An ER-MKKNN(enhanced random mixed kernel K-nearest neighbors algorithm)was developed to meet the requirements of base station network traffic prediction in ultra-dense 5 G/6G environments.A hybrid kernel function was formed by combining a radial basis function kernel with a white-noise kernel, thereby overcoming the trade-off between nonlinear relationship modeling and noise suppression that plagues single-kernel methods.Dual random subsampling of both samples and features, together with a randomized hyperparameter-interval strategy, was employed to bolster generalization stability in high-dimensional, sparse settings.A dynamic weight-allocation mechanism based on inversion of out-of-bag errors was introduced to improve robustness against abrupt traffic fluctuations.Finally, a multi-level parallel architecture was implemented to deliver a scalable prediction framework for ultra-dense network topologies.Experimental evaluations show that ER-MKKNN outperformed deep-learning models in root mean square error, mean absolute percentage error and mean absolute error, respectively, establishing a new technical pathway for intelligent network operations and maintenance.

In order to improve the sensitivity of time-frequency system integrity monitoring, a time-frequency system integrity monitoring method based on robust Kalman filter was proposed.In this method, a robust Kalman filter model was constructed using the historical measurement data of time difference, the time difference prediction bias and the frequency bias were estimated in real time, and the consistency detection was carried out separately, so that the integrity monitoring was realized.The model and method were verified through measured data and simulation analysis, and the results show that:this method can effectively detect and identify single faults of phase jump and frequency jump, and alarm the user;in a single fault scenario, compared with the traditional integrity monitoring method, the detection sensitivity is increased by about 25.0%;in a multi-fault scenario, the method can effectively detect faults, but there is a problem of insufficient fault identification, and the detection sensitivity is reduced by about 26.2% compared to a single fault, but it is still better than the traditional method.

To address the challenges of intelligent diagnosis for open-circuit faults in power electronic inverters, such as the lack of actual fault samples and the issue of varying characteristic adaptability, a set of optimization methods was proposed from two key intelligent elements:data and algorithm, to support the practical applications of intelligent diagnosis for open-circuit faults in power electronic inverters.For the data element, a fault sample amplification method based on inverters′characteristics was proposed, which finds out the minimum number of practical samples required for model training.For the algorithm element, an attention-enhanced method and a frequency points adaptive training method for the diagnosis model were proposed, which significantly improve model training effectiveness and diagnosis accuracy under wide-frequency inverter operation.The effectiveness of the proposed optimization methods for the intelligent elements was validated by experiments.

In view of the contradiction between the need of the electromagnetic sled for real-time accurate position and speed information and the limitation or high cost of traditional position and speed measurement methods, a new measurement system based on vernier caliper structure was proposed and designed.The principle of high precision positioning and the corresponding position analysis method was expounded, and the position prediction algorithm and Kalman filter algorithm were designed to improve the accuracy and real-time performance.The hardware circuit and software program were designed to realize the function, and a synchronous belt guide rail experimental platform was built to verify the designed system.The test results show that the system can achieve millimeter-level positioning accuracy, and performs well in terms of real-time capability, accuracy and engineering application.The positioning and speed measurement system was applied to the electromagnetic levitation propulsion platform.

Modular multilevel converters exhibit significant capacitor voltage ripple under low-speed, high-torque operating conditions. Existing high-frequency injection suppression schemes increase device current stress and losses while introducing overmodulation risk, and their parameter optimization lacks full operational-condition adaptability.To resolve this issue, a high-frequency injection parameter adaptive optimization strategy considering multiple constraints was proposed.Based on system characteristics and a steady-state model, a variable-step gradient descent algorithm was employed offline to generate a minimum injection-amplitude base parameter reference table that satisfies both capacitor voltage ripple and modulation wave constraints.Subsequently, an online adaptive correction mechanism was designed.Injection parameters were dynamically adjusted in real-time according to acquired capacitor voltage ripple and modulation information, compensating for model deviations and operational variations, forming a coordinated architecture of offline global optimization and online local refinement.Simulation and experimental results show that the proposed strategy maintains the capacitor voltage ripple suppression effect while significantly reducing high-frequency circulating currents, demonstrating dynamic tracking capability for the optimal objective.

An adaptive active disturbance rejection control strategy integrating DRL(deep reinforcement learning)with enhanced PSO(particle swarm optimization)was presented, aiming to improve the speed and thrust control performance of PMSLMs(permanent magnet synchronous linear motors).A mathematical model of the motor was established to analyze its dynamic characteristics, followed by the design of a DRLPSO control framework.This framework leveraged reward mechanisms in reinforcement learning to interact with the environment, dynamically optimized ADRC(active disturbance rejection controller)parameters to accommodate varying operating conditions and external disturbances.The modified PSO algorithm incorporated partitioned inertia weights and cyclically utilized historical global optimal data to iteratively update control policies, refining neural network weights and thereby enhancing search efficiency and optimization accuracy.Experimental results show that the proposedDRLPSO-ADRCmethod achieves significantly higher tracking precision in position and velocity, along with improved system stability and resistance to thrust disturbances, compared to conventionalPSO-ADRCalgorithms.These findings validate the effectiveness of the innovative control strategy.

For the common stator winding short circuit and rotor eccentricity faults in surface-mounted permanent magnet synchronous motors, a flexible printed circuit board with small footprint and capable of accommodating a large number of windings was used to fabricate the detection coil, which was then arranged in the stator slots to capture magnetic field information.For the stator winding short circuit fault, a winding short circuit detection method using dual orthogonal phase-locked loop to extract fault characteristic values was proposed.This method can effectively distinguish the short circuit resistance, short circuit winding number, and fault location, and was not affected by the motor′s speed fluctuations.For the rotor eccentricity fault, a differential bridge structure of the detection coil based on high-frequency injection was proposed for eccentricity detection, and ultimately, a 2% eccentricity detection can be achieved.For the composite fault, a fault discrimination scheme based on convolutional neural networks was introduced, and the performance of different learning methods was compared.The experimental results show that under the composite fault condition, a 98% correct rate of winding short circuit assessment is achieved, and the eccentricity detection error using AlexNet with a training data proportion of 60% is only 5%.