White Noise Test Research Articles

Shake table testing for the seismic response of a container crane with uplift and derailment

In the seismic analysis, the boundary condition of a container crane is a complex problem when dealing with the contact between the crane's toes and the ground surface or wharf deck. In operation, the wheels of container cranes move on rails freely but are usually anchored when at rest. Therefore, the wheels could be lifted up and derailed due to seismic excitation. The boundary condition of a container crane has not been fully addressed in past research. It has mostly been simplified as pin supports without considering the possibilities of uplift and derailment of the crane legs. Hence, there is a need to conduct a study focusing on the effects of derailment and uplift on a container crane's seismic response. This paper investigates the seismic response of a container crane via a shake table testing of a 1/20 scale crane constructed according to the similitude law, with a simulation of the boundary condition of the scale crane as a pin and a friction boundary. A white noise test was conducted to determine the natural frequencies of the scale crane, then the seismic response of the container crane was determined via three input ground motions. The three input ground motions were designed by matching real earthquakes to the Korean design elastic response spectrum and scaling down for the shake table testing. Under these seismic excitations, the responses of the container crane were considered in terms of uplift, derailment, portal movement, and bending moments. The shake table testing shows that the maximum difference in the natural frequencies of the crane for the portal sway mode, between the friction boundary and pin support, was 2.1%. Due to the effects of derailment and uplift of the landside legs, the bending moments in the crane's seaside legs with a friction boundary were 109% higher than those with a pinned boundary. In addition, a residual deformation was observed in the portal frame after the vibration had stopped when the scale crane was assigned a friction boundary.

Experimental investigation of a closed vertical cylinder-shaped fish cage in waves

Wave-induced response of a closed floating fish cage consisting of a vertical circular cylinder with an external toroidal floater is studied theoretically and experimentally. A main purpose was to investigate how the internal sloshing would influence the global response of the cage, the interior wave elevation and also the mean drift loads. An ocean basin laboratory was used in order to minimize tank wall effects. An optical system involving eight markers was used to obtain experimental values for radial elastic deformations of the cylindrical part. The closer a coupled natural period between body motions and sloshing is to a corresponding natural sloshing period, the more nonlinear sloshing can be. In the examined case, the highest coupled natural period between surge, pitch and sloshing is 0.8 times the highest natural sloshing period. Linear potential flow theory can, in general, explain experimental transfer functions of rigid-body motions and sloshing due to rigid-body motions obtained by both regular wave and truncated white noise tests. Theoretical second order mean wave drift forces based on a rigid body and potential flow agree also well with experimental results. Resonant 3D sloshing was excited due to ovalizing hydroelastic structural modes in both regular waves and white-noise tests. The closeness between the corresponding structural and sloshing frequencies caused large response. A linear hydroelastic analysis based on WAMIT and LS-DYNA could partly explain the response. Parametric resonant pitch response occurred experimentally at large wave periods partially due to in and out of water motion of portions of the floater and was explained by treating the pitch as uncoupled motion modeled by the Mathieu equation. The pitch motion predicted by linear potential flow theory was, in general, unsatisfactory when the floater was partly out of the water occasionally.

Super-Whiteness of Returns Spectra

Until the late 70’s the spectral densities of stock returns and stock index returns exhibited a type of non-constancy that could be detected by standard tests for white noise. Since then these tests have been unable to find any substantial deviations from whiteness. But that does not mean that today’s returns spectra contain no useful information. Using several sophisticated frequency domain tests to look for specific patterns in the periodograms of returns series we find nothing or, more precisely, less than nothing. Actually, there is a striking power deficiency, which implies that these series exhibit even fewer patterns than white noise. To unveil the source of this “super-whiteness” we design a simple frequency domain test for characterless, fuzzy alternatives, which are not immediately usable for the construction of profitable trading strategies, and apply it to the same data. Because the power deficiency is now much smaller, we conclude that our puzzling findings may be due to trading activities based on excessive data snooping.

