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学者姓名：王武
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This paper proposes a leader-follower control method for multiple snake robot formation. Based on the simplified snake robot model, this work improves the traditional Serpenoid gait mode to a time-varying frequency form. Combined with the line-of-sight (LOS) method, a snake robot trajectory tracking controller is designed to enable the leader to track the desired trajectory at the ideal velocity. Then, the leader-follower following error system of a snake robot formation is established. In this framework, the follower can maintain a preset geometric position relationship with the leader to ensure the fast convergence of the formation location. Lyapunov's theory proves the stability of a snake robot formation error. Simulation and experimental results show that this strategy has the advantages of faster convergence speed and higher tracking accuracy than other current methods.
Keyword ：
Formation control Formation control Leader-follower Leader-follower Snake robot Snake robot Trajectory tracking Trajectory tracking
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| GB/T 7714 | Wang, Wu , Du, Zhihang , Li, Dongfang et al. Leader-follower method-based formation control for snake robots [J]. | ISA TRANSACTIONS , 2025 , 156 : 609-619 . |
| MLA | Wang, Wu et al. "Leader-follower method-based formation control for snake robots" . | ISA TRANSACTIONS 156 (2025) : 609-619 . |
| APA | Wang, Wu , Du, Zhihang , Li, Dongfang , Huang, Jie . Leader-follower method-based formation control for snake robots . | ISA TRANSACTIONS , 2025 , 156 , 609-619 . |
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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 et al. "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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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. et al. "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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在工程教育专业认证持续深入推进的背景下，结合电气工程专业特色，对“组态软件技术”课程教学进行重新设计与教学实践。引入高阶思维临场认知对教学内容进行重构，应用课堂多源数据建立教学评测一体化的教学新模式。实践表明该课程的教学设计有着良好的沉浸式体验感，学生主动建构、解决问题的高阶思维能力得到提升，新的教学模式促进了课程教学的高质量发展。
Keyword ：
临场认知 临场认知 教学实践 教学实践 课堂多源数据 课堂多源数据 高阶思维 高阶思维
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| GB/T 7714 | 陈东毅 , 王武 , 林建新 et al. “组态软件技术”课程教学设计与实践 [J]. | 电气电子教学学报 , 2024 , 46 (02) : 19-23 . |
| MLA | 陈东毅 et al. "“组态软件技术”课程教学设计与实践" . | 电气电子教学学报 46 . 02 (2024) : 19-23 . |
| APA | 陈东毅 , 王武 , 林建新 , 崔凤新 . “组态软件技术”课程教学设计与实践 . | 电气电子教学学报 , 2024 , 46 (02) , 19-23 . |
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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 the field of precision manufacturing, error compensation of parts is the key to improve product quality and manufacturing efficiency. This paper presents a Long Short-Term Memory Network (LSTM) model based on the Gray Wolf optimization algorithm designed to optimize part error compensation. First, we introduce the sources of part errors and their impact on the manufacturing process. Then, we elaborate the application of LSTM network in predicting and compensating part errors by selecting appropriate features through correlation analysis. Through experiments, we verify the effectiveness of the Gray Wolf optimization-based LSTM model in part error prediction and compensation. The experimental results show that compared with the traditional method, the model in this paper has a significant improvement in both error prediction accuracy and compensation efficiency.
Keyword ：
Error prediction Error prediction Gray Wolf optimization algorithm Gray Wolf optimization algorithm Long and short-term memory networks Long and short-term memory networks Part error compensation Part error compensation
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| GB/T 7714 | Yang, Chengju , Wang, Wu , Lin, Tao et al. Optimization of LSTM based on Gray Wolf Optimization Algorithm for Part Error Compensation [J]. | 2024 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS TECHNOLOGY AND INTELLIGENT MANUFACTURING, ICMTIM 2024 , 2024 : 773-777 . |
| MLA | Yang, Chengju et al. "Optimization of LSTM based on Gray Wolf Optimization Algorithm for Part Error Compensation" . | 2024 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS TECHNOLOGY AND INTELLIGENT MANUFACTURING, ICMTIM 2024 (2024) : 773-777 . |
| APA | Yang, Chengju , Wang, Wu , Lin, Tao , Zhou, Shen , Zhang, Ling , Huang, Junxiang . Optimization of LSTM based on Gray Wolf Optimization Algorithm for Part Error Compensation . | 2024 5TH INTERNATIONAL CONFERENCE ON MECHATRONICS TECHNOLOGY AND INTELLIGENT MANUFACTURING, ICMTIM 2024 , 2024 , 773-777 . |
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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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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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