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学者姓名：柴琴琴
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针对光伏电池板安装位置复杂,不利于人工统计的特点,文中设计了一款基于改进YOLOv5的光伏电池数量无人机检测系统,通过无人机拍摄光伏板电池图像并进行数量统计,从而计算区域光伏电池组发电量.文中采用ECA注意力机制对MobileNetV3网络进行改进,使用改进后的网络替换YOLOv5的骨干网络,并引入GSConv模块进一步替换YOLOv5的标准卷积,通过Alpha-EIOU对训练损失函数进行优化,提高模型性能.实验证明,文中提出的模型相比YOLOv5具有更高的检测精度和更快的检测速度.
Keyword ：
ECA ECA MobileNetV3 MobileNetV3 YOLOv5 YOLOv5 光伏电池板 光伏电池板 无人机 无人机
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| GB/T 7714 | 黄子乐 , 张家翔 , 柴琴琴 . 基于改进YOLOv5算法的光伏电池数量检测系统 [J]. | 信息技术 , 2025 , (1) : 28-32,39 . |
| MLA | 黄子乐 等. "基于改进YOLOv5算法的光伏电池数量检测系统" . | 信息技术 1 (2025) : 28-32,39 . |
| APA | 黄子乐 , 张家翔 , 柴琴琴 . 基于改进YOLOv5算法的光伏电池数量检测系统 . | 信息技术 , 2025 , (1) , 28-32,39 . |
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Anoectochilus roxburghii from different origins has different nutritional content and different price. Achieving origin identification is of great significance to the development and standardization of the Anoectochilus roxburghii industry However, it is influenced by complex factors, including the specific strain and growth environment. Achieving a high accuracy in origin identification presents significant challenges and may not always meet the stringent requirements. To account for the unique characteristics of Anoectochilus roxburghii dataset from different origins, such as limited sample size, imbalanced samples, and numerous sample interference factors, a method based on improved SMOTE and CatBoost is designed to address the need for precise origin identification. First, a Fourier transform near infrared spectrometer was used to collect sample information of Anoectochilus roxburghii from three different origins, and then the improved SMOTE algorithm was used to balance the dataset. Finally, CatBoost classifier was used to identify the different origins. Comparative experimental results show that the method proposed in this article has the highest identification accuracy, reaching more than 97%, which is 6.9% and 2.8% higher than using original data and the original SMOTE algorithm respectively. The model constructed can efficiently identify Anoectochilus roxburghii of different origins and can be served as a useful reference for quality supervision of Anoectochilus roxburghii. © 2024 IEEE.
Keyword ：
Anoectochilus roxburghii Anoectochilus roxburghii CatBoost CatBoost Near infrared spectroscopy Near infrared spectroscopy SMOTE SMOTE
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| GB/T 7714 | Wen, P. , Chai, Q. , Wang, W. . Identification of Anoectochilus Roxburghii Origins Based on Imbalanced Dataset [未知]. |
| MLA | Wen, P. 等. "Identification of Anoectochilus Roxburghii Origins Based on Imbalanced Dataset" [未知]. |
| APA | Wen, P. , Chai, Q. , Wang, W. . Identification of Anoectochilus Roxburghii Origins Based on Imbalanced Dataset [未知]. |
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Fault diagnosis of the power devices in inverters is crucial for improving equipment reliability. However, the signal fluctuations caused by load variations during actual operation pose new challenges for inverter fault diagnosis. Existing data-driven fault diagnosis methods are designed based on specific system fault databases, making it difficult to overcome the influence of system parameter changes. In addition, existing transfer learning methods for variable working conditions often require a large amount of unlabeled target domain data for model training. In addition, the application is limited by the sample size of the new working conditions. To tackle these challenges, this paper presents a novel approach for diagnosing open-circuit faults in three-phase inverters by leveraging transfer learning. In this approach, the output voltage of different three-phase inverter loads is used as the fault signal. Then a one-dimensional convolutional neural network integrating attention mechanisms and global average pooling layers is introduced to effectively capture the channel and spatial features of fault characteristics. Next, a domain adversarial neural network is employed to enable the diagnostic model to learn domain-invariant features, so that the target domain and source domain cannot be distinguished. Thus, the model built on the source domain can adapt to changing working conditions. Finally, by utilizing an iterative pseudo-labeling method to train the model, high-precision diagnostic outcomes are achieved and a limited number of labeled samples from the target domain are needed. Experimental results show that the proposed method achieves an average diagnostic accuracy of 96.63% in transfer diagnosis tasks across different systems, and exhibits robustness in environments with various types of noise.