Analysis and Construction of EEMD Smart Model and Fuzzy Forecasting through Improved Bayesian Estimation

Different portfolio models will have a great impact on the model rate of return. In order to meet the data needs of portfolio investment forecasting, a big data generation method based on incremental Bayesian network model is proposed. Firstly, the fuzzy interval number is constructed by using the global GM (1pare 1) prediction model, then the fuzzy Mmurv model is established based on the interval fuzzy number, and the model is optimized based on the midpoint, radius and acceptability of the interval number, thus a single objective programming model with parameters is obtained. The future data is partially generated by using the time series generation algorithm, and the Bayesian network model trained by historical data is updated with the newly generated data. So that the updated Bayesian network can reflect the relationship between the variables and the laws contained in the new and old financial data in this period of time. And use the model to predict the amount of foreign direct investment absorbed by China in 2020, test the robustness and analyze the impact of major random events to show the reliability of the prediction. The analysis is carried out on multiple scales. The results show that the integration of IMF1 and IMF2 components through the white noise test can be regarded as the random impact of external factors; the cycle length of IMF3 component is about 3 years, which can be regarded as the inventory cycle caused by the change of enterprise inventory; the cycle length of IMF4 component is 5 years, which can be regarded as political cycle; the cycle length of IMF5 component is about 20 years, which can be regarded as construction cycle. The set of portfolio paths is generated by path search algorithm in Bayesian network, and the big data set with real data characteristics is generated according to the probability distribution of each path. The experimental results show that the method is feasible and ensures a certain accuracy. The forecast results show that the amount of foreign direct investment in China will continue to fluctuate seasonally and in a trend in recent years.

Seismic response investigation of 1/20 scale container crane through shake table test and finite element analysis

This paper summarizes all detailed results obtained from a series of shake table testings of a 1/20 scale container crane with friction and pin boundary conditions at the Seismic Research and Test Center in South Korea and compared them with those from its finite element analysis (FEA). The tested 1/20 scale container crane was initially designed based on the similitude laws with scaling factors proportional to the sectional properties and dynamic characteristics of a commonly used full-scale crane. To determine experimental natural frequencies of the tested crane, a white noise test was conducted on the shake table. Then, three different ground motions, including Manjil Iran Abbar, Nahanni Canada, and Mineral Virginia, were individually applied in a trolley-boom direction to the crane placed on the shake table to secure its seismic responses, especially for derailment, uplift and drift. A finite element model of the tested crane was also created using multi-frame elements available in commercially used FEA software and simulated to replicate the testing results. Key findings revealed that the effect of the boundary conditions did not significantly affect the mode shapes and frequencies of the tested crane, but the drifts of the crane with the friction boundary condition resulting from each of the ground motions changed substantially as compared to those from the crane with the pin boundary condition. The derailment displacement at the landside legs of the tested crane was also found to be 12.5 mm for the Manjil Iran and Nahanni Canada earthquakes and 11.5 mm for the Mineral Virginia earthquake. The results from the testing were, largely, in good agreement with those from the FEA.

Correlation between Emergency Medical Services Suspected COVID-19 Patients and Daily Hospitalizations

Objective: We sought to determine if Emergency Medical Services (EMS) identified Persons Under Investigation (PUI) for COVID-19 are associated with hospitalizations for COVID-19 disease for the purposes of serving as a potential early indicator of hospital surge. Methods: A retrospective analysis was conducted using data from the Maryland statewide EMS electronic medical records and daily COVID-19 hospitalizations from March 13, 2020 through July 31, 2020. All unique EMS patients who were identified as COVID-19 PUIs during the study period were included. Descriptive analysis was performed. The Box-Jenkins approach was used to evaluate the relationship between EMS transports and daily new hospitalizations. Separate Auto Regressive Integrated Moving Average (ARIMA) models were constructed to transform the data into a series of independent, identically distributed random variables. Fit was measured using the Akaike Information Criterion (AIC). The Box-Ljung white noise test was utilized to ensure there was no autocorrelation in the residuals. Results: EMS units in Maryland identified a total of 26,855 COVID-19 PUIs during the 141-day study period. The median patient age was 62 years old, and 19,111 (71.3%) were 50 years and older. 6,886 (25.6%) patients had an abnormal initial pulse oximetry (<92%). A strong degree of correlation was observed between EMS PUI transports and new hospitalizations. The correlation was strongest and significant at a 9-day lag from time of EMS PUI transports to new COVID-19 hospitalizations, with a cross correlation coefficient of 0.26 (p < .01). Conclusions: A strong correlation between EMS PUIs and COVID-19 hospitalizations was noted in this state-wide analysis. These findings demonstrate the potential value of incorporating EMS clinical information into the development of a robust syndromic surveillance system for COVID-19. This correlation has important utility in the development of predictive tools and models that seek to provide indicators of an impending surge on the healthcare system at large.

