Digital signal processsing functions for ultra-low frequency calibrations

Ingerslev, Henrik; Andresen, Søren; Winther, Jacob Holm

doi:10.21014/acta_imeko.v9i5.1004

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…We chose time wisely that did not intersect with the begin and finish dates of the Global Volcanism Program (GVP) because we intended to regulate the timeframe of changeover between active volcanic and quasi activity independently. Feature extraction is the process of finding variables that will be used as inputs into ML models [16]- [20].…”

Section: Methodsmentioning

confidence: 99%

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

2022

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The circumstances and factors which determine the volcanic explosive ejection are unknown, and currently, there is no effective way to determine the end of a volcanic explosive ejection. At present, the end of an eruption is determined by either generalized standards or the measurement which is unique to the volcano. We investigate the use of controlled machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gaussian Process Classifiers (GPC), and create a decisiveness index D to assess the uniformity of the groups provided by these machine learning models. We analyzed the measured end-date obtained by seismic information categorization is two to four months later than the end-dates determined by the earliest instance of visible eruption for both volcanic systems. Likewise, the measurement systems, measurement technology becomes key elements in the seismic data analysis. The findings are consistent across models and correspond to previous, broad definitions of ejection. Obtained classifications demonstrate a more significant relationship between eruptive movement and visual activity than information base records of ejection start and completion timings. Our research has presented a new measurement-based categorization technique for studying volcanic eruptions, which provides a reliable tool for determining whether or not an emission has stopped without the need for visual confirmation.

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Section: Methodsmentioning

confidence: 99%

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

2022

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“…Some of the calibration institutes may empirically apply a digital filter to deal with the problem, although an unified approach has not yet been established. Another signal processing method using correlation has also been proposed [9]. In this paper, we discuss the limitations of the SAM and its optimization through theoretical investigations, simulations, and experiments.…”

Section: Introductionmentioning

confidence: 99%

Precise sinusoidal signal extraction from noisy waveform in vibration calibration

Shimoda¹,

Kokuyama²,

Nozato³

2022

Metrologia

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Precise extraction of sinusoidal vibration parameters is essential for the dynamic calibration of vibration sensors, such as accelerometers. However, several standard methods have not yet been optimized for large background noise. In this work, signal processing methods to extract small vibration signals from noisy data in the case of accelerometer calibration are discussed. The results show that spectral leakage degrades calibration accuracy. Three methods based on the use of a filter, window function, and numerical differentiation are investigated to reduce the contribution of the calibration system noise. We demonstrate the effectiveness of these methods with theoretical calculations, simulations, and experiments. The uncertainty of microvibration calibration in the National Metrology Institute of Japan is reduced by two orders of magnitudes using the proposed methods. We recommend to use a combination of numerical differentiation and either a time-domain window function or a bandpass filter for most accelerometer microvibration sensitivity calibrations. For suppressing the effects of line noise, adjusting the data set length is also effective.

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“…Some of the calibration institutes empirically apply a digital filter to deal with the problem, although an unified approach has not yet been established. Another signal processing method using correlation has also been proposed [9]. In this paper, we discuss the limitations of the SAM and its optimization through theoretical investigations, simulations, and experiments.…”

Section: Introductionmentioning

confidence: 99%

Precise sinusoidal signal extraction from noisy waveform in vibration calibration

Shimoda,

Kokuyama,

Nozato

2022

Preprint

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Precise extraction of sinusoidal vibration parameters is essential for the dynamic calibration of vibration sensors, such as accelerometers. However, several standard methods have not yet been optimized for large background noise. In this work, signal processing methods to extract small vibration signals from noisy data in the case of accelerometer calibration is discussed. The results show that spectral leakage degrades calibration accuracy. Three methods based on the use of a filter, window function, and numerical differentiation are investigated with theoretical calculations, simulations, and experiments. These methods can effectively reduce the contribution of the calibration system noise. The uncertainty of micro vibration calibration in the National Metrology Institute of Japan is reduced by two orders of magnitudes using the proposed methods. The theoretical analyses in this work can lay the foundation for the optimization of signal processing in vibration calibration, and can be applied to other dynamic calibration fields.

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Digital signal processsing functions for ultra-low frequency calibrations

Cited by 5 publications

References 8 publications

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

A machine learning based sensing and measurement framework for timing of volcanic eruption and categorization of seismic data

Precise sinusoidal signal extraction from noisy waveform in vibration calibration

Precise sinusoidal signal extraction from noisy waveform in vibration calibration

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