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学者姓名：姚立纲
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Abstract ：
Accurately estimating the battery's state of health (SOH) is critical for battery efficiency and stability. Despite significant progress in data-driven methods, the accuracy of these models is limited by feature extraction strategies and the scarcity of dataset samples. To address this issue, this study develops a battery SOH estimation model tailored to the limited sample conditions. A refined composite multiscale discrete sine entropy (RCMDSE) algorithm is proposed, which combines composite multiscale approaches, Shannon entropy theory, and the discrete sine transform. This algorithm is designed to extract high-quality battery entropy domain health features (HFs) from current and voltage signals at various scales and levels. Subsequently, we introduce semi-supervised learning concepts to enhance the estimation performance of the nu-support vector regression (NuSVR) algorithm in limited sample conditions. The golden jackal optimization algorithm (GJO) is used to improve the estimation accuracy of the NuSVR algorithm in a semi-supervised framework. Comparative and ablation experiments on four datasets validate that the battery SOH estimation model maintains RMSE and MAPE values of © 2024 Elsevier Ltd
Keyword ：
Self-supervised learning Self-supervised learning
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| GB/T 7714 | Liu, Yaming , Ding, Jiaxin , Cai, Yingjie et al. A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions [J]. | Journal of Energy Storage , 2025 , 106 . |
| MLA | Liu, Yaming et al. "A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions" . | Journal of Energy Storage 106 (2025) . |
| APA | Liu, Yaming , Ding, Jiaxin , Cai, Yingjie , Luo, Biaolin , Yao, Ligang , Wang, Zhenya . A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions . | Journal of Energy Storage , 2025 , 106 . |
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Entropy theories play a significant role in rotating machinery fault detection. The key parameters of these methods are, however, often selected subjectively based on trial-and-error methods or engineering experience. Unsuitable parameters would result in an inconsistency between the extracted entropy results and the realistic case. In order to address this issue, a complexity measurement method called "swarm intelligence optimization entropy" (SIOE) is proposed, which adaptively estimates optimal parameters using skewness metrics, logistic chaos theory, and African vulture optimization (AVO). By considering the variability and dynamic changes of various signals, SIOE enables the extraction of robust and discriminative dynamic features. Additionally, a collaborative intelligent fault detection method for rotating machinery fault detection is developed, based on SIOE and extreme gradient boosting (XGBoost). This method aims to accurately identify single faults, compound faults, and varying fault degrees within the rotating machinery. Simulation and fault detection experiments on rotating machines demonstrate that SIOE improves recognition accuracy by up to 21.25% compared to existing entropy methods. The proposed intelligent fault detection method improves recognition accuracy by up to 15.71% compared to advanced fault detection methods. These results highlight the advantages of SIOE in complexity measurement and feature extraction, as well as the effectiveness and accuracy of the proposed intelligent fault detection method, in identifying rotating machinery faults.
Keyword ：
Accuracy Accuracy Aerodynamics Aerodynamics Complexity theory Complexity theory Entropy Entropy Extreme gradient boosting (XGBoost) Extreme gradient boosting (XGBoost) fault detection fault detection Fault detection Fault detection feature extraction feature extraction Feature extraction Feature extraction Fluctuations Fluctuations Machinery Machinery Particle swarm optimization Particle swarm optimization rotating machinery rotating machinery swarm intelligence optimization entropy (SIOE) swarm intelligence optimization entropy (SIOE) Vibrations Vibrations
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| GB/T 7714 | Wang, Zhenya , Yao, Ligang , Li, Minglin et al. A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Wang, Zhenya et al. "A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Wang, Zhenya , Yao, Ligang , Li, Minglin , Chen, Meng , Zhao, Jingshan , Chu, Fulei et al. A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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The wearable A-mode ultrasound human-machine interface technology (HMI-A) is a promising sensing modality, with many researchers achieving good results in strictly controlled experimental environments. However, the instability of A-mode ultrasound signals makes gesture recognition technology associated with HMI-A difficult to apply in practical scenarios, and the anatomical variability of the forearm is a major factor contributing to the decrease in gesture recognition performance. Additionally, long-term application can lead to forearm posture changes and probe displacement, causing signal drift. If the distribution of signal data between the training set and the test set is inconsistent, the performance of the trained model on the test set will be poor. Addressing the above issues, this article makes three contributions: 1) a thorough investigation of forearm posture changes, including pronation, supination, flexion, and extension, and their impact on HMI-A gesture recognition performance; 2) proposing an unmarked calibration algorithm based on quantitative analysis to help users reposition the forearm after long-term use; and 3) introducing a domain-adversarial neural network (DANN) to mitigate the impact of signal drift on recognition performance. Through five interval experiments with eight subjects, the long-term gesture recognition performance of the combination of repositioning and DANN methods was validated. The average recognition accuracy (RA) of each experiment increased from 58.81% +/- 3.61% to 89.17% +/- 1.72%, with one subject's RA improving by 60.2%. This study confirms the feasibility of using ultrasound sensing technology for long-term muscle tissue-related applications.
