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学者姓名：柴琴琴
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Abstract ：
In this paper, a resilient adaptive covariance Kalman filter is developed for state estimation under false data injection attack (FDIA) during the process of measurements transmission. The extreme measurement deviation caused by unknown injection vectors is clipped by an adaptive saturation function, and an adaptive noise covariance matrix triggered by prediction residual is constructed to enhance the estimation performance and stability of the filtering error system under FDIA. To analyze the asymptotic convergence of the algorithm, the error expression is constructed to analyze the upper limit of prediction error. Finally, a simulation experiment on an inverted pendulum car verifies the stability and effectiveness of the proposed method in reducing the impact of unknown attack vectors.
Keyword ：
adaptive covariance adaptive covariance false data injection attack false data injection attack Kalman filter Kalman filter saturation function saturation function
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| GB/T 7714 | Zhang, Xiaoyun , Chai, Qinqin , Wang, Wu . Resilient adaptive covariance Kalman filter for state estimation under false data injection attacks [J]. | ASIAN JOURNAL OF CONTROL , 2025 . |
| MLA | Zhang, Xiaoyun 等. "Resilient adaptive covariance Kalman filter for state estimation under false data injection attacks" . | ASIAN JOURNAL OF CONTROL (2025) . |
| APA | Zhang, Xiaoyun , Chai, Qinqin , Wang, Wu . Resilient adaptive covariance Kalman filter for state estimation under false data injection attacks . | ASIAN JOURNAL OF CONTROL , 2025 . |
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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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The cascaded H-bridge inverter exhibits the characteristics of high voltage, large capacity and low harmonic distortion, and has a vital impact on application fields such as battery energy storage and photovoltaic power generation. Fault diagnosis of inverter switches is essential to enhancing equipment dependability. However, the limited number of fault samples and severe overlap of fault signals in real-word applications present difficulties for inverter fault diagnosis. In view of this, this paper introduces a hierarchical classification fault diagnosis strategy founded on an improved siamese network to achieve high-precision fault diagnosis. Firstly, for the purpose of addressing the issues of multiple fault categories and limited samples, an improved Siamese network based on long short-term memory and attention mechanism is proposed to extract more subtle fault difference features, thereby improving the recognition accuracy of overlapping fault classes. Then, to solve the problem of serious overlap of fault samples of different types in the preliminary grouping, a hierarchical fault diagnosis model is proposed to realize high precision fault diagnosis. Finally, the fault data of the cascaded H-bridge inverter was obtained through the semi-physical simulation platform to complete the diagnosis experiment. The experimental results demonstrate the recommended model offers clear benefits in terms of diagnostic accuracy when compared to the conventional model. © 2025 Institute of Physics Publishing. All rights reserved.