Keyword ：
Domain adaptation Domain adaptation Fault diagnosis Fault diagnosis One-dimensional convolutional neural networks One-dimensional convolutional neural networks Pseudo-label Pseudo-label Three-phase inverter Three-phase inverter Transfer learning Transfer learning
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| GB/T 7714 | Chai, Qinqin , Li, Haodong , Wang, Wu et al. Transfer learning based open-circuit fault diagnosis method for three-phase inverters [J]. | JOURNAL OF POWER ELECTRONICS , 2024 . |
| MLA | Chai, Qinqin et al. "Transfer learning based open-circuit fault diagnosis method for three-phase inverters" . | JOURNAL OF POWER ELECTRONICS (2024) . |
| APA | Chai, Qinqin , Li, Haodong , Wang, Wu , Yan, Qibin . Transfer learning based open-circuit fault diagnosis method for three-phase inverters . | JOURNAL OF POWER ELECTRONICS , 2024 . |
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The inverter is one of the most important components in photovoltaic and wind power generation systems, and its stability is crucial to the smooth operation of the system. Power devices are the most fragile components in the inverter. Fault diagnosis and timely processing can greatly improve the reliability of the power generation system. Existing data-driven fault diagnosis methods are designed based on fixed working conditions. Once the system parameters change, the diagnosis accuracy will significantly decrease. To solve these problems, this study proposes a three-phase inverter open circuit fault diagnosis method based on domain adversarial neural network. This method selects the three-phase inverter phase voltage as the input signal, improves the convolutional neural network through the Inception structure, and then uses the domain adversarial neural network to learn domain invariant features. Finally, the diagnosis results are obtained based on the output of the fault classifier. Experimental results show that in transfer diagnosis tasks across different systems, the method achieves an average diagnosis accuracy of 95.01% and exhibits robustness in various noisy environments. © 2024 IEEE.
Keyword ：
Domain adversarial neural network Domain adversarial neural network fault diagnosis fault diagnosis three-phase inverter three-phase inverter transfer learning transfer learning
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| GB/T 7714 | Li, H. , Chai, Q. , Wang, W. et al. Three-phase Inverter Open Circuit Cross-Domain Fault Detection Based on Inception-DANN [未知]. |
| MLA | Li, H. et al. "Three-phase Inverter Open Circuit Cross-Domain Fault Detection Based on Inception-DANN" [未知]. |
| APA | Li, H. , Chai, Q. , Wang, W. , Yan, Q. . Three-phase Inverter Open Circuit Cross-Domain Fault Detection Based on Inception-DANN [未知]. |
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针对传统化学需氧量(chemical oxygen demand, COD)检测存在检测成本高、耗时、易造成二次污染，以及现有检测模型泛化性较差等不足，难以满足水环境实时监测需求的问题，提出基于近红外光谱技术的COD快速无损定量预测模型.实验结果表明，本模型在污水COD光谱数据集上的预测性能，相较于传统机器学习算法和现有其他深度学习算法更优.测试的决定系数(R~2)和均方根误差(E_(RMS))分别达到0.992 1和27.47 mg·L~(-1),模型卷积层的输出特征可解释性强，能有效表征关键波长点.该预测模型为实际水体COD含量快速检测提供一种新的方法.
