We propose an electronic model in Spice, instead of traditional mathematical analysis, for analyzing the performance of ferroelectric liquid crystal （FLC） under various working conditions. Using this equivalent circ...We propose an electronic model in Spice, instead of traditional mathematical analysis, for analyzing the performance of ferroelectric liquid crystal （FLC） under various working conditions. Using this equivalent circuit model,it is easy to simulate and analyze the behavior of an FLC layer in three different typical parameters,including temperature, input light wavelength, and the frequency of driving voltage. We conclude that the response velocity drops as the wavelength increases in the range of visible light, and for the parameter of temperature, the velocity reaches its lowest value when the temperature reaches a certain degree,meanwhile,the frequency of driving voltage exerts important effects on the response velocity only when the frequency is beyond a critical value. Excellent agreement is achieved between simulation and experimental results.展开更多
针对断路器机械特性异常诊断中的多源信号采集与分析问题,设计出一套分合闸线圈电流和触头行程信号的采集系统,并提出了相应的异常模拟方案。通过引入分段滤波和循环差分判别进行特征提取,机器学习算法采用CatBoost模型进行单源信号的...针对断路器机械特性异常诊断中的多源信号采集与分析问题,设计出一套分合闸线圈电流和触头行程信号的采集系统,并提出了相应的异常模拟方案。通过引入分段滤波和循环差分判别进行特征提取,机器学习算法采用CatBoost模型进行单源信号的异常诊断,结合遗传算法(genetic algorithm,GA)进行参数优化,实现了基于线圈电流和触头行程的高准确率诊断。同时,利用线性判别分析(linear discriminant analysis,LDA)方法进行特征融合,提升了异常诊断效果。此外,对多种融合方法的诊断结果进行对比分析。结果表明,LDA-GA-CatBoost的特征级融合方法与基于改进的D-S证据理论(dempster-shafer theory of evidence,DST)的决策级融合方法的异常诊断率最高,均为95.82%,但LDA-GA-CatBoost的模型训练时间仅为改进的D-S证据理论的一半,更具有应用优势。展开更多
Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem s...Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem surface code with three-qubit check operators demonstrates significant application potential due to its simplified measurement operations and low logical error rates.However,the existing minimum-weight perfect matching(MWPM)algorithm exhibits high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoder based on a graph attention network(GAT),representing error syndromes as undirected graphs with edge weights,and employing a multihead attention mechanism to efficiently aggregate node features and enable parallel computation.Compared to MWPM,the GAT decoder exhibits linear growth in computational complexity,adapts to different quantum code structures,and demonstrates stronger robustness under high physical error rates.The experimental results demonstrate that the proposed decoder achieves an overall accuracy of 89.95%under various small code lattice sizes(L=2,3,4,5),with the logical error rate threshold increasing to 0.0078,representing an improvement of approximately 13.04%compared to the MWPM decoder.This result significantly outperforms traditional methods,showcasing superior performance under small code lattice sizes and providing a more efficient decoding solution for large-scale quantum error correction.展开更多
中点钳位(neutral point clamped,NPC)型三电平逆变器并网工作环境恶劣,IGBT面临单管与双管同时故障的挑战,这使得故障特征之间的差异变得非常微弱,进而导致双管故障的识别精度难以有效提升。为此,提出了一种新的故障诊断方法,该方法结...中点钳位(neutral point clamped,NPC)型三电平逆变器并网工作环境恶劣,IGBT面临单管与双管同时故障的挑战,这使得故障特征之间的差异变得非常微弱,进而导致双管故障的识别精度难以有效提升。为此,提出了一种新的故障诊断方法,该方法结合了多通道的二维递归融合图和轻量化多尺度残差(lightweightmultiscale convolutional residuals,LMCR)网络。首先,通过仿真获取三相电流信号作为故障信号;再利用递归图(recurrence plot,RP)将三相电流信号分别转化为二维图并进行多通道融合,以捕捉时间序列中的周期性、突变点和趋势等特征;最后,将递归融合图作为输入,输入到LMCR模型中进行故障识别,LMCR模型整合多级Inception结构和残差网络,用于提取不同尺度的特征并融合这些特征,从而保证网络的梯度消失和爆炸。实验结果显示,该方法在IGBT故障识别中表现出色,无噪声环境下平均识别准确率达100%,噪声环境中也达到了92.53%,充分证明了该方法具有较强的特征提取能力和优异的抗噪性能。展开更多
