Determination of the high-frequency behavior of the impedance from low-frequency data

1990 ◽  
Author(s):  
S. A. Heifets
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Frequency Data ◽  
Frequency Behavior

Magnetic Permeability and Relaxation Frequency in High Frequency Magnetic Materials.

2001 ◽  
Vol 674 ◽  
Author(s):  
M.I. Rosales ◽  
H. Montiel ◽  
R. Valenzuela
Keyword(s):  
High Frequency ◽  
Domain Walls ◽  
Magnetocrystalline Anisotropy ◽  
Low Frequency ◽  
Frequency Dispersion ◽  
Anisotropy Constant ◽  
Relaxation Frequency ◽  
Resonance Relaxation ◽  
Frequency Behavior

ABSTRACTAn investigation of the frequency behavior of polycrystalline ferrites is presented. It is shown that the low frequency dispersion (f < 10 MHz) of permeability is associated with the bulging of pinned domain walls, and has a mixed resonance-relaxation character, closer to the latter. It is also shown that there is a linear relationship between the magnetocrystalline anisotropy constant, K1, and the relaxation frequency. The slope of this correlation depends on the grain size. Such a relationship could allow the determination of this basic parameter from polycrystalline samples.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

Mechanisms of Noise Transmission through Helicopter Gearbox Support Struts

1994 ◽  
Vol 116 (4) ◽  
pp. 548-554 ◽  
Author(s):  
M. J. Brennan ◽  
R. J. Pinnington ◽  
S. J. Elliott
Keyword(s):  
High Frequency ◽  
Vibration Isolation ◽  
Low Frequency ◽  
Structural Members ◽  
Vibration Transmission ◽  
Gear Noise ◽  
High Frequencies ◽  
Passive Vibration Isolation ◽  
In Series ◽  
Frequency Behavior

Helicopter gearbox support struts are structural members designed to take large compressive and tensile mechanical loads. They also act as vibration transmission paths connecting the major noise and vibration generators of the main rotor and gearbox to the fuselage and cabin. In this paper the mechanisms of vibration transmission through these struts are examined. The differences between the low frequency behavior that is important in the transmission of the rotor vibrations, and the high frequency behavior that is important in the transmission of gear noise are discussed. A flexible element is introduced in series with the strut, and the limitations of this simple passive measure in attenuating longitudinal and flexural vibrations are highlighted. The analysis shows that there is a difference in the mechanisms of vibration transmission between low and high frequencies, and that there are difficulties in applying effective passive vibration isolation measures to this system.

scholarly journals Does the high-frequency data is helpful for forecasting Russian inflation?

2021 ◽  
Vol 37 (2) ◽  
pp. 318-343
Author(s):  
Dmitriy Tretyakov ◽  
◽  
Nikita Fokin ◽  
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
High Frequency ◽  
Inflation Targeting ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Policy Regime ◽  
The Future ◽  
Forecasting Inflation

Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation rates became more relevant than ever. In the new monetary policy regime, it is important for the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take measures to return inflation to the target level. In addition, for effective monetary policy, the households must trust the actions of monetary authorities and they must be aware of the future dynamics of inflation. Thus, to manage inflationary expectations of economic agents, the Central Bank should actively use the information channel, publish accurate forecasts of consumer price growth. The aim of this work is to build a model for nowcasting, as well as short-term forecasting of the rate of Russian inflation using high-frequency data. Using this type of data in models for forecasting is very promising, since this approach allows to use more information about the dynamics of macroeconomic indicators. The paper shows that using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several basic models, which only use low-frequency data.

Low-frequency Data Acquisition from High-frequency Source Using Half-cycle Pseudorandom Sweep

2016 ◽  
Author(s):  
Y. Nakamura ◽  
M. Takanashi ◽  
M. Nakatsukasa ◽  
Y. Kunishi ◽  
J. Sakakibara ◽  
...  
Keyword(s):  
Data Acquisition ◽  
High Frequency ◽  
Low Frequency ◽  
Half Cycle ◽  
Frequency Data ◽  
Frequency Source

Required Data Collection Times for Steady and Quasi-steady Experiments

2017 ◽  
Author(s):  
Robert F. Roddy ◽  
David E. Hess
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Frequency Noise ◽  
Surface Ship ◽  
System A ◽  
Low Pass ◽  
Collection Period ◽  
Almost All ◽  
Low Pass Filters

One of the requirements in performing steady or quasi-steady experiments is the determination of adequate collection times so that the data will not be biased due to low frequency energy in the data stream. Since virtually all steady experiments run at DTMB have low pass filters in line with the signal conditioning, high frequency noise is not a consideration in determining the required collection times. At both EMB and DTMB almost all of the surface ship drag measurements were made using gravity type balances until about 1970. These balances used both springs and dampers to modify the natural frequency of the system so that a good average model drag could be determined in a 5-6 sec collection period. Submarine model experiments began using block gages to measure drag beginning in the late 1950's. For these experiments crude methods were used to damp the output data but, to the author’s knowledge, no methods were ever put into place that was analogous to the springs and damper system. A method for determining the required collection times for any steady or quasi-steady experiment is presented along with sample cases showing the necessity for, and the utility of, using such a method.

Sign in / Sign up

Export Citation Format

Share Document