Keyword ：
Accuracy Accuracy A-mode ultrasound A-mode ultrasound domain-adversarial neural network (DANN) domain-adversarial neural network (DANN) Electrodes Electrodes Gesture recognition Gesture recognition long-term gesture recognition long-term gesture recognition Muscles Muscles Thumb Thumb Training Training transfer learning transfer learning Ultrasonic imaging Ultrasonic imaging wearable ultrasound wearable ultrasound
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| GB/T 7714 | Shangguan, Qican , Lian, Yue , Cai, Shaoxiong et al. DANN-Repositing Strategy for Zero Retraining Long-Term Hand Gesture Recognition Using Wearable A-Mode Ultrasound [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Shangguan, Qican et al. "DANN-Repositing Strategy for Zero Retraining Long-Term Hand Gesture Recognition Using Wearable A-Mode Ultrasound" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Shangguan, Qican , Lian, Yue , Cai, Shaoxiong , Wu, Jun , Yao, Ligang , Lu, Zongxing . DANN-Repositing Strategy for Zero Retraining Long-Term Hand Gesture Recognition Using Wearable A-Mode Ultrasound . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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柔性作业车间调度问题不仅要安排工序的加工顺序，还要选择当前工序所使用的机器，是一类灵活性和复杂性较高的NP(non-deterministic polynomial)-hard问题。以最小化最大完工时间、最小化总机器负荷、最小化最大机器负荷为目标，建立多目标优化模型，将非占优排序融入交叉熵算法，提出求解多目标柔性作业车间调度问题的交叉熵方法(cross-entropy method for multi-objective optimization,CEMO)，以“随机分布筛”处理工序排列约束函数，确保采样点的可行性并提高收敛速率。对CEMO的机理分析表明，该方法可以利用非占优排序所得精英样本的引导作用，使收敛速度比应用交叉熵方法求解单目标问题更快。同时，针对最大完工时间优化时易出现的早熟现象，提出基于总机器负荷和最大机器负荷的机器分配预训练技术及采样矩阵提前停止更新技术，促进精英样本的进化。最后，通过数值实验验证了CEMO的机理，结果表明该方法可行，且具有收敛快、解的分布更广更均匀的优点。
Keyword ：
交叉熵算法 交叉熵算法 多目标优化 多目标优化 柔性作业车间调度 柔性作业车间调度 非占优排序 非占优排序
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| GB/T 7714 | 杨艳华 , 潘鑫 , 张丽丽 et al. 基于交叉熵算法求解多目标柔性作业车间调度问题 [J]. | 武汉大学学报(工学版) , 2024 , 57 (04) : 497-508 . |
| MLA | 杨艳华 et al. "基于交叉熵算法求解多目标柔性作业车间调度问题" . | 武汉大学学报(工学版) 57 . 04 (2024) : 497-508 . |
| APA | 杨艳华 , 潘鑫 , 张丽丽 , 姚立纲 . 基于交叉熵算法求解多目标柔性作业车间调度问题 . | 武汉大学学报(工学版) , 2024 , 57 (04) , 497-508 . |
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啮合温度影响双圆弧弧齿锥齿轮章动传动过程中的疲劳、振动、噪声等行为，在双圆弧弧齿锥齿轮的寿命预测和力学响应时不可忽视。为了揭示双圆弧弧齿锥齿轮章动传动本体温度场分布规律，对双圆弧弧齿锥齿轮进行齿面接触分析，得到啮合区间；结合摩擦学和传热学得到齿轮的热载荷，建立了双圆弧弧齿锥齿轮温度有限元数值模型；采用控制变量法，分析了相关影响因素对锥齿轮温度的影响规律及原因。结果表明，齿轮温度随模数的增大而降低，随螺旋角的增大而升高，随章动角的增大先降低再升高；润滑油温度对本体温度的影响呈线性关系。研究为降低齿轮高温失效风险提供了参考。
Keyword ：
双圆弧弧齿锥齿轮 双圆弧弧齿锥齿轮 影响因素 影响因素 本体温度 本体温度 章动传动 章动传动
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| GB/T 7714 | 陈颖 , 姚立纲 , 王兴盛 et al. 双圆弧弧齿锥齿轮章动传动啮合齿轮本体温度的相关影响因素分析 [J]. | 机械传动 , 2024 , 48 (02) : 10-16 . |
| MLA | 陈颖 et al. "双圆弧弧齿锥齿轮章动传动啮合齿轮本体温度的相关影响因素分析" . | 机械传动 48 . 02 (2024) : 10-16 . |
| APA | 陈颖 , 姚立纲 , 王兴盛 , 张大卫 , 王雪滢 . 双圆弧弧齿锥齿轮章动传动啮合齿轮本体温度的相关影响因素分析 . | 机械传动 , 2024 , 48 (02) , 10-16 . |
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Automated of gas and liquid classification technologies are of great in multiple fields including food production and human healthcare. Of these, fruit juice contains water, organic acids, minerals and other nutrients which offers a pleasant taste and promotes healthy condition. However, the main challenges faced by conventional components sensing technologies for juice classification are limited to the complexity of experimental preparation, bulky instrument, high consumption and susceptibility to contamination. Moisture Electricity Generation (MEG) technology has made it feasible to acquire energy from trace amounts of water or environmental humidity. This work proposes a novel sensing unit based on MEG technology. The unit mainly comprises non-woven fabric, hydroxylated carbon nanotubes, polyvinyl alcohol, a solution of sea salt and liquid alloy. By this approach, humid air (relative humidity 60%), pure water and juices from three fruits (lemon, kiwifruit, and clementine) have been successfully classified in 15 seconds. The classification accuracy can reach 90%. Electrical signals standard lines highlight the specific response between samples. The relative standard deviation of stable output section is 1.6% and the root-mean-square error between test data and the standard curve is less than 0.08, which indicates the stability, accuracy are fine. Besides, the sensing unit demonstrates an acceptable reusability. The presented approach may provide opportunities to improve sensing paradigms in industrial and medical settings.
