Residual Cross-correlation Research Articles

Heterogeneous Remote Sensing Image Matching Algorithm Based on Residual Pseudo-Siamese Convolution Cross-Correlation Network

Heterogeneous Remote Sensing Image Matching Algorithm Based on Residual Pseudo-Siamese Convolution Cross-Correlation Network

Multicopter Fault Detection and Identification via Data-Driven Statistical Learning Methods

This paper presents the introduction, investigation, and critical assessment of three data-driven methods for rotor failure detection and identification in a multicopter. These methods are based on aircraft attitude signals obtained from forward flight under turbulence and uncertainty. The knowledge-based method exploits the system rigid-body dynamics insight under the different rotor failures to construct a decision tree that detects and identifies the rotor failure simultaneously by how the roll, pitch, and yaw signals violate the statistical confidence limits immediately after failure. For the statistical time-series method, the development of stochastic time-series models and residual-based statistical hypothesis tests are discussed. Here, fault detection in the transient response is followed by identification after the signals reach a stationary state, after controller compensation, with the healthy and the different faulty models, respectively, in a two-step manner. The third method employs the healthy time-series model to extract a useful feature, which is the residual cross correlation, as an input to a neural network trained to achieve simultaneous rotor failure detection and identification. The time-series assisted neural network is capable of making decisions throughout the entire flight with an accuracy of 98.8%, with minimum computation time (less than 0.03 s) making it the best alternative for real-time monitoring.

Sensitivity and Uncertainty Analysis for Parameterization of Multiphase Flow Models

The long-standing question on the adequate description of multiphase flow in porous media may be ultimately decided based on the ability to estimate model parameters with sufficient accuracy that make the models distinguishable. Since the most-common Darcy scale multiphase flow models all use a somewhat phenomenological relative permeability or resistance factor formulation, the key question is what the associated uncertainty really is when derived from flow experiments by inverse modeling. In this work, a recently developed workflow for systematic assessment of uncertainty was used to analyze the impact of the choice of relative permeability models and associated uncertainty. In an exemplary fashion, the Corey and LET relative permeability parameterizations were compared. The choice of Corey and LET models in the inverse modeling workflows showed differences in the derived relative permeability relations. The Corey parameterization is found to be more restrictive and imposed additional constraints on parameters. For example, varying the connate water saturation and residual oil saturation did not improve the match with experimental data. The pressure drop, saturation profiles and the capillary pressure–saturation relationship constrained the solution and imposing additional constraints on, e.g., residual oil saturation has very little impact on the result. In contrast, the LET function provided more degrees of freedom in order to accommodate the shape of the relative permeability curves. The findings also suggested that both Corey and LET models may not necessarily provide optimum parameterizations of the experimental data. The cross-correlations of fit parameters and non-Gaussian residuals indicated that we were still dealing with a phenomenological parameterization that is not yet the fully adequate description of the data. This may be the starting point for a comparison of different flow models beyond the uncertainty imposed by the choice of model parameterizations. Future work is aimed at assessing whether better choices in interpretation workflows and optimized experimental workflows can minimize these issues.

Pearson cross-correlation in the first four black hole binary mergers

We adopt the Pearson cross-correlation measure to analyze the LIGO Hanford and LIGO Livingston detector data streams around the events GW150914, GW151012, GW151226 and GW170104. We find that the Pearson cross-correlation method is sensitive to these signals, with correlations peaking when the black hole binaries reconstructed by the LIGO Scientific and Virgo Collaborations, are merging. We compare the obtained cross-correlations with the statistical correlation fluctuations arising in simulated Gaussian noise data and in LIGO data at times when no event is claimed. Our results for the significance of the observed cross-correlations are broadly consistent with those announced by the LIGO Scientific and Virgo Collaborations based on matched-filter analysis. In the same data, if we subtract the maximum likelihood waveforms corresponding to the announced signals, no residual cross-correlations persists at a statistically significant level.