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| GB/T 7714 | Lin, Bin , Chai, Qinqin , Wang, Wu . Fault Diagnosis of Cascade H-bridge Inverter Based on Siamese Network under Small Sample Condition [C] . 2025 . |
| MLA | Lin, Bin 等. "Fault Diagnosis of Cascade H-bridge Inverter Based on Siamese Network under Small Sample Condition" . (2025) . |
| APA | Lin, Bin , Chai, Qinqin , Wang, Wu . Fault Diagnosis of Cascade H-bridge Inverter Based on Siamese Network under Small Sample Condition . (2025) . |
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Aiming at the problem that a large number of electric vehicles randomly connected to the grid poses a huge challenge to the security of the power grid, this paper proposes a strategy to guide the orderly charging of electric vehicles by using the time-of-use electricity price policy. Firstly, an orderly charging scheduling model for electric vehicles taking into account the response level to the policy is constructed. Then, a hybrid algorithm combining Spider Wasp Optimization (SWO) and Particle Swarm Optimization (PSO) is used to optimize the peak-valley electricity price period. Finally, by using the Monte Carlo and probability statistics theory methods to simulate the daily charging load of electric vehicles, the experiment of different response level are carried out. And results of different optimization methods for solving the scheduling model are compared. Comparison results show that the proposed method achieves the smallest peak to valley difference with the lest iterations. The proposed method can provides an effective strategy for peak shaving and valley filling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Mathematical programming Mathematical programming Monte Carlo methods Monte Carlo methods Particle swarm optimization (PSO) Particle swarm optimization (PSO)
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| GB/T 7714 | Lai, Qianling , Chai, Qinqin , Wang, Wu . Orderly Charging Optimization Scheduling for Electric Vehicles Based on Improved Spider Wasp Optimization [C] . 2025 : 118-126 . |
| MLA | Lai, Qianling 等. "Orderly Charging Optimization Scheduling for Electric Vehicles Based on Improved Spider Wasp Optimization" . (2025) : 118-126 . |
| APA | Lai, Qianling , Chai, Qinqin , Wang, Wu . Orderly Charging Optimization Scheduling for Electric Vehicles Based on Improved Spider Wasp Optimization . (2025) : 118-126 . |
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Solving the problem of water level fluctuations in small hydropower station systems is challenging under traditional industrial control methods. This difficulty arises from the system’s high nonlinearity and the complexities involved in mechanism modeling. To address this, an improved neuro-fuzzy approach is proposed. In which, the multi-head attention mechanism based long short-term memory network is used to describe complex water level change patterns, and the fuzzy controller is introduced to dynamically adjust the control parameters to reduce water level fluctuation. Simulation-based on real hydropower station system data is carried out, and the superiority of the improved model under complex dynamic conditions is verified by comparing the prediction accuracy of different neural network methods and the effects of fuzzy controller and traditional PID control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Control nonlinearities Control nonlinearities Fuzzy neural networks Fuzzy neural networks Proportional control systems Proportional control systems Three term control systems Three term control systems Two term control systems Two term control systems
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| GB/T 7714 | Yang, Rongguo , Chai, Qinqin , Cai, Fenghuang et al. Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach [C] . 2025 : 469-478 . |
| MLA | Yang, Rongguo et al. "Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach" . (2025) : 469-478 . |
| APA | Yang, Rongguo , Chai, Qinqin , Cai, Fenghuang , Wang, Wu . Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach . (2025) : 469-478 . |
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Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is one of the most precious Chinese medicine with extraordinary effects in medical treatment and health protection. Planting and tissue-cultured are two main cultivated methods of A. roxburghii. There are slight characteristic differences between Planting and tissue-cultured A. roxburghii, but they show significant differences in medicinal and market value. Therefore, the identification of cultivated methods plays an important role in effectively securing the medicinal efficacy of A. roxburghii and maintaining a good market order. However, due to the influence of composite differences such as different cultivars, different geographical origins and different times of cultivation, the difficulty and complexity of identification in cultivated methods increase heavily. This paper proposes an effective model to discriminative different cultivated methods of A. roxburghii based on improved 1D-inception-CNN. The experiments were conducted on two kinds of A. roxburghii, and their NIRS data were collected by a Fourier transform near-infrared spectrometer. Considering the unbalanced proportion of planting and tissue-cultured samples,the NIRS data was over sampled by using SMOTE first. Secondly, a one-dimensional convolutional neural network based on improved Inception was constructed to identify planting and tissue-cultured A. roxburghii though both include different varieties, different geographical origins and different cultivating times. Finally, Bayesian optimization was used to optimize the hyperparameters of the model. The final average identification accuracy, precision, recall, and F1-score of five-fold crossvalidation reached 97.95%, 96.16%, 100%, and 98.02%. The identification model proposed in this experiment provides a useful method to identify planting and tissue-cultured A. roxburghii effectively and rapidly and provides an idea for the identification of cultivation methods of other Chinese herbal medicines.
