Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning(SRL)and deep neural...Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning(SRL)and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest(MDCBF)model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.展开更多
为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)...为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络混合的柔直换流阀冷却系统入阀水温的预测模型,并以此为基础对柔直换流站阀冷系统的冷却能力进行评估。首先,采用RF算法对由阀冷系统监测变量组成的高维特征集进行重要性分析,筛选出影响入阀水温的重要特征,与历史入阀水温构成输入特征向量。然后,将特征向量输入到BiLSTM预测模型,对模型进行训练并实现对入阀水温的准确预测和冷却能力定量评估。最后,以广东电网某柔直换流站为实例对所提方法进行分析,验证了所提出的基于RF-BiLSTM的混合模型预测精度优于BiLSTM模型、RF模型、支持向量机(support vector machine,SVM)模型和自回归滑动平均模型(auto-regressive and moving average,ARMA)模型,并且实现了冷却能力的定量评估。结果表明该换流站冷却裕量达98%,存在过度冷却、能源浪费的问题,与换流站现场运行情况相符,验证了文中所提方法的有效性和准确性。展开更多
该文基于某660 MW电站锅炉的现场运行数据,在进行数据预处理的基础上,利用随机森林算法对输入变量进行特征提取以降低变量维数和消除变量间的相关性,并与双向长短时记忆神经网络(bi-directional long short-term memory,Bi-LSTM)相结合...该文基于某660 MW电站锅炉的现场运行数据,在进行数据预处理的基础上,利用随机森林算法对输入变量进行特征提取以降低变量维数和消除变量间的相关性,并与双向长短时记忆神经网络(bi-directional long short-term memory,Bi-LSTM)相结合,建立了SCR脱硝系统入口NOx浓度的模型。将上述模型与其他建模方法进行比较,并将该模型实际应用于某电厂,作为精准喷氨控制的基础,结果表明通过Bi-LSTM建立的预测模型具有良好的预测能力,为进一步实施精准喷氨控制提供了模型基础。展开更多
基金supported in part by the NSFC-Xinjiang Joint Fund under Grant No.U1903127in part by the Natural Science Foundation of Shandong Province under Grant No.ZR2020MF052。
文摘Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning(SRL)and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest(MDCBF)model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.
文摘为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络混合的柔直换流阀冷却系统入阀水温的预测模型,并以此为基础对柔直换流站阀冷系统的冷却能力进行评估。首先,采用RF算法对由阀冷系统监测变量组成的高维特征集进行重要性分析,筛选出影响入阀水温的重要特征,与历史入阀水温构成输入特征向量。然后,将特征向量输入到BiLSTM预测模型,对模型进行训练并实现对入阀水温的准确预测和冷却能力定量评估。最后,以广东电网某柔直换流站为实例对所提方法进行分析,验证了所提出的基于RF-BiLSTM的混合模型预测精度优于BiLSTM模型、RF模型、支持向量机(support vector machine,SVM)模型和自回归滑动平均模型(auto-regressive and moving average,ARMA)模型,并且实现了冷却能力的定量评估。结果表明该换流站冷却裕量达98%,存在过度冷却、能源浪费的问题,与换流站现场运行情况相符,验证了文中所提方法的有效性和准确性。
文摘该文基于某660 MW电站锅炉的现场运行数据,在进行数据预处理的基础上,利用随机森林算法对输入变量进行特征提取以降低变量维数和消除变量间的相关性,并与双向长短时记忆神经网络(bi-directional long short-term memory,Bi-LSTM)相结合,建立了SCR脱硝系统入口NOx浓度的模型。将上述模型与其他建模方法进行比较,并将该模型实际应用于某电厂,作为精准喷氨控制的基础,结果表明通过Bi-LSTM建立的预测模型具有良好的预测能力,为进一步实施精准喷氨控制提供了模型基础。
