Estimation Of Response Function Research Articles

A Spatial-Frequency Approach to Point-Wise Frequency Response Function Estimation with Digital Image Correlation

The use of digital image correlation for modal analysis is becoming an appealing option thanks to its non-contact and full-field measurement process. However, frequency response function (FRF) estimation can be challenging due to the limited number of time domain data and heavy measurement noise. Thereby, the present work aims to propose a method which improves the estimation accuracy of point-wise FRFs. Firstly, a Gaussian-process-based spatial-frequency model is proposed, which makes use of the intrinsic properties of the FRF and the local spatial information of field measurements. Then, a Bayesian solution is developed, which is enforced by a stable and efficient numerical procedure. Finally, the effectiveness of the proposed method is verified by making a comparison with the spectral estimator through the use of simulated data, and it is further validated based on an experimental application.

Climate change and tourism demand: Risks for extreme heat?

Extremely high temperatures have become a major hurdle to tourists' experiences in various destinations. In this study, we present the first nationwide estimation of the temperature-tourism response function using a fine-grained panel dataset constructed by matching tourism data and daily meteorological datasets for 280 cities from 2005 to 2019 in China. Results show a non-linear relationship between temperature and tourism employing the temperature bins method, which is not sensitive to response equations. In addition, we conduct a rich heterogeneity analysis in the article to further analyse the effects of other external conditions on the temperature-tourism relationship, including the destination's level of tourism economic development, population, infrastructure development, and tourism resource endowment. We also observed that increasing summer temperatures result in decreased tourism arrivals and revenue, while rising autumn temperatures lead to increased tourism arrivals and revenue. This research contributes to a deeper understanding of the complex relationship between temperature dynamics and tourism patterns, offering insights into destination management and adaptation strategies.

An Economic Study to Estimate Response Functions for Sugar Beet Yield in the Arab Republic of Egypt

An Economic Study to Estimate Response Functions for Sugar Beet Yield in the Arab Republic of Egypt

Accurate frequency response function estimation using noise measurements in experimental modal analysis

Accurate frequency response function estimation using noise measurements in experimental modal analysis

Digital Twin-Enabled Response Function Analysis: A Synthetic Approach to Ship's Propulsion System Assessment

Analyzing the behavior of vessels in actual sea conditions is crucial for conceptual system design, safety and energy efficiency considerations. However, the essential seakeeping problem is reduced to the analysis of wave-hull interactions often neglecting consideration of the propulsion. But in the meantime, the synthetic consideration of wave–propulsion interactions is a key for safety and energy efficiency. Furthermore, safety evaluation of a ship's design with reduced propulsion power in adverse seas is vital for risk management. Response functions are commonly used to estimate propulsion system responses in incoming seaways. This paper proposes a synthetic approach using digital twin technology for rapid response function estimation. It introduces a companion linearized state-space model linked with the digital twin, enabling immediate retrieval of coefficients for response function analysis at the desired operating point. This integrated methodology provides a comprehensive representation of ship propulsion behavior in wave environments, offering a comprehensive framework for system performance assessment.

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

An experimental investigation on the fluid–structure coupling in horizontal pipes conveying two-phase intermittent flow

Two-phase flow is typically found in several industrial applications, such as in the production and transportation of oil and gas in the petrochemical industry, in the catalytic cracking and microreactors in the chemical industry, and in nuclear reactor cooling pumps. Measurement of two-phase flow features is usually necessary and has been done in several ways, including pressure probes, resistive sensors, gamma-ray, wire-mesh sensor and many others. However, these are either intrusive or invasive techniques, which might be of challenging application in industrial environments, or rely on a hazardous radioactive source. Vibration-based measurement of two-phase flow in pipes stands out as a non-invasive/non-intrusive approach and, consequently, multiphase-flow induced vibration in pipes has receiving increasing attention in recent years. In this work, the dynamic behaviour of a horizontal tube conveying an intermittent two-phase gas-liquid flow is investigated based on indirect approaches. The phenomenon of fluid–structure coupling is investigated using acceleration and pressure measurement. Moreover, the bubble size distribution is estimated from high-speed camera images and a deep learning model for image segmentation, along with its spectral content and time modulation. Focus is given at frequency bands around the cut-on frequencies of the circumferential wave modes of the pipe. An approach based on the estimation of frequency response function of the pressure and vibration at the liquid slug and elongated bubble is proposed, such that the coherence function can be used as quantitative measure of the coupling. Two experimental conditions with intermittent flow are investigated as representative cases. It is shown that there is a great vibration amplification at the cut-on frequencies of circumferential wave modes in pipes due to the corresponding structural wave and pressure coupling. Consequently, the frequency of passage of bubble can be estimated from the demodulation of vibration response filtered at the cut-on modes. The experimental results pave the way for innovative vibration-based measurement approaches.