Keyword ：
一维卷积神经网络 一维卷积神经网络 化学需氧量 化学需氧量 定量预测模型 定量预测模型 实时监测 实时监测 水环境 水环境 近红外光谱 近红外光谱
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| GB/T 7714 | 范日高 , 王武 , 郑芝芳 et al. 近红外光谱的水体污染指标COD定量预测模型 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (02) : 228-235 . |
| MLA | 范日高 et al. "近红外光谱的水体污染指标COD定量预测模型" . | 福州大学学报(自然科学版) 52 . 02 (2024) : 228-235 . |
| APA | 范日高 , 王武 , 郑芝芳 , 柴琴琴 . 近红外光谱的水体污染指标COD定量预测模型 . | 福州大学学报(自然科学版) , 2024 , 52 (02) , 228-235 . |
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该文以双有源桥DC-DC变换器为对象，详细分析了变换器在三重移相调制下的两种数学模型，又基于这两种数学模型提出了对应的电感电流优化控制策略，搭建了包含MATLAB/Simulink仿真平台、StarSim半实物仿真平台和实物平台的三阶段实验平台，在平台中通过实验验证了优化控制策略的可行性和有效性。所设计的三阶段开发流程能够加深学生对电力电子领域相关知识的理解，逐步提升学生的理论分析能力、仿真验证能力和实践操作能力。
Keyword ：
仿真 仿真 优化控制策略 优化控制策略 半实物仿真 半实物仿真 双有源桥变换器 双有源桥变换器 实验平台设计 实验平台设计
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| GB/T 7714 | 蔡逢煌 , 龚兴阳 , 柴琴琴 et al. 基于“仿真—半实物仿真—实物”三阶段开发流程的DAB变换器实验探索与研究 [J]. | 实验技术与管理 , 2024 , 41 (05) : 46-53 . |
| MLA | 蔡逢煌 et al. "基于“仿真—半实物仿真—实物”三阶段开发流程的DAB变换器实验探索与研究" . | 实验技术与管理 41 . 05 (2024) : 46-53 . |
| APA | 蔡逢煌 , 龚兴阳 , 柴琴琴 , 王武 . 基于“仿真—半实物仿真—实物”三阶段开发流程的DAB变换器实验探索与研究 . | 实验技术与管理 , 2024 , 41 (05) , 46-53 . |
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The paper proposes an ultra-local model predictive current control strategy to improve the stability and robustness of the phase-shifted full-bridge(PSFB) converter under parameter mismatch causing by operating conditions changes. Firstly, analysis is conducted to show the effect of circuit parameter mismatch on the anticipated current estimate in the PSFB converter circuit. Secondly, an ultra-local model is built for the PSFB converter by analyzing the total disturbance of the circuit. Ultimately, the unknown nonlinear total disturbance part of the model is estimated using a Kalman filter, which is then integrate in model predictive control algorithm to enhance the control effect. The experiments on a semi-physical simulation platform demonstrate that the proposed strategy effectively suppresses the steady-state error caused by parameter mismatch when compared to the conventional predictive current control scheme. It increases system resilience and anti-disturbance capabilities in the event of parameter disturbances, as well as its capacity to adapt efficiently to changes in working conditions. © 2024 IEEE.
Keyword ：
Kalman filter Kalman filter phase-shifted full-bridge converter phase-shifted full-bridge converter predictive current control predictive current control ultra-local model ultra-local model
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| GB/T 7714 | Lin, J. , Wang, W. , Chai, Q. et al. Ultra-local model Predictive Current Control of phase-shifted full-bridge Based on Kalman Filter [未知]. |
| MLA | Lin, J. et al. "Ultra-local model Predictive Current Control of phase-shifted full-bridge Based on Kalman Filter" [未知]. |
| APA | Lin, J. , Wang, W. , Chai, Q. , Sheng, M. . Ultra-local model Predictive Current Control of phase-shifted full-bridge Based on Kalman Filter [未知]. |
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In this paper, a dental symptom detection model based on YOLO is proposed in order to detect different dental symptom in panoramic oral roentgenogram. This model introduces the Global Attention Mechanism into the backbone feature extraction network to obtain rich cross-latitude features and enhance the network's global feature extraction capabilities in low-contrast images. At the same time, the Spatial Pyramid Pooling Fast module in the network is replaced and the Atrous Spatial Pyramid Pooling technology is used to improve the recognition ability of larger targets such as tooth germ. Finally, according to the special structure, size and position of different dental symptoms, the Focal-EIoU is introduced to replace CIoU, which increases the weight proportion of positive samples in the training process and reduces the problem of missed detection or false detection. Experiments on self-built data sets show that the improved YOLO model has improved mAP@0.5 by 4.3% compared to the original model, and the detection effect has been generally improved. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.
