM2.5、PM10等气象数据，并利用照度传感器采集照度信息，以此作为智慧路灯控制的样本数据集；对每个样本信号进行归一化处理；调用长短期记忆神经网络算法，以湿度、风速、PM2.5、PM10作为模型的输入特征，能见度作为模型的输出，构建能见度检测算法模型；结合能见度检测算法模型所得的能见度情况与当前的照度情况构建路灯控制策略；根据所构建的路灯控制策略，在高能见度时，采用普通亮度与高色温照明模式，节约能源；在低能见度时输出更高的亮度与更低的色温，增强路灯透雾能力。本发明能够实现不同能见度下的路灯自适应调光。
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| GB/T 7714 | 林培杰 , 程树英 , 章杰 et al. 一种基于长短期记忆神经网络的智慧路灯控制方法 : CN202210682896.0[P]. | 2022-06-15 00:00:00 . |
| MLA | 林培杰 et al. "一种基于长短期记忆神经网络的智慧路灯控制方法" : CN202210682896.0. | 2022-06-15 00:00:00 . |
| APA | 林培杰 , 程树英 , 章杰 , 郑伟彬 , 陈志聪 , 吴丽君 et al. 一种基于长短期记忆神经网络的智慧路灯控制方法 : CN202210682896.0. | 2022-06-15 00:00:00 . |
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