Modeling High-Dimensional Time Series: A Factor Model With Dynamically Dependent Factors and Diverging Eigenvalues

This article proposes a new approach to modeling high-dimensional time series by treating a p-dimensional time series as a nonsingular linear transformation of certain common factors and idiosyncratic components. Unlike the approximate factor models, we assume that the factors capture all the nontrivial dynamics of the data, but the cross-sectional dependence may be explained by both the factors and the idiosyncratic components. Under the proposed model, (a) the factor process is dynamically dependent and the idiosyncratic component is a white noise process, and (b) the largest eigenvalues of the covariance matrix of the idiosyncratic components may diverge to infinity as the dimension p increases. We propose a white noise testing procedure for high-dimensional time series to determine the number of white noise components and, hence, the number of common factors, and introduce a projected principal component analysis (PCA) to eliminate the diverging effect of the idiosyncratic noises. Asymptotic properties of the proposed method are established for both fixed p and diverging p as the sample size n increases to infinity. We use both simulated data and real examples to assess the performance of the proposed method. We also compare our method with two commonly used methods in the literature concerning the forecastability of the extracted factors and find that the proposed approach not only provides interpretable results, but also performs well in out-of-sample forecasting. Supplementary materials for this article are available online.

Experimental evaluation of a mooring system simplification methodology for reducing mooring lines in a VLFS model testing at a moderate water depth

The scale model experiment for typical offshore floating structures with increasingly excessive mooring lines is usually complex and difficult to set up, which often results in low reliability of the experimental results. In the previous paper, Liang et al. (2019) proposed a mooring system simplification methodology for the scale model experiment based on the equivalent of the vessel/mooring coupled dynamics for vessel with original and simplified mooring systems and demonstrated its effectiveness by numerical studies In this paper, the feasibility of the mooring system simplification methodology is further evaluated by a model test for a very large floating structure’s (VLFS’s) mooring system comprising 20 mooring chains. A 10 chains simplified mooring system is obtained with the correlation coefficients of coupled vessel/mooring dynamics larger than 0.9. Thereafter, the equivalence of the original and simplified mooring systems is validated by performing the decay test, white noise test, regular and irregular wave tests. The coincidence of the model test results, including natural period, damping coefficient, response amplitude operator (RAO), motion power spectral density and motion time series of the VLFS model, confirms the equivalence of the simplified mooring system with the original mooring system and indicates that the mooring system simplification methodology is effective in designing the simplified mooring system to replace the original mooring system in a scale model experiment.

Multi-body hydrodynamic resonance and shielding effect of vessels parallel and nonparallel side-by-side

An attempt has been made to study the hydrodynamic interaction of side-by-side vessels based on the gap resonant modes which were theoretically derived by Molin et al. The hydrodynamic associated wave elevations, coefficients, and wave forces are analyzed for a real hull shaped FPSO and a ship. The resonant characteristics of parallel and nonparallel configurations under different wave headings are analyzed. The resonant mode due to the gap and its impact on various hydrodynamic results are emphasized in this study. The vessel motions are simulated in the time domain by using Cummins’ approach. Calculated motion RAOs by white noise test and relative motion under irregular waves are validated by measurement in the model test. The resonant behaviors and phase differences of each degree of freedom of vessels are investigated. The shielding effect is evaluated numerically and experimentally by exchanging vessels on lee-side and weather-side. Results indicate the different degree of freedom has a distinct preference to react to the resonant modes. The first mode which is superior under beam sea may become less significant under oblique and head seas. Higher resonant mode shifts to lower frequency in the nonparallel configuration. The shielding effect only suppresses the motion caused by the gap resonance, the natural frequency resonance like roll remains without change.

Hypothesis testing for high-dimensional time series via self-normalization

Self-normalization has attracted considerable attention in the recent literature of time series analysis, but its scope of applicability has been limited to low-/fixed-dimensional parameters for low-dimensional time series. In this article, we propose a new formulation of self-normalization for inference about the mean of high-dimensional stationary processes. Our original test statistic is a U-statistic with a trimming parameter to remove the bias caused by weak dependence. Under the framework of nonlinear causal processes, we show the asymptotic normality of our U-statistic with the convergence rate dependent upon the order of the Frobenius norm of the long-run covariance matrix. The self-normalized test statistic is then constructed on the basis of recursive subsampled U-statistics and its limiting null distribution is shown to be a functional of time-changed Brownian motion, which differs from the pivotal limit used in the low-dimensional setting. An interesting phenomenon associated with self-normalization is that it works in the high-dimensional context even if the convergence rate of original test statistic is unknown. We also present applications to testing for bandedness of the covariance matrix and testing for white noise for high-dimensional stationary time series and compare the finite sample performance with existing methods in simulation studies. At the root of our theoretical arguments, we extend the martingale approximation to the high-dimensional setting, which could be of independent theoretical interest.

