Matches in SemOpenAlex for { <https://semopenalex.org/work/W2070944105> ?p ?o ?g. }
- W2070944105 endingPage "546" @default.
- W2070944105 startingPage "540" @default.
- W2070944105 abstract "In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications." @default.
- W2070944105 created "2016-06-24" @default.
- W2070944105 creator A5013970445 @default.
- W2070944105 creator A5031274623 @default.
- W2070944105 creator A5087489952 @default.
- W2070944105 date "2014-01-01" @default.
- W2070944105 modified "2023-09-26" @default.
- W2070944105 title "Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise" @default.
- W2070944105 cites W1963999667 @default.
- W2070944105 cites W1979731145 @default.
- W2070944105 cites W1980240972 @default.
- W2070944105 cites W1987921974 @default.
- W2070944105 cites W1992758828 @default.
- W2070944105 cites W1994146659 @default.
- W2070944105 cites W1996421999 @default.
- W2070944105 cites W2004728699 @default.
- W2070944105 cites W2005282998 @default.
- W2070944105 cites W2016731871 @default.
- W2070944105 cites W2032663446 @default.
- W2070944105 cites W2037273971 @default.
- W2070944105 cites W2044843375 @default.
- W2070944105 cites W2052144963 @default.
- W2070944105 cites W2057168506 @default.
- W2070944105 cites W2066934803 @default.
- W2070944105 cites W2068501389 @default.
- W2070944105 cites W2075907934 @default.
- W2070944105 cites W2078707738 @default.
- W2070944105 cites W2092183766 @default.
- W2070944105 cites W2093490812 @default.
- W2070944105 cites W2101913771 @default.
- W2070944105 cites W2112768936 @default.
- W2070944105 cites W2115428134 @default.
- W2070944105 cites W2116967254 @default.
- W2070944105 cites W2118178838 @default.
- W2070944105 cites W2125303986 @default.
- W2070944105 cites W2127604727 @default.
- W2070944105 cites W2131651138 @default.
- W2070944105 cites W2146727821 @default.
- W2070944105 cites W2166397251 @default.
- W2070944105 cites W2167682994 @default.
- W2070944105 cites W2169873973 @default.
- W2070944105 cites W2810652404 @default.
- W2070944105 cites W4211019631 @default.
- W2070944105 cites W4229557733 @default.
- W2070944105 cites W4237156741 @default.
- W2070944105 doi "https://doi.org/10.1016/j.measurement.2013.09.008" @default.
- W2070944105 hasPublicationYear "2014" @default.
- W2070944105 type Work @default.
- W2070944105 sameAs 2070944105 @default.
- W2070944105 citedByCount "53" @default.
- W2070944105 countsByYear W20709441052014 @default.
- W2070944105 countsByYear W20709441052015 @default.
- W2070944105 countsByYear W20709441052016 @default.
- W2070944105 countsByYear W20709441052017 @default.
- W2070944105 countsByYear W20709441052018 @default.
- W2070944105 countsByYear W20709441052019 @default.
- W2070944105 countsByYear W20709441052020 @default.
- W2070944105 countsByYear W20709441052021 @default.
- W2070944105 countsByYear W20709441052022 @default.
- W2070944105 crossrefType "journal-article" @default.
- W2070944105 hasAuthorship W2070944105A5013970445 @default.
- W2070944105 hasAuthorship W2070944105A5031274623 @default.
- W2070944105 hasAuthorship W2070944105A5087489952 @default.
- W2070944105 hasConcept C11413529 @default.
- W2070944105 hasConcept C114996537 @default.
- W2070944105 hasConcept C115961682 @default.
- W2070944105 hasConcept C121332964 @default.
- W2070944105 hasConcept C127413603 @default.
- W2070944105 hasConcept C131021393 @default.
- W2070944105 hasConcept C13412647 @default.
- W2070944105 hasConcept C137270730 @default.
- W2070944105 hasConcept C142433447 @default.
- W2070944105 hasConcept C154945302 @default.
- W2070944105 hasConcept C163294075 @default.
- W2070944105 hasConcept C196216189 @default.
- W2070944105 hasConcept C199360897 @default.
- W2070944105 hasConcept C207658827 @default.
- W2070944105 hasConcept C24326235 @default.
- W2070944105 hasConcept C24890656 @default.
- W2070944105 hasConcept C2776257435 @default.
- W2070944105 hasConcept C2778116611 @default.
- W2070944105 hasConcept C2778755073 @default.
- W2070944105 hasConcept C2779843651 @default.
- W2070944105 hasConcept C41008148 @default.
- W2070944105 hasConcept C47432892 @default.
- W2070944105 hasConcept C554190296 @default.
- W2070944105 hasConcept C62520636 @default.
- W2070944105 hasConcept C76155785 @default.
- W2070944105 hasConcept C84462506 @default.
- W2070944105 hasConcept C94915269 @default.
- W2070944105 hasConcept C99498987 @default.
- W2070944105 hasConceptScore W2070944105C11413529 @default.
- W2070944105 hasConceptScore W2070944105C114996537 @default.
- W2070944105 hasConceptScore W2070944105C115961682 @default.
- W2070944105 hasConceptScore W2070944105C121332964 @default.
- W2070944105 hasConceptScore W2070944105C127413603 @default.
- W2070944105 hasConceptScore W2070944105C131021393 @default.
- W2070944105 hasConceptScore W2070944105C13412647 @default.