Fundamental Plane of BOSS galaxies: correlations with galaxy properties, density field, and impact on RSD measurements

ABSTRACT Fundamental Plane (FP) of elliptical galaxies can be used to predict the intrinsic size of galaxies and has a number of plausible application to study cosmology and galaxy physics. We present a detailed analysis of the FP of the SDSS-III BOSS LOWZ and CMASS galaxies. For the standard FP, we find a strong redshift evolution for the mean residual and show that it is primarily driven by the redshift evolution of the surface brightness of the galaxies. After correcting for the redshift evolution, the FP residuals are strongly correlated with the galaxy properties and some observational systematics. We show that the variations in the FP between the central and satellite galaxies, which have been observed in the literature, can primarily be explained by the correlation of the FP with the galaxy luminosity. We also measure the cross-correlations of the FP residuals with the galaxy density field. The amplitude of the cross-correlations depends on the galaxy properties and environment with brighter and redder galaxies showing stronger correlation. In general, galaxies in denser environments (higher galaxy bias) show stronger correlations. We also compare FP amplitude with the amplitudes of intrinsic alignments (IA) of galaxy shapes, finding the two to be correlated. Finally, using the FP residuals, we also study the impact of IA on the constraint of growth rate using redshift-space distortions (RSD). We do not observe any significant trends in measurements of the growth rate f as function of the amplitude of FP–density correlations, resulting in null detection of the effects of IA on the RSD measurements.

(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models

A portmanteau and a maximum statistic are proposed to discern strictly exogenous from predetermined regressors. Both test statistics rely on residual cross-correlations, pre-whitened via autoregressive sieves. Limiting normality of the portmanteau statistic and Gumbel-convergence of the maximum statistic are derived.

Investigation of anisotropic spatial correlations of intra-event residuals of multiple earthquake intensity measures using latent dimensions method

SUMMARYConsidering spatial correlation of multiple earthquake intensity measures (IMs) is of particular importance in loss assessment of spatially distributed assets. This subject has been investigated in previous studies under the assumption of isotropy. Considering the fact that the assumption of isotropy is not valid in general, the present study employs a non-separable covariance model based on latent dimensions method to investigate anisotropic properties of spatial correlations and cross-correlations of intra-event residuals of multiple earthquake IMs. This method leads to the generation of valid covariance matrix in order to model anisotropic spatially distributed multivariate random fields. Two sets of IMs are considered in this study; the first set consists of peak ground intensity values (acceleration, velocity, and displacement), and the second set consists of spectral accelerations at three different periods. Data of 10 earthquake events in California and Japan are utilized in this study to estimate parameters of marginal and cross-covariance models. Moreover, parameters of covariance model of regional site condition, which is considered as average shear wave velocity of top 30 m of soil profile (Vs30), are obtained in order to investigate the effect of local sited conditions on spatial correlations of IMs. It is shown that maximum range and anisotropy ratio of covariance models of intra-event residuals of IMs are correlated with those of Vs30 values. Also, it is observed that the anisotropy direction of residuals of IMs is consistent with anisotropy direction of Vs30 values. Finally, predictive models are proposed to obtain marginal and cross-covariance functions for different earthquake IMs considering anisotropy.

Evidence for Spin–Orbit Alignment in the TRAPPIST-1 System

Abstract In an effort to measure the Rossiter–McLaughlin effect for the TRAPPIST-1 system, we performed high-resolution spectroscopy during transits of planets e, f, and b. The spectra were obtained with the InfraRed Doppler spectrograph on the Subaru 8.2 m telescope, and were supplemented with simultaneous photometry obtained with a 1 m telescope of the Las Cumbres Observatory Global Telescope. By analyzing the anomalous radial velocities, we found the projected stellar obliquity to be λ = 1 ± 28° under the assumption that the three planets have coplanar orbits, although we caution that the radial-velocity data show correlated noise of unknown origin. We also sought evidence for the expected deformations of the stellar absorption lines, and thereby detected the “Doppler shadow” of planet b with a false-alarm probability of 1.7%. The joint analysis of the observed residual cross-correlation map including the three transits gave °. These results indicate that the the TRAPPIST-1 star is not strongly misaligned with the common orbital plane of the planets, although further observations are encouraged to verify this conclusion.