Keyword ：
Anoectochilus roxburghii Anoectochilus roxburghii Bayesian optimization Bayesian optimization Inception module Inception module One-dimensional convolutional neural network One-dimensional convolutional neural network SMOTE SMOTE
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| GB/T 7714 | Lan Yan , Wang Wu , Xu Wen et al. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN [J]. | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (1) : 158-163 . |
| MLA | Lan Yan et al. "Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN" . | SPECTROSCOPY AND SPECTRAL ANALYSIS 44 . 1 (2024) : 158-163 . |
| APA | Lan Yan , Wang Wu , Xu Wen , Chai Qin-qin , Li Yu-rong , Zhang Xun . Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN . | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (1) , 158-163 . |
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Advanced wind power prediction technique plays an essential role in the stable operation of the grid with largescale grid integration of wind power. Most research focuses on distance-based static classification where the subjective nature of initial center selection increases the uncertainty of the prediction. And the data classification on a daily basis neglects the potentially significant climate changes at smaller time scales. To address these issues, the improved snake optimization-long short-term memory (ISO-LSTM) model with Gaussian mixture model (GMM) clustering is proposed to forecast wind power from an adaptive perspective. By exploiting the merits of the probabilistic classification, the K-means optimized GMM clustering enables an appropriate feature modelling for substantial climate changes at smaller time scales. Then the ISO algorithm exhibits higher search accuracy and is better suited for finding hyperparameter combinations for LSTM neural networks. The data from the National Aeronautics and Space Administration (NASA) of the US is used to validate the effectiveness of the proposed method. Compared to the traditional K-means clustering, the K-means optimized GMM clustering has increased accuracy by 2.63 %. Simultaneously, with the adoption of the enhanced ISO algorithm, the accuracy further increases by 7.27 %. Different existing models have also been tested; it shows that the proposed model demonstrates higher prediction accuracy.
Keyword ：
Gaussian mixture model Gaussian mixture model Improved snake optimization Improved snake optimization K -means algorithm K -means algorithm Long short-term memory network Long short-term memory network Probabilistic classification Probabilistic classification
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| GB/T 7714 | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin et al. Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| MLA | Zhou, Yu et al. "Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 157 (2024) . |
| APA | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin , Chai, Qinqin , Wang, Wu . Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
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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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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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Accurate estimation of the state of health (SOH) of lithium-ion batteries is an important guarantee to ensure safe and reliable operation of lithium-ion battery systems. However, the complex aging mechanism inside the battery makes it difficult to measure the battery SOH directly. In this paper, a SOH estimation method based on a novel dual-stage attention-based recurrent neural network (DARNN) and health feature (HF) extraction from time varying charging process is proposed. Firstly, the constant current charging time, the maximum temperature time, the isochronous voltage difference, and the isochronous current were extracted as lithium-ion battery HFs, and their correlations with SOH are verified by spearman correlation coefficient. Secondly, the DARNN is proposed to capture the time-dependent and temporal features of the input sequence and to accurately predict SOH. Finally, the proposed estimation method is validated on the NASA battery dataset. The results show that the method can accurately estimate SOH for lithium-ion batteries. The mean square error and the mean absolute percentage error of the method are <0.5 %.
Keyword ：
Dual -stage attention -based recurrent neural Dual -stage attention -based recurrent neural Lithium-ion battery Lithium-ion battery network network State of health State of health
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| GB/T 7714 | Hong, Jiangnan , Chen, Yucheng , Chai, Qinqin et al. State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network [J]. | JOURNAL OF ENERGY STORAGE , 2023 , 72 . |
| MLA | Hong, Jiangnan et al. "State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network" . | JOURNAL OF ENERGY STORAGE 72 (2023) . |
| APA | Hong, Jiangnan , Chen, Yucheng , Chai, Qinqin , Lin, Qiongbin , Wang, Wu . State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network . | JOURNAL OF ENERGY STORAGE , 2023 , 72 . |
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