Ripple density resolution dependence on ripple width
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Abstract:
The goal of the study was to investigate how variations in ripple width influence the ripple density resolution. The influence of the ripple width was investigated with two experimental paradigms: (i) discrimination between a rippled test signal and a rippled reference signal with opposite ripple phases and (ii) discrimination between a rippled test signal and a flat reference signal. The ripple density resolution depended on the ripple width: the narrower the width, the higher the resolution. For distinguishing between two rippled signals, the resolution varied from 15.1 ripples/oct at a ripple width of 9% of the ripple frequency spacing to 8.1 ripples/oct at 64%. For distinguishing between a rippled test signal and a non-rippled reference signal, the resolution varied from 85 ripples/oct at a ripple width of 9% to 9.3 ripples/oct at a ripple width of 64%. For distinguishing between two rippled signals, the result can be explained by the increased ripple depth in the excitation pattern due to the widening of the inter-ripple gaps. For distinguishing between a rippled test signal and a non-rippled reference signal, the result can be explained by the increased ratio between the autocorrelated and uncorrelated components of the input signal.Keywords:
SIGNAL (programming language)
Most investigators emphasize the importance of detecting the reflected signal from the defect to determine if the pipe wall has any damage and to predict the damage location. However, often the small signal from the defect is hidden behind the other arriving wave modes and signal noise. To overcome the difficulties associated with the identification of the small defect signal in the time history plots, in this paper the time history is analyzed well after the arrival of the first defect signal, and after different wave modes have propagated multiple times through the pipe. It is shown that the defective pipe can be clearly identified by analyzing these late arriving diffuse ultrasonic signals. Multiple reflections and scattering of the propagating wave modes by the defect and pipe ends do not hamper the defect detection capability; on the contrary, it apparently stabilizes the signal and makes it easier to distinguish the defective pipe from the defect-free pipe. This paper also highlights difficulties associated with the interpretation of the recorded time histories due to mode conversion by the defect. The design of electro-magnetic acoustic transducers used to generate and receive the guided waves in the pipe is briefly described in the paper.
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Electromagnetic acoustic transducer
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Effects of ripple width in rippled-spectrum signals on ripple density resolution was investigated. Two measurement paradigms were tested: (i) ripple density resolution for discrimination between two rippled signals and (ii) discrimination between a rippled test signal and non-rippled reference signal. The ripple widths varied from 9% to 64% of the ripple frequency spacing. For both paradigms, the ripple density resolution increased with deceasing the ripple width. For discrimination between two rippled signals, the resolution was 8.1 ripples/oct for a ripple width of 64% and increased to 15.1 ripples/oct at the ripple width of 9%. For discrimination between a rippled test and non-rippled reference signal, the resolution was 9.3 ripples/oct at a ripple width of 64% and increased to 85 ripples/oct at a ripple width of 9%. Discrimination between two rippled signals is hypothesized to depend on ripple depth in the excitation pattern; the depth increases with narrowing the ripple width. Discrimination between a rippled test and non-rippled reference signal is hypothesized to depend on temporal processing; the effect of the ripple width appears due to increasing the ratio of the autocorrelated to uncorrelated components of the input signal with narrowing the ripples.
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Uncorrelated
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The goal of the study was to investigate how variations in ripple width influence the ripple density resolution. The influence of the ripple width was investigated with two experimental paradigms: (i) discrimination between a rippled test signal and a rippled reference signal with opposite ripple phases and (ii) discrimination between a rippled test signal and a flat reference signal. The ripple density resolution depended on the ripple width: the narrower the width, the higher the resolution. For distinguishing between two rippled signals, the resolution varied from 15.1 ripples/oct at a ripple width of 9% of the ripple frequency spacing to 8.1 ripples/oct at 64%. For distinguishing between a rippled test signal and a non-rippled reference signal, the resolution varied from 85 ripples/oct at a ripple width of 9% to 9.3 ripples/oct at a ripple width of 64%. For distinguishing between two rippled signals, the result can be explained by the increased ripple depth in the excitation pattern due to the widening of the inter-ripple gaps. For distinguishing between a rippled test signal and a non-rippled reference signal, the result can be explained by the increased ratio between the autocorrelated and uncorrelated components of the input signal.
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Forcing (mathematics)
Hysteresis
Wavenumber
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It is critical for buried target detection via ripple scattering to know the ripple structure, e.g., the ripple height and spatial wavelength. In the present paper, backscattering data from a 300-kHz system show that ripple wavelength and height can potentially be estimated from backscattering images. Motivated by the backscatter data, we have developed a time-domain numerical model to simulate scattering of high-frequency sound by a ripple field. This model treats small-scale scatterers as Lambertian scatterers distributed randomly on the large-scale ripple field. We have found that this approach characterizes the field data well. Numerical simulations are conducted to investigate the possibility of remotely sensing bottom ripple heights and wavelength.
Backscatter (email)
Ripple marks
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This paper analyzes the mechanism of ripple generation,summarizes characteristics of the ripple and its effects on load,and puts forward a discussion of methods for measuring ripple of DC stabilized power supply.
Ripple marks
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We show that the autocorrelation function of an optical signal may be simply obtained using a signal averager and a weighted signal-averaging technique. The method applies to the measurement of either intensity fluctuations or photocount fluctuations, and we present expressions for the weighted signal average and its error for each case. Finally we test the results experimentally and compare the data with the corresponding theoretical expressions.
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Autocorrelation technique
Signal averaging
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Signals with rippled frequency spectra are used to investigate capabilities of hearing to analyze and discriminate complex sounds. Till now, rippled signals with the difference between discriminated signals equal across the frequency band were exploited. In the present study, signals of different ripple densities in which ripple differences varied across the signal band were used. Ripple-density difference (RDD) thresholds increased with increasing the standard ripple density. At a standard ripple density of 2 to 10 ripples/oct, RDD threshold dependence on ripple density was identical for signal frequencies of 1 to 4 kHz: thresholds were from 0.06 ripples/oct at a standard density of 2 ripples/oct to 5–7 ripples/oct at a standard ripple density of 10 ripples/oct; RDD thresholds were not measurable at standard ripple densities above 10 ripples/oct and frequencies of 1 and 2 kHz. However, at a frequency of 4 kHz, RDD thresholds were measurable at standard ripple densities of 15 ripples/oct and higher. Hypothetically, at ripple densities of up to 10 ripples/oct, the signals were discriminated by the excitation-pattern mechanism; at ripple densities above 10 ripples/oct and a signal center frequency of 4 kHz, the signals were discriminated by the temporal-processing mechanism. [Work supported by Russian Science Foundation, Grant 16-15-10046.]
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Hysteresis
Ripple marks
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When a solid object or wheel is repeatedly dragged on a dry sandy surface, ripple patterns are formed. Although the conditions to form ripple patterns have been studied well, methods to eliminate the developed ripple patterns have not been understood thus far. Therefore, history-dependent stability of the ripple patterns formed on a sandy surface is investigated in this study. First, the ripple patterns are formed by sweeping the flat sandy surface with a flexible plow at a constant speed. Then, the sweeping speed is reduced, and the variation of ripple patterns is measured. As a result, we find that the ripple patterns show hysteresis. Specifically, the increase in amplitude of ripples is observed when the reduced velocity is close to the initial velocity forming the ripple pattern. In addition, splitting of ripples is found when the reduced velocity is further decreased. From a simple analysis of the plow's motion, we discuss the physical mechanism of the ripple splitting.
Hysteresis
Ripple marks
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