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| GB/T 7714 | Lin, Jiawen , Dong, Hui , Yang, Jintian et al. A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism [J]. | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS , 2024 : 76-80 . |
| MLA | Lin, Jiawen et al. "A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism" . | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS (2024) : 76-80 . |
| APA | Lin, Jiawen , Dong, Hui , Yang, Jintian , Jia, Haichao , Li, Minglin , Yao, Ligang et al. A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism . | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS , 2024 , 76-80 . |
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Cardiovascular disease is becoming the leading cause of human mortality. In order to address this, flexible continuum robots have emerged as a promising solution for miniaturizing and automating vascular interventional equipment for diagnosing and treating cardiovascular diseases. However, existing continuum robots used for vascular intervention face challenges such as large cross-sectional sizes, inadequate driving force, and lack of navigation control, preventing them from accessing cerebral blood vessels or capillaries for medical procedures. Additionally, the complex manufacturing process and high cost of soft continuum robots hinder their widespread clinical application. In this study, we propose a thermally drawn-based microtubule soft continuum robot that overcomes these limitations. The proposed robot has cross-sectional dimensions several orders of magnitude smaller than the smallest commercially available conduits, and it can be manufactured without any length restrictions. By utilizing a driving strategy based on liquid kinetic energy advancement and external magnetic field for steering, the robot can easily navigate within blood vessels and accurately reach the site of the lesion. This innovation holds the potential to achieve controlled navigation of the robot throughout the entire blood vessel, enabling in situ diagnosis and treatment of cardiovascular diseases.
Keyword ：
cardiovascular cardiovascular magnetic driving magnetic driving microtubule soft continuum robot microtubule soft continuum robot submillimeter submillimeter thermal drawing thermal drawing
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| GB/T 7714 | Wang, Xufeng , Liu, Wei , Luo, Qinzhou et al. Thermally Drawn-Based Microtubule Soft Continuum Robot for Cardiovascular Intervention [J]. | ACS APPLIED MATERIALS & INTERFACES , 2024 , 16 (23) : 29783-29792 . |
| MLA | Wang, Xufeng et al. "Thermally Drawn-Based Microtubule Soft Continuum Robot for Cardiovascular Intervention" . | ACS APPLIED MATERIALS & INTERFACES 16 . 23 (2024) : 29783-29792 . |
| APA | Wang, Xufeng , Liu, Wei , Luo, Qinzhou , Yao, Ligang , Wei, Fanan . Thermally Drawn-Based Microtubule Soft Continuum Robot for Cardiovascular Intervention . | ACS APPLIED MATERIALS & INTERFACES , 2024 , 16 (23) , 29783-29792 . |
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The hand gesture recognition (HGR) technology in A-mode ultrasound human-machine interface (HMI-A), based on traditional machine learning, relies on intricate feature reduction methods. Researchers need prior knowledge and multiple validations to achieve the optimal combination of features and machine learning algorithms. Furthermore, anatomical differences in the forearm muscles among different subjects prevent specific subject models from applying to unknown subjects, necessitating repetitive retraining. This increases users' time costs and limits the real-world application of HMI-A. Hence, this article presents a lightweight 1-D four-branch squeeze-to-excitation convolutional neural network (CNN) (4-branch SENet) that outperforms traditional machine learning methods in both feature extraction and gesture classification. Building upon this, a weight fine-tuning strategy using transfer learning enables rapid gesture recognition across subjects and time. Comparative analysis indicates that the freeze feature and fine-tuning fully connected (FC) layers result in an average accuracy of 96.35% +/- 3.04% and an average runtime of 4.8 +/- 0.15 s, making it 52.9% faster than subject-specific models. This method further extends the application scenarios of HMI-A in fields such as medical rehabilitation and intelligent prosthetics.
