Smooth construction of impulse response functions and applied loads using a time domain deconvolution method

2019 ◽  
Vol 443 ◽  
pp. 430-443 ◽  
Author(s):  
J.W. Draper III ◽  
S.W. Lee
Keyword(s):  
Impulse Response ◽  
Time Domain ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Deconvolution Method

Near-Optimal Time-Domain Control of Small Buoys in Irregular Waves

2014 ◽  
Author(s):  
Umesh A. Korde ◽  
R. Cengiz Ertekin
Keyword(s):  
Optimal Control ◽  
Impulse Response ◽  
Time Domain ◽  
The Body ◽  
Irregular Waves ◽  
Single Frequency ◽  
Impulse Response Functions ◽  
Control Force ◽  
Response Functions ◽  
Absorbed Power

Within the linear theory framework, smooth optimal control for maximum energy conversion in irregular waves requires independent synthesis of two non-causal impulse response functions operating on the body oscillations near the free surface, and one non-causal impulse response function relating the exciting force to the incident wave profile at the body. Full cancellation of reactive forces and matching of radiation damping thus requires knowledge or estimation of device velocity into the future. As suggested in the literature, the control force can be synthesized in long-crested waves by suitably combining the ‘full’ impulse response functions with wave surface elevation information at an appropriately determined distance up-wave of the device. This paper applies the near-optimal control approach investigated earlier by one of the authors (Korde, UA, Applied Ocean Research, to appear) to small floating cylindrical buoys. Absorbed power performance is compared with two other cases, (i) when single-frequency tuning is used based on non-real time adjustment of the reactive and resistive loads to maximize conversion at the spectral peak frequency, and (ii) when no control is applied with damping set to a constant value. Time domain absorbed power results are discussed.

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

Sign in / Sign up

Export Citation Format

Share Document