为了充分利用不同类型的时间传递链路,需要实现不同采样率下时间传递链路数据的融合应用,提出了一种基于多分辨率分析的数据融合方法.首先对原始数据进行小波分解,把数据分解到统一的分辨率,初步消除高频噪声;然后在不同分辨率下进行Kal...为了充分利用不同类型的时间传递链路,需要实现不同采样率下时间传递链路数据的融合应用,提出了一种基于多分辨率分析的数据融合方法.首先对原始数据进行小波分解,把数据分解到统一的分辨率,初步消除高频噪声;然后在不同分辨率下进行Kalman滤波;最后通过Mallat快速重构算法得到融合结果.使用该方法处理中国科学院国家授时中心(National Time Service Center,NTSC)和德国联邦物理技术研究所(PhysikalischTechnische Bundesanstalt,PTB)之间的时间传递数据,结果显示融合算法能够处理链路异常或中断造成的数据问题.由于GPS(Global Positioning System)PPP(Precise Point Positioning solutions)链路实测结果性能整体优于TWSTFT(Two-Way Satellite Time and Frequency Transfer)链路,因此用GPS PPP链路测量结果评估融合算法增益.以快速协调世界时(Rapid Realization of Coordinated Universal Time,UTCr)为参考,数据融合结果的准确性增益约1%,日频率稳定度增益优于20%.同时融合算法可以抑制TWSTFT链路的周期噪声,能够有效提高链路的稳定性和鲁棒性.展开更多
A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet...A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.展开更多
文摘为了充分利用不同类型的时间传递链路,需要实现不同采样率下时间传递链路数据的融合应用,提出了一种基于多分辨率分析的数据融合方法.首先对原始数据进行小波分解,把数据分解到统一的分辨率,初步消除高频噪声;然后在不同分辨率下进行Kalman滤波;最后通过Mallat快速重构算法得到融合结果.使用该方法处理中国科学院国家授时中心(National Time Service Center,NTSC)和德国联邦物理技术研究所(PhysikalischTechnische Bundesanstalt,PTB)之间的时间传递数据,结果显示融合算法能够处理链路异常或中断造成的数据问题.由于GPS(Global Positioning System)PPP(Precise Point Positioning solutions)链路实测结果性能整体优于TWSTFT(Two-Way Satellite Time and Frequency Transfer)链路,因此用GPS PPP链路测量结果评估融合算法增益.以快速协调世界时(Rapid Realization of Coordinated Universal Time,UTCr)为参考,数据融合结果的准确性增益约1%,日频率稳定度增益优于20%.同时融合算法可以抑制TWSTFT链路的周期噪声,能够有效提高链路的稳定性和鲁棒性.
基金Projects(60634020, 60904077, 60874069) supported by the National Natural Science Foundation of ChinaProject(JC200903180555A) supported by the Foundation Project of Shenzhen City Science and Technology Plan of China
文摘A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.
