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.

To further optimize the hardware offloading of collective communication based on the network interface card in the "Tianhe" network, and to support more types of collective communication algorithms and larger message sizes, the order-preserving triggering mechanism and data buffering method for collective communication hardware offloading was investigated.An order-preserving triggering mechanism for concurrent multitasking was proposed, which meets the desired semantics of collective communication and ensures the reproducibility of floating-point computation results.A dynamic network data buffering method based on Hash tables and pulsed credit flow control was proposed to alleviate the contradiction between limited hardware buffering resources and the high demand for buffering a large amount of network data from concurrent multitasking.Experimental results show that compared with software-based collective communication operations, this method can support the hardware offloading of various algorithms for several typical collective communication operations, with significant performance improvement. Meanwhile, the hardware implementation cost is low, especially with high utilization of buffering resources.

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 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.

Particle transport simulations using stochastic methods face significant challenges on conventional von Neumann architectures, particularly due to random branching events and irregular memory access patterns.These limitations stem from the fundamental mismatch between probabilistic algorithms and deterministic computing paradigms.To bridge the gap between architecture and algorithms, a probabilistically tunable true random number generator was developed based on spintronic and ferroelectric devices.The physical randomness of spintronic devices was leveraged to provide a physical random source for the architecture, and the throughput of random bits was enhanced through optimized control logic and writing mechanisms.Next, programmable synapses were designed based on the memristive properties of ferroelectric devices, enabling nonvolatile continuous weight storage with tunable probabilities.The experimental results indicate that the proposed approach achieves performance improvements ranging from 171 to 1028 times compared to a general-purpose CPU when solving a sample transport problem.Furthermore, compared to existing spin-transfer torque magnetic tunnel junction based true random number generators, the developed method not only enables tunable probability random sampling but also achieves a throughput of 303 Mbit/s when generating uniformly distributed random sequences.

Combining software defined networking and SR(segment routing)can optimize network performance, but in large-scale dynamic networks, excessive link utilization at key nodes can lead to a surge in queue delays.To address this, a SROD-LC(segment routing optimization algorithm based on deep reinforcement learning and load centrality theory)was proposed.By quantifying the importance of network nodes using load centrality theory, key nodes are identified and their link load states are monitored;utilizing a multi-agent reinforcement learning framework, distributed deep reinforcement learning agents are deployed at key nodes, coordinating routing decisions through a shared reward mechanism to achieve proactive optimization of link loads.At the same time, leveraging the flexibility of SR, segment identifier lists are dynamically adjusted to quickly reroute partial traffic, reducing local link utilization and avoiding potential congestion.Simulation experiments based on real network topologies show that when the proportion of SR key nodes is in the range of 0.3~0.5, the SROD-LC algorithm exhibits significant optimization effects, reducing the network′s maximum link utilization by 21%~35% compared to baseline algorithms.

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.