由于开关器件故障是有源中性点箝位(active neutral point clamped,ANPC)三电平逆变器故障的主要类型,快速定位故障器件对提升ANPC三电平逆变器的可靠性具有重要意义。针对ANPC三电平逆变器开关器件开路故障,从传播路径的角度分析了不...由于开关器件故障是有源中性点箝位(active neutral point clamped,ANPC)三电平逆变器故障的主要类型,快速定位故障器件对提升ANPC三电平逆变器的可靠性具有重要意义。针对ANPC三电平逆变器开关器件开路故障,从传播路径的角度分析了不同调制算法下开关器件故障对输出电流的影响以及输出电流与负载侧电压的相位差对故障电流波形的影响。以三相输出电流作为故障特征量,提出一种基于切换调制策略的ANPC三电平逆变器开路故障诊断方法。首先采用电流平均值法先对故障开关器件的大致范围进行判定,然后切换调制策略以实现故障器件的精确定位。最后,通过仿真与实验验证了该方法可以在1个基波周期内实现故障定位,具有检测速度快、抗干扰能力强的优点,且无需额外增加用于故障检测的传感器。展开更多
文摘We propose an electronic model in Spice, instead of traditional mathematical analysis, for analyzing the performance of ferroelectric liquid crystal （FLC） under various working conditions. Using this equivalent circuit model,it is easy to simulate and analyze the behavior of an FLC layer in three different typical parameters,including temperature, input light wavelength, and the frequency of driving voltage. We conclude that the response velocity drops as the wavelength increases in the range of visible light, and for the parameter of temperature, the velocity reaches its lowest value when the temperature reaches a certain degree,meanwhile,the frequency of driving voltage exerts important effects on the response velocity only when the frequency is beyond a critical value. Excellent agreement is achieved between simulation and experimental results.
文摘针对断路器机械特性异常诊断中的多源信号采集与分析问题,设计出一套分合闸线圈电流和触头行程信号的采集系统,并提出了相应的异常模拟方案。通过引入分段滤波和循环差分判别进行特征提取,机器学习算法采用CatBoost模型进行单源信号的异常诊断,结合遗传算法(genetic algorithm,GA)进行参数优化,实现了基于线圈电流和触头行程的高准确率诊断。同时,利用线性判别分析(linear discriminant analysis,LDA)方法进行特征融合,提升了异常诊断效果。此外,对多种融合方法的诊断结果进行对比分析。结果表明,LDA-GA-CatBoost的特征级融合方法与基于改进的D-S证据理论(dempster-shafer theory of evidence,DST)的决策级融合方法的异常诊断率最高,均为95.82%,但LDA-GA-CatBoost的模型训练时间仅为改进的D-S证据理论的一半,更具有应用优势。
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901)。
文摘Quantum error-correcting codes are essential for fault-tolerant quantum computing,as they effectively detect and correct noise-induced errors by distributing information across multiple physical qubits.The subsystem surface code with three-qubit check operators demonstrates significant application potential due to its simplified measurement operations and low logical error rates.However,the existing minimum-weight perfect matching(MWPM)algorithm exhibits high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoder based on a graph attention network(GAT),representing error syndromes as undirected graphs with edge weights,and employing a multihead attention mechanism to efficiently aggregate node features and enable parallel computation.Compared to MWPM,the GAT decoder exhibits linear growth in computational complexity,adapts to different quantum code structures,and demonstrates stronger robustness under high physical error rates.The experimental results demonstrate that the proposed decoder achieves an overall accuracy of 89.95%under various small code lattice sizes(L=2,3,4,5),with the logical error rate threshold increasing to 0.0078,representing an improvement of approximately 13.04%compared to the MWPM decoder.This result significantly outperforms traditional methods,showcasing superior performance under small code lattice sizes and providing a more efficient decoding solution for large-scale quantum error correction.
文摘由于开关器件故障是有源中性点箝位(active neutral point clamped,ANPC)三电平逆变器故障的主要类型,快速定位故障器件对提升ANPC三电平逆变器的可靠性具有重要意义。针对ANPC三电平逆变器开关器件开路故障,从传播路径的角度分析了不同调制算法下开关器件故障对输出电流的影响以及输出电流与负载侧电压的相位差对故障电流波形的影响。以三相输出电流作为故障特征量,提出一种基于切换调制策略的ANPC三电平逆变器开路故障诊断方法。首先采用电流平均值法先对故障开关器件的大致范围进行判定,然后切换调制策略以实现故障器件的精确定位。最后,通过仿真与实验验证了该方法可以在1个基波周期内实现故障定位,具有检测速度快、抗干扰能力强的优点,且无需额外增加用于故障检测的传感器。