Keyword ：
A-mode ultrasound A-mode ultrasound convolutional neural network (CNN) convolutional neural network (CNN) Convolutional neural networks Convolutional neural networks deep learning deep learning Feature extraction Feature extraction Gesture recognition Gesture recognition hand gesture recognition (HGR) hand gesture recognition (HGR) human-machine interaction (HMI) human-machine interaction (HMI) Muscles Muscles Sensors Sensors transfer learning transfer learning Transfer learning Transfer learning Ultrasonic imaging Ultrasonic imaging
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| GB/T 7714 | Lian, Yue , Lu, Zongxing , Huang, Xin et al. A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (10) : 17183-17192 . |
| MLA | Lian, Yue et al. "A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound" . | IEEE SENSORS JOURNAL 24 . 10 (2024) : 17183-17192 . |
| APA | Lian, Yue , Lu, Zongxing , Huang, Xin , Shangguan, Qican , Yao, Ligang , Huang, Jie et al. A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound . | IEEE SENSORS JOURNAL , 2024 , 24 (10) , 17183-17192 . |
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The development of intelligent rehabilitation robots has greatly reduced the workload of rehabilitation physicians. Human-machine interaction (HMI) control methods are a critical technology for intelligent rehabilitation robots. Therefore, we systematically review the HMI methods and control strategies for upper and lower limb rehabilitation robots and summarizing the HMI methods with different sensors. The integration of rehabilitation robots and HMI control methods has grown significantly in recent years. For this reason, this article takes the sensing methods as the entry point to give readers a quick overview of the current status of HMI research. We present different sensing methods, interactive control strategies, applications, and evaluation methods and discuss the limitations and future development directions in the field. The results show that the mainstream control methods of HMI are based on motion signals, surface electromyography (sEMG), ultrasound (US), and electroencephalogram (EEG). In the field of rehabilitation robotics, human intention recognition-based interaction strategy is the mainstream HMI strategy, which mainly collects biosignals, force/moment, spatial angle, and other information for human intention recognition. Future research may focus on the use of multimodal sensing interactions, flexible control strategies, and generalized rehabilitation assessment mechanism.
Keyword ：
Control strategies Control strategies human intention recognition human intention recognition human-machine interaction (HMI) human-machine interaction (HMI) rehabilitation robot rehabilitation robot sensing methods sensing methods
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| GB/T 7714 | Lu Zongxing , Zhang Jie , Yao Ligang et al. The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (9) : 13773-13787 . |
| MLA | Lu Zongxing et al. "The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review" . | IEEE SENSORS JOURNAL 24 . 9 (2024) : 13773-13787 . |
| APA | Lu Zongxing , Zhang Jie , Yao Ligang , Chen Jinshui , Luo Hongbin . The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review . | IEEE SENSORS JOURNAL , 2024 , 24 (9) , 13773-13787 . |
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基于弧齿锥齿轮的复杂齿面设计和实际啮合情况,提出一种利用齿廓成型刀具的齿廓建模方法,能有效实现弧齿锥齿轮齿廓的精确建模.以弧齿锥齿轮为主要建模对象,提出自动化建模及装配方法,开发一种参数化设计系统软件.该软件能够实现弧齿锥齿轮的自动化快速建模和装配,缩短建模时间,提高设计质量与效率,有助于推动产业数字化转型,发展高端装备制造业.
Keyword ：
参数化设计 参数化设计 工业软件 工业软件 弧齿锥齿轮 弧齿锥齿轮 自动化建模及装配 自动化建模及装配
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| GB/T 7714 | 姚立纲 , 黄思捷 , 贾超 et al. 弧齿锥齿轮齿面建模和参数化设计系统软件开发 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 560-568 . |
| MLA | 姚立纲 et al. "弧齿锥齿轮齿面建模和参数化设计系统软件开发" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 560-568 . |
| APA | 姚立纲 , 黄思捷 , 贾超 , 丁嘉鑫 , 蔡英杰 . 弧齿锥齿轮齿面建模和参数化设计系统软件开发 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 560-568 . |
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