Operator growth hypothesis in open quantum systems

The operator growth hypothesis (OGH) is a technical conjecture about the behavior of operators—specifically, the asymptotic growth of their —under repeated action by a Liouvillian. It is expected to hold for a sufficiently generic closed many-body system. When it holds, it yields bounds on the high-frequency behavior of local correlation functions and measures of chaos (like OTOCs). It also gives a route to numerically estimating response functions. Here we investigate the generalization of OGH to open quantum systems, where the Liouvillian is replaced by a Lindbladian. For a quantum system with local Hermitian jump operators, we show that the OGH is modified: we define a generalization of the Lanczos coefficient and show that it initially grows linearly as in the original OGH, but experiences exponentially growing oscillations on scales determined by the dissipation strength. We see this behavior manifested in a semi-analytically solvable model (large-q SYK with dissipation), numerically for an ergodic spin chain, and in a solvable toy model for operator growth in the presence of dissipation (which resembles a non-Hermitian single-particle hopping process). Finally, we show that the modified OGH connects to a fundamental difference between Lindblad and closed systems: At high frequencies, the spectral functions of the former decay algebraically, while in the latter they decay exponentially. This is an experimentally testable statement, which also places limitations on the applicability of Lindbladians to systems in contact with equilibrium environments. Published by the American Physical Society 2024

Identification of vector autoregressive models with nonlinear contemporaneous structure

We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models, we show that, under certain conditions, a large class of structural VAR models is identifiable. We spell out these specific conditions and propose a scheme for the estimation of structural impulse response functions in a nonlinear setting. We assess the performance of this scheme in a simulation experiment. Finally, we apply it in a study on the effects of the macroeconomic shocks that propagate through the economy, allowing for asymmetry between responses from positive and negative impulses.

Ridge regularized estimation of VAR models for inference

Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge‐type estimators. The asymptotic distribution and the properties of cross‐validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.

The role of tweets in agricultural export: an approach from text-mining and time-series analyses

PurposeThis study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of New Zealand (NZ) kiwifruit imported into Japan.Design/methodology/approachTweets mentioning Zespri™ were utilised as a proxy of such awareness. They were first summarised using two text-mining techniques: tf-idf scoring and a co-occurrence network graph. Afterwards, the authors estimated a tri-variate vector autoregression (VAR) model consisting of the net imports of NZ kiwifruit in Japan, unit import price and number of tweets. Additionally, the occurrence frequency of tweets with “Kiwi Brothers”, promotional characters for Zespri™’s sales, was added to the model, and a tetra-variate VAR model was estimated. Finally, Granger-causality tests, an estimation of the impulse response function and forecast error variance decomposition was conducted.FindingsAll these variables were found to Granger-cause each other. Furthermore, a shock in the document frequency of “Kiwi Brothers” significantly affected Japan’s kiwifruit imports from NZ, explaining approximately 20% of future imports. Zespri™’s distinctive sales promotion was, thus, found to contribute in part to the recent increase in NZ’s kiwifruit export to Japan.Originality/valueThis paper is the first to apply text-regression methodology to food consumption research; it contributes to food consumption research by proposing a practical way to combine tweets with outcome variables using a time-series analysis.

Contrast response function estimation with nonparametric Bayesian active learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

A Local Gaussian Process Regression Approach to Frequency Response Function Estimation

Frequency response function (FRF) estimation is a classical subject in system Identification. In the past two decades, there have been remarkable advances in developing local methods for this subject, e.g., the local polynomial method, local rational method, and iterative local rational method. The recent concentrations for local methods are two issues: the model order selection and the Identification of lightly damped systems. To address these two issues, we propose a new local method called local Gaussian process regression (LGPR). We show that the frequency response function locally is either analytic or resonant, and this prior knowledge can be embedded into a kernel-based regularized estimate through a dot-product kernel plus a resonance kernel induced by a second-order resonant system. The LGPR provides a new route to tackle the aforementioned issues. In the numerical simulations, the LGPR shows the best FRF estimation accuracy compared with the existing local methods, and moreover, the LGPR is more robust with respect to sample size and noise level.

