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.

To investigate the effect of interlaminar properties on the tensile properties of fiber hybrid composites, two kinds of epoxy resins with different toughness, 7901 and 9A16, were used as the matrix.Interlayer carbon/glass hybrid composites with different numbers of carbon fiber layers were designed and manufactured.The effects of mode Ⅱ interlaminar fracture toughness(G_ⅡC)on the failure mode and mechanical properties of carbon/glass hybrid composites were investigated through both theoretical and experimental investigation.The results show that, the higher mode Ⅱ interlaminar fracture toughness is, the more the carbon layer tends to fail in fragmentation, achieving a higher critical thickness for fragmentation, which is beneficial for achieving pseudo-ductility.In addition, the G_ⅡC on the modulus and strength of hybrid composites is marginal, as the variation is within 5%.However, the G_ⅡCdemonstrates a significant impact on the pseudo-ductility strain, which is decreased by 40.7% when the G_ⅡC is increased from 1.75 N/mm to 2.08 N/mm.

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%.

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.

AI chips face on-chip memory limits in deep learning.Current optimization methods focus on static computation graphs, leaving room to improve memory efficiency for dynamic graphs.To overcome this limitation, a memory optimization framework for control-flow computation graphs was developed.The framework realized operator-level memory reuse within subgraphs and further achieved recursive reuse across subgraphs by exploiting control-flow characteristics.In addition, a ping-pong buffering strategy for weight data was introduced to mitigate the memory wall between on-chip and off-chip memory, thereby allowing overlapping of memory access and computation operations within subgraphs.Validation on the domestic LUNA AI chip has demonstrated that the proposed framework improves on-chip memory utilization by 5.9% compared with existing methods.Moreover, the strategy effectively alleviates the memory wall problem by reducing data transfer time between on-chip and off-chip memory, resulting in execution efficiency improvements of up to 29%.

For the new network communication challenges of efficient data interaction between components in open interactive environments, a novel C2N(computing and control network)was proposed.Aiming at the extreme requirements for efficiency, real-time performance, flexibility, and security, C2N adopts intelligent and simplified designs in protocol architecture, planning, application, and security design, providing high-performance and highly flexible basic network support for strong real-time collaborative fusion among heterogeneous resources.Based on a detailed investigation of relevant research work, key technologies of C2N were discussed, such as data link layer enhancement, remote direct memory access for sensor-controllers, and service-oriented sensing and control middleware.It also introduced the key technology research and test evaluation carried out by the network chip and system team of the National University of Defense Technology, and prospected future challenges and research directions to help China gain leading advantages in high-end equipment systems and innovative ecosystems.

Power semiconductor modules are the core energy conversion units in power converters.By optimizing their design, the power density can be significantly enhanced.However, current design methods lack systematic summaries.To address this, a systematic summary across four levels(material, chip, packaging and drive)was presented.This included utilizing wide bandgap materials, enhancing chip structure, adopting advanced packaging and improving gate drive design.The underlying principles behind these methods for increasing power density were summarized, and classified and compared the existing research on improving the power density of converters based on power semiconductor module design.The primary challenges in current research were combed, and the future development trend was forecasted.

To improve the design performance of long-range guided rockets, a multidisciplinary parametric model of long-range guided rockets was first established to achieve high-precision performance simulation of guided rockets.A sequence approximation optimization method based on an improved augmented radial basis function was proposed, which enhanced the generalization ability of the augmented radial basis function model through anisotropic techniques.Recursive evolution experimental design and fast cross-validation were used to improve the efficiency of approximation modeling, and an imprecise search strategy was applied for sequence sampling.The effectiveness of the proposed optimization method was verified through numerical examples.A sequence approximate optimization design of the long-range guided rocket was carried out, and the maximum range increase by 16.7% compared to before optimization while satisfying design constraints.

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.

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.