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This paper summarized the key points of nursing cooperation for a patient undergoing mixed-reality system guided resection of endometrial malignancy. key points of nursing included preoperative preparation, management of surgical instruments and equipments, preoperative investigation of precision and perioperative nursing cooperation. Based on the establishment of intelligent management system, mixed-reality technology has advantages in postoperative education, postoperative evaluation, preoperative investigation of precision, guidance of intraoperative operation and postoperative clinical teaching. (本文总结1例在混合现实技术引导下行腹腔镜子宫内膜恶性肿瘤切除术的护理配合要点, 包括术前准备、手术物品准备、术中护理配合等。通过构建手术室智能管理平台, 将混合现实技术应用于妇科腹腔镜的术前宣教、术前评估、术前精准讨论、术中操作引导以及术后临床教学, 具有明显优势。)

High-quality Piper laetispicum (Piper laetispicum C. DC) is the key to the development of foods, natural medicines, and cosmetics. Its crude fat, ash, piperine, protein, and aroma compounds were determined in this experiment. Principal component (PCA) and hierarchical cluster analyses (HCA) were used to evaluate the aroma compounds at different developmental stages. The main aroma compounds identified using steam distillation combined with GC-MS were sabinene (34.83–76.14%), α-copaene (5.11–19.51%), linalool (2.42–15.70%), trans-caryophyllene (2.37–6.57%), α-pinene (1.51–4.31%), and germacrene D (1.30–4.10%). The aroma metabolites at different developmental stages were analysed using non-targeted metabolomes, and linalool was found to be the most abundant. Based on the experimental results, there were more nutrient compounds in young Piper laetispicum than in the last three developmental stages. The aromatic metabolites contributed the most to PC1. There were also more different metabolites of aroma between the young and expanding stages. Therefore, regarding quality, young fruits have great potential. [ABSTRACT FROM AUTHOR]

After injection molding, plastic gears often exhibit surface defects, including those on end faces and tooth surfaces. These defects encompass a wide range of types and possess complex characteristics, which pose challenges for inspection. Current visual inspection systems for plastic gears suffer from limitations such as single-category defect inspection and low accuracy. There is an urgent industry need for a comprehensive and accurate method and system for inspecting defects on plastic gears, with improved inspection capability and higher accuracy. This paper presents an intelligent inspection algorithm network for plastic gear defects (PGD-net), which effectively captures subtle defect features at arbitrary locations on the surface compared to other models. An adaptive sample weighting method is proposed and integrated into an improved Focal-IoU loss function to address the issue of low inspection accuracy caused by imbalanced defect dataset distributions, thus enhancing the regression accuracy for difficult defect categories. CoordConv layers are incorporated into each inspection head to improve the model's generalization capability. Furthermore, a dataset of plastic gear surface defects comprising 16 types of defects is constructed, and our algorithm is trained and tested on this dataset. The PGD-net achieves a comprehensive mean average precision (mAP) value of 95.6% for the 16 defect types. Additionally, an online inspection system is developed based on the PGD-net algorithm, which can be integrated with plastic gear production lines to achieve online full inspection and automatic sorting of plastic gear defects. The entire system has been successfully applied in plastic gear production lines, conducting daily inspections of over 60,000 gears. [ABSTRACT FROM AUTHOR]

In this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application. [ABSTRACT FROM AUTHOR]

This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform. [ABSTRACT FROM AUTHOR]

The present work is aimed at studying the effects of the magnetorheological finishing process, using a low-frequency alternating magnetic field, on the finishing performance of 6063 aluminum alloy. The study investigates the influence of key excitation parameters such as current, frequency, excitation gap, and iron powder diameter on the material removal and surface roughness (Ra) of the finished workpiece by experiments. This study employs a single-factor experimental method, and the finish surface is analyzed by a Zigo non-contact white light interferometer. The magnetic field strength in the processing area increases with the increase in the excitation current and decreases with the increase in the excitation gap. When the current frequency is set to 1 Hz, the circulation and renewal of abrasives in the magnetic cluster is most sufficient, resulting in the optimal surface roughness value for the workpiece. According to the experimental results of the excitation parameters, more suitable process parameters were selected for a two-stage finishing experiment. The surface roughness of 6063 aluminum alloy was improved from 285 nm to 3.54 nm. Experimental results highlighted that the magnetorheological finishing using a low-frequency alternating magnetic field is a potential technique for obtaining nano-scale finishing of the 6063 aluminum alloy. [ABSTRACT FROM AUTHOR]

Summary: For the complex time‐varying uncertain nonlinear speed and tension system of reversible cold strip rolling mill, an adaptive sliding mode observers (ASMOs)‐based dissipative Hamilton finite time optimization control method is given in this article. First, the ASMOs are developed to observe rolling mill system's unmatched uncertainties, and their observation errors can converge to the origin in finite time. Second, combined with rolling mill system's physical structure characteristics, its dissipative Hamilton model is established by pre‐feedback processing, and then the dissipative Hamilton finite time controllers are designed based on interconnection and damping configuration and energy shaping method, and the system states can converge in finite time. Third, the adaptive mutation particle swarm optimization algorithm is adopted to optimize the controller parameters, which can further improve rolling mill system's control performance. Finally, the simulation results verify the validity of the given method based on actual field data. [ABSTRACT FROM AUTHOR]

In this paper, fault estimation and active fault‐tolerant control are studied for a class of nonlinear systems with simultaneous actuator and sensor faults, as well as unknown external disturbances. Firstly, the state equation of a class of nonlinear systems is transformed into an augmented system state equation by extending the sensor fault as an auxiliary state. Then, a novel fault estimation observer based on iterative learning with unknown inputs is designed to estimate the system state, as well as actuator and sensor faults. Subsequently, by using the fault estimation information, a dynamic output feedback active fault‐tolerant control scheme is proposed to compensate for the influence of faults on the system. Lyapunov stability theory is used to prove the stability of the closed‐loop system and the convergence of the fault estimation observer. The gain matrices of the fault estimation observer and fault‐tolerant controller are obtained by solving linear matrix inequalities. Furthermore, the paper avoids the use of the λ norm in the convergence proof of the conventional iterative learning algorithm, which reduces the amount of calculation in the derivation process. Finally, the effectiveness and accuracy of the proposed method are verified through simulation of the DC motor angular velocity system. [ABSTRACT FROM AUTHOR]