针对现有电动汽车电池状态估计方法存在运算效率低和估算准确率低的问题,提出一种模型以估算电动汽车电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)。采用堆叠降噪自编码器(stacked denosing auto encoder,SDAE)...针对现有电动汽车电池状态估计方法存在运算效率低和估算准确率低的问题,提出一种模型以估算电动汽车电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)。采用堆叠降噪自编码器(stacked denosing auto encoder,SDAE)清洗电压、电流和温度数据中的异常数据和空缺数据,减小对估算精度的影响。引入动态通道剪枝(dynamical channel pruning,DCP)技术对Informer模型进行稀疏化处理,提高剪枝后模型的性能和稳定性。将清洗过的数据输入DCPInformer模型实现SOC和SOH的精确估计。实验结果表明,所提出的SDAE-DCPInformer模型估计SOC的平均绝对误差和均方根误差分别达到0.25%和0.38%,估计SOH的平均绝对误差和均方根误差分别达到了0.51%和0.64%。与传统Transformer等模型相比,所提模型预测SOC和SOH的速度更快,估算准确度有效提升,拥有的更好稳定性和泛化性。展开更多
Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction systems.However,missing data frequently occur during data recording,posing a great challenge to data mining t...Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction systems.However,missing data frequently occur during data recording,posing a great challenge to data mining tasks.In this study,we propose a novel time series data representation-based denoising autoencoder(DAE)for the reconstruction of missing values.Two data representation methods,namely,recurrence plot(RP)and Gramian angular field(GAF),are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series.Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series.A comprehensive comparison is conducted amongst the different representations on standard datasets.Results show that the 2D representations have a lower reconstruction error than the raw time series,and the RP representation provides the best outcome.This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.展开更多
第五代(fifth-generation,5G)移动通信技术的兴起,推动了物联网(Internet of things,IoT)的发展。然而,随着物联网数据传输量的爆发式增长,频谱资源短缺问题越来越严重。频谱感知技术极大的提高了物联网频谱利用率。但是,物联网移动通...第五代(fifth-generation,5G)移动通信技术的兴起,推动了物联网(Internet of things,IoT)的发展。然而,随着物联网数据传输量的爆发式增长,频谱资源短缺问题越来越严重。频谱感知技术极大的提高了物联网频谱利用率。但是,物联网移动通信环境的复杂性高以及信号易畸变的特性,对现有的频谱感知算法提出了重大挑战。因此,提出了一种融合去噪自编码器(denoising autoencoder,DAE)和改进长短时记忆(long short term memory,LSTM)神经网络的智能频谱感知算法。DAE通过编码和解码过程挖掘移动信号的底层结构特征,改进的LSTM频谱感知分类器模型结合过去时刻信息特征对时序信号序列进行分类。与支持向量机(support vector machine,SVM)、循环神经网络(recurrent neural network,RNN)、LeNet5、学习矢量量化(learning vector quantization,LVQ)和Elman算法相比,该算法的感知性能提高了45%。展开更多
文摘针对现有电动汽车电池状态估计方法存在运算效率低和估算准确率低的问题,提出一种模型以估算电动汽车电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)。采用堆叠降噪自编码器(stacked denosing auto encoder,SDAE)清洗电压、电流和温度数据中的异常数据和空缺数据,减小对估算精度的影响。引入动态通道剪枝(dynamical channel pruning,DCP)技术对Informer模型进行稀疏化处理,提高剪枝后模型的性能和稳定性。将清洗过的数据输入DCPInformer模型实现SOC和SOH的精确估计。实验结果表明,所提出的SDAE-DCPInformer模型估计SOC的平均绝对误差和均方根误差分别达到0.25%和0.38%,估计SOH的平均绝对误差和均方根误差分别达到了0.51%和0.64%。与传统Transformer等模型相比,所提模型预测SOC和SOH的速度更快,估算准确度有效提升,拥有的更好稳定性和泛化性。
文摘Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction systems.However,missing data frequently occur during data recording,posing a great challenge to data mining tasks.In this study,we propose a novel time series data representation-based denoising autoencoder(DAE)for the reconstruction of missing values.Two data representation methods,namely,recurrence plot(RP)and Gramian angular field(GAF),are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series.Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series.A comprehensive comparison is conducted amongst the different representations on standard datasets.Results show that the 2D representations have a lower reconstruction error than the raw time series,and the RP representation provides the best outcome.This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.
文摘第五代(fifth-generation,5G)移动通信技术的兴起,推动了物联网(Internet of things,IoT)的发展。然而,随着物联网数据传输量的爆发式增长,频谱资源短缺问题越来越严重。频谱感知技术极大的提高了物联网频谱利用率。但是,物联网移动通信环境的复杂性高以及信号易畸变的特性,对现有的频谱感知算法提出了重大挑战。因此,提出了一种融合去噪自编码器(denoising autoencoder,DAE)和改进长短时记忆(long short term memory,LSTM)神经网络的智能频谱感知算法。DAE通过编码和解码过程挖掘移动信号的底层结构特征,改进的LSTM频谱感知分类器模型结合过去时刻信息特征对时序信号序列进行分类。与支持向量机(support vector machine,SVM)、循环神经网络(recurrent neural network,RNN)、LeNet5、学习矢量量化(learning vector quantization,LVQ)和Elman算法相比,该算法的感知性能提高了45%。
