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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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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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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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在电气工程专业实践教学过程中凝练出由Turn up、Turn away、Turn back等环节组成的3-Turns教学法。经过多轮的实践应用，3-Turns教学法在实践共同体中提供了一个兼具解释性与可操作性的教学样例。研究发现，3-Turns教学法对实践共同体促进工科生深度学习起到了提质增效的作用；3-Turns教学法为工程实践学习情境的强化设计提供了理论依据与经验支持。该研究验证了基于3-Turns教学法的实践共同体促进工科生深度学习的有效性，为助力工科人才培养质量的持续提升做出了积极的探索与实践。
Keyword ：
3-Turns教学法 3-Turns教学法 实践共同体 实践共同体 实践教学 实践教学 深度学习 深度学习
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| GB/T 7714 | 陈东毅 , 王武 , 林建新 et al. 3-Turns教学法在实践共同体促进深度学习中的应用 [J]. | 实验室研究与探索 , 2024 , 43 (04) : 151-154 . |
| MLA | 陈东毅 et al. "3-Turns教学法在实践共同体促进深度学习中的应用" . | 实验室研究与探索 43 . 04 (2024) : 151-154 . |
| APA | 陈东毅 , 王武 , 林建新 , 黄捷 , 陈建国 . 3-Turns教学法在实践共同体促进深度学习中的应用 . | 实验室研究与探索 , 2024 , 43 (04) , 151-154 . |
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