Keyword ：
Errors Errors Feature extraction Feature extraction
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| GB/T 7714 | Huang, Yinggui , Chai, Qinqin , Wang, Wu . Modified YOLO network for symptom detection in panoramic oral roentgenogram [C] . 2024 : 7848-7853 . |
| MLA | Huang, Yinggui et al. "Modified YOLO network for symptom detection in panoramic oral roentgenogram" . (2024) : 7848-7853 . |
| APA | Huang, Yinggui , Chai, Qinqin , Wang, Wu . Modified YOLO network for symptom detection in panoramic oral roentgenogram . (2024) : 7848-7853 . |
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金线莲是一种珍贵中药材,其治疗、保健作用十分显著.金线莲培育方式主要有种植、组培等,不同培育方式的金线莲,在性状上仅表现出细微差异,但药用、市场价值差异显著,培育方式鉴别能有效保证药用疗效、维护良好市场秩序,然而由于不同品系、产地、培育时间等复合差异的影响,增加了培育方式鉴别难度与复杂度.提出一种基于改进1D-Inception-CNN模型的金线莲培育方式鉴别方法.采用近红外光谱仪采集种植、组培金线莲的光谱,首先使用合成少数类过采样技术(SMOTE)进行过采样以解决种植品、组培品样本比例不平衡问题,其次构建基于改进Inception结构的一维卷积神经网络对来自不同品系、产地、培育时间的金线莲进行种植品、组培品鉴别,最后采用贝叶斯优化方法对构建的卷积神经网络模型超参数进行优化;最终五折交叉验证平均鉴别准确率、精确率、召回率、综合评价指标高达97.95％、96.16％、100％、98.02％.研究表明,实验提出的鉴别模型为快速鉴别金线莲种植品、组培品提供一种有效方法.
Keyword ：
Inception模块 Inception模块 一维卷积神经网络 一维卷积神经网络 少数类过采样技术 少数类过采样技术 贝叶斯优化 贝叶斯优化 金线莲 金线莲
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| GB/T 7714 | 蓝艳 , 王武 , 许文 et al. 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 [J]. | 光谱学与光谱分析 , 2024 , 44 (1) : 158-163 . |
| MLA | 蓝艳 et al. "基于SMOTE和Inception-CNN的种植和组培金线莲鉴别" . | 光谱学与光谱分析 44 . 1 (2024) : 158-163 . |
| APA | 蓝艳 , 王武 , 许文 , 柴琴琴 , 李玉榕 , 张勋 . 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 . | 光谱学与光谱分析 , 2024 , 44 (1) , 158-163 . |
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针对传统三相电压源逆变器开路故障诊断方法存在准确率低和鲁棒性差的问题，提出一种用于故障诊断的改进二维卷积神经网络优化方法.该方法首先引入一种新的数据预处理方式，通过马尔可夫变迁场(MTF)将原始时域电压信号数据转换成二维灰度图像，有效保留特征的时空关系；其次，提出采用并行注意力机制对卷积神经网络ResNet18特征提取层提取的特征分别进行通道和空间特征筛选，并完成有效特征融合；最后，融合的特征经ResNet18全连接层和输出层得到故障分类结果.实验结果表明，所提出的改进故障诊断方法能将诊断精度提升至99.80%;在不同噪声条件下均能保持90%以上的分类准确性，验证该方法可有效提高逆变器开路故障诊断性能和鲁棒性.
Keyword ：
ResNet18网络 ResNet18网络 开路故障 开路故障 注意力机制 注意力机制 逆变器 逆变器 马尔可夫变迁场 马尔可夫变迁场
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| GB/T 7714 | 谢泽文 , 陈裕成 , 柴琴琴 et al. 改进残差网络的逆变器开路电路故障诊断 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (01) : 45-52 . |
| MLA | 谢泽文 et al. "改进残差网络的逆变器开路电路故障诊断" . | 福州大学学报(自然科学版) 52 . 01 (2024) : 45-52 . |
| APA | 谢泽文 , 陈裕成 , 柴琴琴 , 林琼斌 , 王武 . 改进残差网络的逆变器开路电路故障诊断 . | 福州大学学报(自然科学版) , 2024 , 52 (01) , 45-52 . |
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