During the actual operation of the solid oxide fuel cell (SOFC), degradation is one of the most difficult technical problems to overcome. Predicting the degradation trend and estimating the remaining useful life (RUL) can effectively diagnose the potential failure and prolong the useful life of the fuel cell. To study the degradation trend of the SOFC under constant load conditions, a SOFC degradation model based on the ohmic area specific resistance (ASR) is presented first in this paper. Based on this model, a particle filter (PF) algorithm is proposed to predict the long-term degradation trend of the SOFC. The prediction performance of the PF is compared with that of the Kalman filter, which shows that the proposed algorithm is equipped with better accuracy and superiority. Furthermore, the RUL of the SOFC is estimated by using the obtained degradation prediction data. The results show that the model-based RUL estimation method has high accuracy, while the excellence of the PF algorithm for degradation trend prediction and RUL estimation is proven.

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure’s convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%.

To improve suitability in applications with high dynamic performance requirements, the transient response characteristics of high-power piezoelectric transducers should be studied quantitatively. This paper proposes the vector reduction method to solve the complex transient equations and obtains a transient matching scheme clarifying the mechanism of electrical matching resistance on electromechanical damping. A matching scheme with a combination of full-bridge inverter, transformer and series LC circuit is designed and validated, which can provide suitable electrical damping without causing energy losses. Consequently, the experiment verifies the transient properties of the proposed scheme. For a typical piezoelectric cutting transducer with 100.8 ms response time, our scheme is verified to have high dynamic performance within frequency response time of 5.5 ms and vibration response time of 15.0 ms.

A dual-resonant ultrasonic motor is designed with an independent dual-oscillator configuration. The performance of this motor depends on both the structural parameters of the dual oscillators and the vibration mode combinations (i.e., vibration mode matching) between the stator and rotor. However, due to the relatively complex theoretical model of the dual-oscillator ultrasonic motor, two key performance indicators—maximum output torque and maximum speed—exhibit a mutually exclusive relationship. This poses significant challenges to the accurate characterization and optimization of the motor’s performance. To address this issue, this study first adopts a back propagation (BP) neural network fitting approach to establish the complex functional relationship between the optimization parameters and performance objectives, thereby achieving a scientific fitting of the motor’s theoretical model. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II)—well-suited for handling mutually exclusive multi-objective optimization problems—is employed to optimize the motor’s performance. Finally, based on the obtained Pareto optimal solution set, the oscillator’s structural parameters that meet the performance requirements are determined.

In order to facilitate the widespread application of ultrasonic motors, it is essential to conduct a quantitative study aimed at enhancing their performance. The present paper provides a comprehensive theoretical analysis of an ultrasonic motor equipped with dual vibrators, enabling operation in both the single-driven and dual-driven modes, thereby enhancing versatility in terms of performance adjustment. This study provides a detailed examination of the motor’s unique performance characteristics and its varying output responses to different driving signals. Experimental investigations are conducted in both the single-driven and dual-driven modes to validate theoretical predictions. The results demonstrate that the motor exhibits a maximum speed, torque, and power that are 1.59, 1.28, and 1.62 times higher than those of the single-driven stator, respectively. A conclusion can be drawn that the motor will attain the desired performance when operated in the appropriate driven mode.

To address the limited adaptability of fixed-parameter PID controllers under dynamically varying reference signals and the strong dependence of data-driven PID methods on training-data quality, this paper proposes a GRU-based online PID gain-scheduling framework supported by high-quality dataset construction. Diverse reference excitations are first designed, and sequential quadratic programming (SQP) is used as an expert label generator to produce trajectory-level PID gain labels. A region-of-interest (ROI)-based dynamic sample selection strategy is then introduced to retain informative transient samples and reduce the dominance of redundant steady-state data. The gated recurrent unit (GRU) network learns a temporal mapping from error-state sequences to PID gains and is deployed online with closed-loop safeguards, including filtered derivative information, gain denormalization, smoothing, and actuator constraints. In a representative nominal neural-controller benchmark, GRU-PID achieves a rise time of 0.59 s, a settling time of 0.97 s, ISE = 2.10, ITAE = 39.35, and TV = 394.48, showing a favourable balance between tracking accuracy and control-signal smoothness. Five-seed tests further indicate that GRU-PID provides stable nominal performance comparable to competitive neural schedulers, while simulation-based robustness evaluations suggest lower tracking errors than the tested neural baselines under measurement noise, step disturbance, actuator saturation, and combined uncertainty scenarios within the considered benchmark setting.