scholarly journals Modeling Personalized Item Frequency Information for Next-basket Recommendation

2020 ◽  
Author(s):  
Haoji Hu ◽  
Xiangnan He ◽  
Jinyang Gao ◽  
Zhi-Li Zhang
Keyword(s):  
Frequency Information

Lipreading with Auditory Low-frequency Information Contextual Constraints

1996 ◽  
Vol 25 (2) ◽  
pp. 127-132
Author(s):  
Jerker Rönnberg ◽  
Stefan Samuelsson ◽  
Björn Lyxell ◽  
Stig Arlinger
Keyword(s):  
Low Frequency ◽  
Frequency Information

Anomalies Enhanced: The Value of Higher Frequency Information

2015 ◽  
Author(s):  
Yufeng Han ◽  
Dayong Huang ◽  
Guofu Zhou
Keyword(s):  
Frequency Information

scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

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