针对煤矿井下电控系统中DC-DC电源模块电容软故障类型多样、诊断精度不足的问题,提出了一种基于并行时序卷积网络(TCN)与图卷积网络(GCN)的融合模型。以150 W Boost型DC-DC电源为研究对象,采集电路中4个测点的电压信号。该模型通过TCN...针对煤矿井下电控系统中DC-DC电源模块电容软故障类型多样、诊断精度不足的问题,提出了一种基于并行时序卷积网络(TCN)与图卷积网络(GCN)的融合模型。以150 W Boost型DC-DC电源为研究对象,采集电路中4个测点的电压信号。该模型通过TCN捕获长时依赖特征,以GCN刻画测点拓扑关系;二者在特征层拼接,实现时间维与空间结构信息的互补融合。实验结果表明,该模型平均准确率达99.72%;在6 dB、4 dB、2 dB、0 dB信噪比条件下,准确率分别达到99.48%、98.54%、98.17%和93.78%,高于其他模型。该研究为煤矿井下电控设备中电容软故障的智能诊断提供了有效技术路径。展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
引入基于风向相似度自适应的GCN-LSTM模型进行昆山市PM_(2.5)预测,并与GCN模型和LSTM模型预测结果进行比较。结果显示,风向相似度自适应GCN-LSTM模型对昆山市PM_(2.5)浓度模拟的整体平均绝对误差、均方根误差和平均绝对百分比误差分别为...引入基于风向相似度自适应的GCN-LSTM模型进行昆山市PM_(2.5)预测,并与GCN模型和LSTM模型预测结果进行比较。结果显示,风向相似度自适应GCN-LSTM模型对昆山市PM_(2.5)浓度模拟的整体平均绝对误差、均方根误差和平均绝对百分比误差分别为3.30μg/m^(3)、5.16μg/m^(3)和15.6%,低于GCN模型和LSTM模型的对应指标。对于未来1 h PM_(2.5)浓度预测,风向相似度自适应GCN-LSTM模型在多个方面均比GCN模型和LSTM模型表现更好。展开更多
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
文摘引入基于风向相似度自适应的GCN-LSTM模型进行昆山市PM_(2.5)预测,并与GCN模型和LSTM模型预测结果进行比较。结果显示,风向相似度自适应GCN-LSTM模型对昆山市PM_(2.5)浓度模拟的整体平均绝对误差、均方根误差和平均绝对百分比误差分别为3.30μg/m^(3)、5.16μg/m^(3)和15.6%,低于GCN模型和LSTM模型的对应指标。对于未来1 h PM_(2.5)浓度预测,风向相似度自适应GCN-LSTM模型在多个方面均比GCN模型和LSTM模型表现更好。