Shaking table tests on a deep-water high-pier whole bridge under joint earthquake, wave and current action

The deep-water high-pier bridges that located in active seismicity areas have the potential to be suffered for earthquakes, wave and current action synchronously during construction and service period. In order to investigate the dynamic responses of the whole bridge piers under joint earthquakes, waves and current action. This paper is based on a deep-water high-pier whole bridge as a prototype, pursuant to the elastic similarity law, the shaking table test under the joint action of an earthquake, wave and current is carried out for a 1:220 geometric scale model. At different water levels, a white noise test and independent earthquake action, joint wave and current action, and joint earthquake, wave and current action were carried out. The test research found that the presence of water decreases the natural frequency of the piers. The effect of water on the higher-order frequencies are greater than those of the low-order frequencies. The dynamic amplification factors of bridge piers in water are greater than those without water, but the effect of the water level is decreases exceed a certain water level. Earthquakes have a great influence on dynamic amplification factors and strains, and the waves and current have a greater influence on hydrodynamic pressures. Earthquakes, waves, and current have an influence on the bridge, but the dominant is earthquakes. The influence of the waves and current on the bridge can not be ignored. The peak strains and peak hydrodynamic pressures under joint earthquake, wave and current action are evaluated to the peak strains and peak hydrodynamic pressures under superimposed independent earthquake, independent wave and current action. It can be seen that the joint effect is not a simple superposition effect. There are complex interactions between the earthquake, wave and current. Considering the effects of coupling, the whole bridge is more complex than a single pier, and the dynamic responses are complicated. Therefore, it is essential to investigate the joint action of earthquakes, wave and current in bridge design under complex circumstances.

Rapid post-earthquake damage assessment of ageing reinforced concrete bridge piers using time-frequency analysis

The role of signal-based nonlinear system identification methods for the rapid post-earthquake damage assessment of reinforced concrete (RC) bridge piers is explored. Experimental data from the shaking table tests of six RC columns with and without corrosion damage are used as benchmark data. The specimens are excited under three different ground motions with different time-series characteristics, structural detailing, and corrosion levels. The proposed system identification methods make use of accelerations alone (but not displacements as these are costly in-situ) to estimate the instantaneous frequency. The Wigner-Ville distribution and Hilbert transform are utilised due to their high resolution in both time and frequency domains. A combination of modal filtering and thresholding, using instantaneous amplitudes, is employed to attenuate the unreliable spikes in the Hilbert transform’s instantaneous frequency estimates. Their performance is benchmarked against a moving linear regression and standard white-noise tests. The comparison of the experimental results and time-frequency analysis indicates that the Wigner-Ville distribution and the Hilbert transform can produce reliable rapid damage detection when the response amplitude is large. The Wigner-Ville distribution has better robustness and higher resolution. The robustness of the more computationally efficient Hilbert transform can be significantly improved by the introduction of modal filtering and thresholding.

A white noise test under weak conditions

We consider the well-known white noise test that is available in the literature. Its null distribution is known to be asymptotically normal under different sets of conditions for processes with finite 8th moment. We show that for some specific models, the normality continues to hold under the finiteness of only the 4th moment. This includes various GARCH models, stochastic autoregressive volatility model and autoregressive conditional duration model. Under the alternate hypothesis, for specific models such as infinite order moving average, non-linear moving average and bilinear models with finite 4th moment, we show that with suitable centering and scaling, which is of a different order of magnitude compared to the null case, the test statistic is asymptotically normal.

Spectral Inference under Complex Temporal Dynamics

We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.

