In obtaining the travel time of sound/time difference in ultrasonic measurements, locating the ultrasonic wave head is pivotal. The existing location/detection methods are poor in terms of accuracy and anti-jamming capability. Therefore, they cannot be used in certain high precision fields. Hence, this study develops a novel method based on spectrum-subtracted sub-band singular spectrum entropy (SSB-SSE), which locates the wave head by introducing both sub-band and spectrum subtraction into singular spectrum entropy (SSE). Furthermore, this study generates a reasonable sub-band selection technique. Simulation experiments demonstrate that SSB-SSE is more accurate, robust, and capable of anti-jamming than other existing spectrum entropies. Therefore it can locate ultrasonic wave heads more effectively in layered media measurements.
HojjatiM.H. and HonarvarF., An investigation of the relationship between subsurface and head waves by finite element modeling, Nondestructive Testing and Evaluation31(4) (2016), 319–330.
2.
FaëseF.JenotF.OuaftouhM. et al., Fast slot characterization using laser ultrasonic and mode conversion, Measurement Science and Technology24(9) (2013), 095602.
3.
FerrandA.DarmonM.ChatillonS. et al., Modeling of ray paths of head waves on irregular interfaces in TOFD inspection for NDE, Ultrasonics54(7) (2014), 1851–1860.
4.
ZhangJ.W.ShiY.B.WangZ.G. and LiuX.E., Research of signal process and detecting head wave of acoustic array, China Measurement Technology32(4) (2006), 4–5, 100.
5.
SongY.ChenH.Y.ChenK.G.LiuX.H.PengL.G. and ZhengQ.Y., Detection of shear head wave of dipole acoustic logging based on wavelet transformation, Journal of Oil and Gas Technology30(5) (2008), 73–76.
6.
ZhouY.Q.WangX.M. and ChenD.H., Study of numerical simolation on amplitude attenuation of first arrivals in cased-hole acoustic logging, Well Logging Technology31(4) (2007), 321–326.
7.
HuangW.W.ShaoG.P. and LiJ.X., Detection and correlation correct of first arrival in digital acoustic array, Well Logging Technology29(2) (2005), 112–114.
8.
ChenH. and ZhaoZ.M., Extraction of head wave energy for formation wave in cement bond evaluation, Petroleum Instruments19(6) (2005), 46–47.
9.
LiQ.G. and TanS.W., A comparative study on the check of signal singularity based on Hilber-Huang transform, Journal of Xihua University ⋅ Natural Science27(4) (2008), 1, 3–6.
10.
JiangJ. and XieH.B., Denoising nonlinear time series using singular spectrum analysis and fuzzy entropy, Chinese Physics Letters33(10) (2016), 19–23.
11.
ShenJ.L.HungJ.W. and LeeL.S., Robust entropy-based endpoint detection for speech recognition in noisy environments, Proceeding of International Conference on Spoken Language Processing (1998), 232–238.
12.
WuB.F. and WangK.C., Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments, IEEE Trans on Speech and Audio Processing13(5) (2005), 762–775.
13.
LiuH.P.LiX.XuB.L. and JiangN., Summary and survey of endpoint detection algorithm for speech signals, Application Research of Computers25(8) (2008), 2278–2283.
14.
LuoY.F. and BaoC.C., Super resolution pitch detection based on band-partitioning spectral entropy and signal decomposition in DCT domain, Acta Electronica Sinica35(1) (2007), 13–22.
15.
ChouzenouxE.MoussaouiS.IdierJ. and MarietteF., Efficient maximum entropy reconstruction of nuclear magnetic resonance T1-T2 spectra, Signal Processing, IEEE Transactions on58(12) (2010), 6040–6051.
16.
ChenS.L.CaoR.R.BiG.H.ZhangW.Y. and GaoC., Distinguish faults located inside/outside protection zone of UHVDC transmission line by multi-resolution singular spectrum entropy and support vector machine, Power System Technology39(4) (2015), 989–994.
17.
ZhuY.W.ShiX.C. and LiP., Classification of islanding and grid disturbance based on multi-resolution singular spectrum entropy and SVM, Proceedings of the CSEE31(7) (2011), 64–70.
18.
LiR.YuD.J. and ZengW., Damage diagnosis of structures based on singular spectrum entropy and impulse response, Journal of Vibration Engineering19(3) (2006), 331–335.
19.
LuoS.R.ChengJ.S. and YangY., Intelligent fault diagnosis for roter using MSHOSSE and GA-VPMCD method, Chinese Journal of Mechanical Engineering25(21) (2014), 2912–2917, 2924.
20.
LiJ., Recognition of the optical image based on the wavelet space feature spectrum entropy, Optik126(23) (2015), 3931–3935.
21.
YangM.Y.ZhaoZ.Q. and YaoW.Y., A study of transient protection based on wavelet energy entropy for EHV transmission line, Power System Protection and Contol37(11) (2009), 10–14.
22.
YangW.X. and JiangJ.S., Study on the singular entropy of mechanical signal, Chinese Journal of Mechanical Engineering36(12) (2000), 9–13.
YuanY. and MaX.J., Classification of reciprocation compressor faults based on multi-component singular entropy, Journal of Dalian University of Technology47(2) (2007), 196–200.
25.
JiaC. and XuB., An improved entropy based endpoint detection algorithm, Proceeding of International Symposium on Chinese Spoken Language Processing, Taipei, Taiwan 8 (2002). http//www.isca-speech.org/archive/.
26.
KleinschmidtT.SridharanS. and MasonM., The use of phase in complex spectrum subtraction for robust speech recognition, Computer Speech and Language25(3) (2011), 585–600.
27.
SaxenaP.GuptaV.K. and ChandraM., Musical noise reduction capability of various speech enhancement algorithms, Advances in Intelligent Systems and Computing434 (2016), 693–701.
28.
CaoM.H.SunZ.Q. and GongE.L., A modified method for acoustic pulse extraction under noisy environments, Machinery, Materials and Information Technology Applications, (2016).
29.
SunB. and ZhangZ.L., Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory, IEEE Sensors Journal15(12) (2015), 7161–7168.
