With the development of power-electronics technology, PWM rectifiers are widely used in many fields. They are especially suitable for high-precision- and high-efficiency-application scenarios, such as communication and industrial control. Although it has many advantages, overshoot control during startup is a challenge. Therefore, this paper first derives the input–output mathematical model through equation establishment and dq-coordinate transformation. Next, a voltage and current double closed-loop simulation model is established in MATLAB/Simulink, and its control effect is analyzed. On this basis, a new voltage-setting strategy is designed to reduce the output-voltage overshoot of the PWM rectifier when starting. The simulation results demonstrate that the strategy has a good application effect and can contain starting-voltage overshooting from 12.88% to 0.925%. Moreover, the principle of this strategy is simpler and easier to understand and implement compared with traditional strategies.

The motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision.

This paper proposes a five-degree-of-freedom (5-DOF) hybrid magnetic bearing (HMB) for direct-drive wind turbines, which can realize suspension in the 4-DOF radial and 1-DOF axial directions. Only two sets of radial control windings are employed in the proposed HMB because only one set of radial control windings can achieve the 2-DOF suspension in the radial direction. Unlike the traditional active thrust magnetic bearings, this paper uses a cylindrical rotor core without a large thrust disc in the novel HMB. The numbers of the controller, power amplifier and system volume can be reduced in the magnetic suspension system. This paper also presents the structure and basic characteristics of the proposed magnetic bearing. A precision equivalent magnetic circuit analysis of the permanent magnet ring and control magnetic field is conducted in this study, in consideration of the non-uniform distribution of magnetic density. Accordingly, the mathematical models, including the suspension force expression, are derived based on the accurate equivalent magnetic circuit. The basic principle of the structure parameter design is presented, based on the given key parameters. The accuracy of the analytical method is further validated by 3D finite element analysis.

In order to decouple traditional levitation windings and armature windings, a new selfdecoupling magnetic levitation generator (SDMLG) is proposed for wind turbines. This new generator adopts double-stator structure. The armature windings are in the outer stator, and the levitation windings are in the inner stator. The rotor is made of a distributed hollow structure, so that it can effectively decouple the levitation subsystem and armature subsystem. The new structure and operating principle of the generator are presented in this paper. Then the expressions of levitation forces are deduced by analyzing magnetic flux distributions and winding flux linkages. Finite-element analysis method (FEA) is used as the tool for analyzing the performance of the new generator. And the results verify that the levitation windings and armature windings are effectively decoupled.

The growing demand for meat products has made quality and safety assessment a priority throughout production, processing, storage, distribution, and consumption. Meat quality encompasses sensory attributes, physicochemical properties, nutritional composition, microbial status, freshness, and safety-related characteristics. Conventional sensory, physicochemical, and microbiological methods provide reliable reference measurements, but they are generally time-consuming, labor-intensive, destructive, and unsuitable for real-time or online inspection. This review summarizes recent advances in non-destructive technologies for meat quality assessment, including spectroscopy, imaging and machine vision, odor and taste sensing, ultrasonic and electrical methods, low-field nuclear magnetic resonance, and multi-source information fusion. After outlining the fundamental principles and representative applications of each technology, this review further compares the types of sensing information they provide, the quality attributes they are suitable for assessing, their application scenarios, practical limitations, and model validation requirements. Current evidence indicates that these technologies have considerable potential for rapid screening, process monitoring, quality visualization, and intelligent evaluation. However, practical deployment still requires reliable reference measurements, representative calibration datasets, robust models, external validation, and standardization across instruments and production conditions. Future research should prioritize sensor miniaturization, calibration transfer, standardized datasets, explainable intelligent evaluation, and practically deployable multi-source fusion.

Kiwifruit softens rapidly after harvest, and its marketable quality depends on firmness, soluble solids, dry matter, acidity, mechanical damage, disease, aroma and other attributes that determine grading, storage and sales. Traditional physicochemical tests are accurate but usually destructive, time-consuming and unsuitable for batch sorting, online monitoring or dynamic quality management. This review summarizes six non-destructive testing approaches: optical, acoustic, electromagnetic, dielectric, mechanical/texture-property and electronic nose technologies. Their principles, system configurations, quality indicators, modeling strategies, applications and limitations are compared, with particular attention to validation design, model transferability, approximate technology maturity and industrial constraints. Current evidence shows that near-infrared spectroscopy and hyperspectral imaging are most widely used for soluble solids, firmness, dry matter, chilling injury and early damage detection; machine vision is effective for appearance grading and surface defects; acoustic and mechanical methods mainly support firmness and texture evaluation; dielectric and electromagnetic techniques help characterize moisture, tissue structure and internal disorders; and electronic noses capture volatile signals related to ripening and spoilage. Future work should prioritize cross-variety, cross-origin and cross-device model transfer, multi-source sensor fusion, interpretable modeling, portable instruments, online deployment and standardized validation.