An Efficient Implementation for Regularized Frequency Response Function and Transient Estimation

In this paper, we propose an efficient implementation for the kernel-based regularized frequency response function (FRF) and transient estimation. In particular, we show that when the diagonal correlated (DC) kernel is used, the output kernel matrix in the frequency domain has a hierarchically of-diagonal low-rank (HODLR) structure, and by exploring this structure, the FRF and transient estimates can be computed very efficiently The overall computational complexity is O(y2Nb) log2Nb), where Nb is the number of data in the interested frequency band and 7 is the HODLR rank of the output kernel matrix that is often much smaller than N&. The efficacy of the proposed implementation is demonstrated by numerical simulations.

Estimation of dielectric response functions for NIR-SWIR absorbing dyes by inverse analysis of diffuse reflectance

This study describes the estimation of dielectric response functions for NIR-SWIR absorbing dyes by inverse analysis of diffuse reflectance. The inverse analysis procedure is based on the Kramers–Kronig relations, the Kubelka–Munk model of diffuse reflectance, and the background subtraction of substrate spectra. The calculated response functions provide estimates of the dielectric response characteristics for NIR-SWIR absorbing dyes, as well as their sensitivity to material-background environments. This study demonstrates that Kramers–Kronig analysis can be adopted for calculating estimates of dielectric functions, given any reasonable estimate of absorbance. Specifically, that Kramers–Kronig analysis provides filtering of dielectric response structure with respect to spectrum artifacts due to inverse-analysis and spectroscopic-measurement procedures. This can significantly reduce the experimentation necessary to achieve desired dielectric response characteristics for NIR-SWIR dye and substrate combinations. Furthermore, these calculated response functions can support the construction of approximate effective medium models capable of estimating reflectance features for dyed fabrics.

Endogeneity and other problems in curvilinear income-waste response function estimations

Endogeneity and other problems in curvilinear income-waste response function estimations

Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers

PurposeNew total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data.MethodsTwenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood–brain barrier.ResultsCBF ranged from 37 to 69 mL blood min−1 100 mL of tissue−1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively.ConclusionThe high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.

Empirical Transfer Function Estimation With Differential Filtering and Its Application to Fine Positioning Control of Galvano Scanner

Fast and precise point-to-point (PTP) positioning control is a critical technology for improving the throughput and product quality of industrial machines. This study aims to investigate the frequency response function (FRF) estimation method with differential filtering (ETFE-Diff: empirical transfer function estimation with differential filtering) that uses the input and output signals of PTP motion during processing operation and to apply ETFE-Diff to a feedforward (FF) compensation adjustment. In this study, the principle of ETFE-Diff for FRF estimation was clarified, compared to the existing FRF estimation methods, i.e., empirical transfer function estimation and local rational modeling. Moreover, the effectiveness of ETFE-Diff for FF compensation adjustment in improving positioning accuracy of a galvano scanner as an industrial servo system was experimentally demonstrated.

Beyond climate change: Examining the role of environmental justice, agricultural mechanization, and social expenditures in alleviating rural poverty

Extreme weather events and extreme poverty are two sides of the same coin, with far-reaching consequences for emerging nations like Pakistan. Rural people are more likely to experience poverty and inequality as climate change worsens. This research aspires to close the gap between environmental ethics and justice by investigating how climate change issues contribute to poverty in Pakistan. The study used Robust Least Squares (RLS) regression to analyze the impact of water scarcity, extreme temperatures, and excessive rainfall on rural poverty in Pakistan from 1990Q1 to 2022Q4. Further, the study examines the effect of environmental justice interventions, access to healthcare and education, agricultural value-added and agricultural mechanization on the country's rural poverty. Results reveal that climate change contributes to rural poverty in Pakistan, while environmental justice initiatives, healthcare access, and agricultural automation alleviate poverty incidence. The Impulse Response Function (IRF) estimates suggested that rural poverty will be exacerbated over the next decade by water scarcity, high temperatures, and low agricultural value added but alleviated by excessive rainfall, environmental justice intervention, healthcare access, and agricultural mechanization. According to Variance Decomposition Analysis (VDA) projections, agricultural value added will substantially impact rural poverty by 2032, increasing it by 11.431%. Addressing these problems requires policymakers to prioritize the interests of the most marginalized groups by fostering fair results. Policies should cut GHG emissions and encourage sustainable development to combat climate change. Modernizing farming techniques and expanding access to healthcare are also necessary for increasing efficiency and production. It is essential to execute environmental justice interventions so that all communities have access to environmental resources and protections equitably. Promoting equitable outcomes and reducing poverty in Pakistan's climate change context may be achieved by closing the gap between environmental ethics and justice.

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels

Mean Group Distributed Lag Estimation of Impulse Response Functions in Large Panels