In the field of deep space exploration,the rapid development of terahertz spectrometer has put forward higher requirements to the back-end chirp transform spectrometer(CTS)system.In order to simultaneously meet the me...In the field of deep space exploration,the rapid development of terahertz spectrometer has put forward higher requirements to the back-end chirp transform spectrometer(CTS)system.In order to simultaneously meet the measurement requirements of wide bandwidth and high accuracy spectral lines,we built a CTS system with an analysis bandwidth of 1 GHz and a frequency resolution of 100 kHz around the surface acoustic wave(SAW)chirp filter with a bandwidth of 1 GHz.In this paper,the relationship between the CTS nonlinear phase error shift model and the basic measurement parameters is studied,and the effect of CTS phase mismatch on the pulse compression waveform is analyzed by simulation.And the expander error optimization method is proposed for the problem that the large nonlinear error of the expander leads to the unbalanced response of the CTS system and the serious distortion of the compressed pulse waveform under large bandwidth.It is verified through simulation and experiment that the method is effective for reducing the root mean square error(RMSE)of the phase of the expander from 18.75°to 6.65°,reducing the in-band standard deviation of the CTS frequency resolution index from 8.43 kHz to 4.72 kHz,solving the problem of serious distortion of the compressed pulse waveform,and improving the uneven CTS response under large bandwidth.展开更多
Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not conside...Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.展开更多
文摘In the field of deep space exploration,the rapid development of terahertz spectrometer has put forward higher requirements to the back-end chirp transform spectrometer(CTS)system.In order to simultaneously meet the measurement requirements of wide bandwidth and high accuracy spectral lines,we built a CTS system with an analysis bandwidth of 1 GHz and a frequency resolution of 100 kHz around the surface acoustic wave(SAW)chirp filter with a bandwidth of 1 GHz.In this paper,the relationship between the CTS nonlinear phase error shift model and the basic measurement parameters is studied,and the effect of CTS phase mismatch on the pulse compression waveform is analyzed by simulation.And the expander error optimization method is proposed for the problem that the large nonlinear error of the expander leads to the unbalanced response of the CTS system and the serious distortion of the compressed pulse waveform under large bandwidth.It is verified through simulation and experiment that the method is effective for reducing the root mean square error(RMSE)of the phase of the expander from 18.75°to 6.65°,reducing the in-band standard deviation of the CTS frequency resolution index from 8.43 kHz to 4.72 kHz,solving the problem of serious distortion of the compressed pulse waveform,and improving the uneven CTS response under large bandwidth.
基金Projects(61773405,61725306,61533020)supported by the National Natural Science Foundation of ChinaProject(2018zzts583)supported by the Fundamental Research Funds for the Central Universities,China
文摘Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.
