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Objective: This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine (TCM) Master XIONG Jibo in diagnosing and treating arthralgia syndrome (AS) through data mining. Methods: Data of outpatient cases of Professor XIONG Jibo were collected from January 1, 2014 to December 31, 2018, along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor: XIONG Jibo's Clinical Medical Record 1, which was published in December 2019. The five variables collected from the patients’ data were TCM diagnostic information, TCM and western medicine diagnoses, syndrome, treatment, and prescription. A database was established for the collected data with Excel. Using the Python environment, a customized modified natural language processing (NLP) model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud. Frequency analysis, association rule analysis, cluster analysis, and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform (V3.0) and RStudio (V4.0.3). Results: A total of 610 medical records of Professor XIONG Jibo were collected from the case database. A total of 103 medical records were included after data screening criteria, which comprised 187 times (45 kinds) of prescriptions and 1 506 times (125 kinds) of Chinese herbs. The main related meridians were the liver, spleen, and kidney meridians. The properties of Chinese herbs used most were mainly warm, flat, and cold, while the flavors of herbs were mainly bitter, pungent, and sweet. The main patterns of AS included the damp heat, phlegm stasis, and neck arthralgia. The most commonly used herbs for AS were Chuanniuxi (Cyathulae Radix), Huangbo (Phellodendri Chinensis Cortex), Cangzhu (Atractylodis Rhizoma), Qinjiao (Gentianae Macrophyllae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangqi (Astragali Radix), and Chuanxiong (Chuanxiong Rhizoma). The most common effect of the herbs was “promoting blood circulation and removing blood stasis”, followed by “supplementing deficiency (Qi supplementing, blood supplementing, and Yang supplementing)”, and “dispelling wind and dampness”. The data were analyzed with the support ≥ 15% and confidence = 100%, and after de-duplication, five second-order association rules, 39 third-order association rules, 39 fourth-order association rules, and two fifth-order association rules were identified. The top-ranking association rules of each were “Cangzhu (Atractylodis Rhizoma) → Huangbo (Phellodendri Chinensis Cortex)” “Cangzhu (Atractylodis Rhizoma) + Chuanniuxi (Cyathulae Radix) → Huangbo (Phellodendri Chinensis Cortex)” “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) + Gancao (Glycyrrhizae Radix et Rhizoma) → Qinjiao (Gentianae Macrophyllae Radix)” and “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) +Gancao (Glycyrrhizae Radix et Rhizoma) + Huangbo (Phellodendri Chinensis Cortex) → Qinjiao (Gentianae Macrophyllae Radix)”, respectively. Five clusters were obtained using cluster analysis of the top 30 herbs. The herbs were mainly drying dampness, supplementing Qi, and promoting blood circulation. The main prescriptions of AS were Ermiao San (二妙散), Gegen Jianghuang San (葛根姜黄散), and Huangqi Chongteng Yin (黄芪虫藤饮). The herbs of core prescription included Cangzhu (Atractylodis Rhizoma), Chuanniuxi (Cyathulae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangbo (Phellodendri Chinensis Cortex), Mugua (Chaenomelis Fructus), Qinjiao (Gentianae Macrophyllae Radix), Danggui (Angelicae Sinensis Radix), and Yiyiren (Coicis Semen). Conclusion: Clearing heat and dampness, relieving collaterals and pain, and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo. Additionally, customized NLP model could improve the efficiency of data mining in TCM.

This article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller. [ABSTRACT FROM AUTHOR]

Parametric uncertainties and unmodeled disturbances limit the tracking performance improvement of DC motor systems. In this paper, a third-order continuous adaptive integral recursive terminal sliding mode (3-AIRTSM) control scheme is formulated for high-performance motion control of DC motors. The residual parameter uncertainties and unmodeled disturbances are synthesised as the lumped uncertainties. Then, the derivative of the lumped uncertainties is analysed whose upper bound is denoted by system state and control signal dependent terms with unknown coefficients. Meanwhile, a novel third-order sliding mode structure including integral element is constructed to obtain the chattering-free control input. In the proposed controller, the equivalent control term functions as feedforward model compensation, and the reaching control input handles the lumped uncertainties with adaptive gain coefficients, which reduces the design conservation and avoids the high-gain feedback issue. The closed-loop system stability analysis indicates that both tracking error and gain estimation errors converge to zero in finite time under the proposed controller. Comparative experimental results verify the proposed 3-AIRTSM controller can realize the best tracking performance with the modest or the lowest control effort, compared with 3-IRTSM and 3-RTSM controllers. [ABSTRACT FROM AUTHOR]

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions. [ABSTRACT FROM AUTHOR]

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high‐performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three‐axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high‐gain feedback issue in RISE feedback control will be avoided. The proposed RISE‐based neural network robust controller theoretically guarantees an excellent semi‐global asymptotic stability. Comparative simulation is performed on a 3‐DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]