A MAX-CORRELATION WHITE NOISE TEST FOR WEAKLY DEPENDENT TIME SERIES

This article presents a bootstrapped p-value white noise test based on the maximum correlation, for a time series that may be weakly dependent under the null hypothesis. The time series may be prefiltered residuals. The test statistic is a normalized weighted maximum sample correlation coefficient $ \max _{1\leq h\leq \mathcal {L}_{n}}\sqrt {n}|\hat {\omega }_{n}(h)\hat {\rho }_{n}(h)|$, where $\hat {\omega }_{n}(h)$ are weights and the maximum lag $ \mathcal {L}_{n}$ increases at a rate slower than the sample size n. We only require uncorrelatedness under the null hypothesis, along with a moment contraction dependence property that includes mixing and nonmixing sequences. We show Shao’s (2011, Annals of Statistics 35, 1773–1801) dependent wild bootstrap is valid for a much larger class of processes than originally considered. It is also valid for residuals from a general class of parametric models as long as the bootstrap is applied to a first-order expansion of the sample correlation. We prove the bootstrap is asymptotically valid without exploiting extreme value theory (standard in the literature) or recent Gaussian approximation theory. Finally, we extend Escanciano and Lobato’s (2009, Journal of Econometrics 151, 140–149) automatic maximum lag selection to our setting with an unbounded lag set that ensures a consistent white noise test, and find it works extremely well in controlled experiments.

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN).

A Study on the Relationships of White Noise Environment, Test Anxiety, Academic Self-Efficacy, and Learning Flow of University Students

A Study on the Relationships of White Noise Environment, Test Anxiety, Academic Self-Efficacy, and Learning Flow of University Students

Application of Clustering and VARIMA for Rainfall Prediction

Global warming causes climate change throughout the world which has an impact on changes in erratic rainfall. For fishermen, the estimated rainfall is an important thing to know before sailing. VARIMA is divided into VAR, VMA, VARMA, and VARIMA. This study uses a rainfall dataset consisting of rainfall (mm), temperature (°C), humidity (%) and wind speed (km/hour) with observations per day for one year in 2018. Training data is data that starts from 1 January 2018 until 30 November 2018 and the testing data starts on 1 December 2018 until 31 December 2018. Clustering was carried out in the area around Lampulo Pelabuhan Perikanan Samudera (PPS) which are Bandar Raya, Lampineung, Kuta Radja, and Kuta Alam. The best model obtained is VARIMA (1,1,2), which is based on the smallest AIC and BIC values, which are 8.5104 and 9.0581. The VARIMA (1,1,2) model also fulfills the white noise test, so it can be concluded that the data does not have autocorrelation and model can be used for predictions. Estimated values of rainfall, temperature, humidity and wind speed are not much different from actual values, with MAPE of all variables are relatively low.

Design of LQR Tracking Controller Combined With Orthogonal Collocation State Planning for Process Optimal Control

As one of the most important optimization methods for process optimal control, orthogonal collocation method has been widely used. However, this kind of optimization method is generally an open-loop optimization frame, which makes the model disturbances greatly affect the control performance and quality. In order to improve the optimal control performance of dynamic systems with disturbances, this work proposed an orthogonal collocation optimization-based linear quadratic regulator (OC-LQR) control method for process optimal control problems. Firstly, the orthogonal collocation method is derived in detail to transform the optimal control problem so that optimal state curves can be accordingly calculated. Then, an improved LQR controller design is proposed by using the obtained optimal state curves so as to construct the feedback control frame. On this basis, a state planning-based LQR tracking control is established with closed-loop control characteristics to tackle model disturbance problem. Meanwhile, the detailed Simulink model of the proposed control method is constructed for simulation execution. Finally, the proposed control structure is tested on a classical process optimal control problem with model disturbance (white noise) test and Gaussian mixture noise test to verify the performance of the proposed method. Simulation studies show that the control performance of the proposed method is excellent, where the mean absolute error of state curve tracking averagely reduces by 34.73% and the minimal performance index improves by 64.44% when compared with the classical orthogonal optimal control method. Simulation results demonstrate the effectiveness of the proposed planning-based tracking control framework.

Experimental study of structural change detection using data-driven reduced-order models

This paper presents a comparison of the effectiveness of three different data-driven vibration-based approaches in detecting and locating structural changes in a 1/4 scale six-storey single-bay steel frame laboratory structure from measured experimental input-output data obtained from band-limited white-noise base-excitation tests. The implemented methodologies are based on reduced-order models obtained using three input-output system identification approaches: a system realisation algorithm using information matrices, a general time-domain least-squares identification method, and a non-parametric chain-like system identification approach. Variations in the estimated reduced-order models are then used to indicate the presence and infer the location of actual structural changes in the test structure. The results of this experimental study show that even though the changes introduced by the various levels of damage in the structure were robustly detected in the presence of modelling, measurement, and data processing errors using reduced-order representations, the identified change locations in the reduced-order model could not be, in some cases, reliably correlated with the actual damage location in the structure.