(Consistently) Testing Strict Exogeneity Against the Alternative of Predeterminedness

A portmanteau- and a maximum statistic are proposed to discern the null hypothesis of strict exogeneity from its alternative of predeterminedness in a linear model with shortmemory errors. Both test statistics rely on the vector-valued residual cross-correlation function, suitably pre-whitened via autoregressive sieves. Limiting normality of the portmanteautype statistic and Gumbel-convergence of the maximum statistic are derived under the null of strict exogeneity. While the first statistic is likely to be more powerful in detecting smoothly dampening cross-correlations, superiority of the latter is expected in situations of sparse alternatives. In contrast to classical generalized methods of moments based tests, the approach discussed here is able to consistently detect violations of the null at all possible lags without suffering size distortions. Monte-Carlo evidence confirms the accuracy of the asymptotic theory as a description of the finite sample behaviour. As an illustration, the proposed tests are applied to the study of dynamic effects of weather on orange juice prices.

Analysis of trends, periodicities, and correlations in the beryllium-7 time series in Northern Europe

The activity concentrations of beryllium-7, a natural radiotracer that is considered as a tracer of the stratospheric-tropospheric exchange, shows a distinct behaviour in Northern Europe compared to the central and southern parts of the continent. In this study, we use the measurements collected at four sampling stations in Scandinavia (Ivalo, Umea, Kista, Risoe) between 2001 and 2010 and investigate their trends, periodicities and residuals with the aim to further understand the common features in the beryllium-7 data records in northern sampling sites. The beryllium-7 activity concentrations exhibit statistically significant positive trends that range from an average value of 1.50%/year to an average value of 2.96%/year. We detect a one-year periodicity in all the sites, and in the southernmost site, Risoe in Denmark, additional higher-frequency harmonics. In the residual time series, we find outliers that represent occurrences of extremely high beryllium-7 activity concentration. Cross-correlations of the beryllium-7 residuals across the four sites decrease with increasing distance; similarly, as indicated by local Hurst exponents the records exhibit long-range correlations that weaken towards the end of the investigated period. To investigate the causes of the detected trends, we also calculate correlations between beryllium-7 and factors related to its production, transport and removal from the atmosphere: in particular, cross-correlations of the beryllium-7 residuals with residuals in sunspot number, local temperature, atmospheric pressure and precipitation, as well as Arctic Oscillation index and North Atlantic Oscillation index. Most of the obtained correlations, however, are not statistically significant, highlighting the need to analyse a longer time period in order to evaluate the impact of different factors on the airborne beryllium-7 activity concentration.

Lensing and the Warm-hot Intergalactic Medium

Abstract The correlation of weak lensing and Cosmic Microwave Anisotropy (CMB) data traces the pressure distribution of the hot, ionized gas and the underlying matter density field. The measured correlation is dominated by baryons residing in halos. Detecting the contribution from unbound gas by measuring the residual cross-correlation after masking all known halos requires a theoretical understanding of this correlation and its dependence with model parameters. Our model assumes that the gas in filaments is well described by a log-normal probability distribution function, with temperatures 105–7 K and overdensities ξ ≤ 100. The lensing-Comptonization cross-correlation is dominated by gas with overdensities in the range ξ ≈ [3–33]; the signal is generated at redshifts z ≤ 1. If only 10% of the measured cross-correlation is due to unbound gas, then the most recent measurements set an upper limit of on the mean temperature of the intergalactic Medium. The amplitude is proportional to the baryon fraction stored in filaments. The lensing-Comptonization power spectrum peaks at a different scale than the gas in halos, making it possible to distinguish both contributions. To trace the distribution of the low-density and low-temperature plasma on cosmological scales, the effect of halos will have to be subtracted from the data, requiring observations with larger signal-to-noise ratios than are currently available.

The effects of common risk factors on stock returns: A detrended cross-correlation analysis

In this paper, we investigate the cross-correlations between Fama and French three factors and the return of American industries on the basis of cross-correlation statistic test and multifractal detrended cross-correlation analysis (MF-DCCA). Qualitatively, we find that the return series of Fama and French three factors and American industries were overall significantly cross-correlated based on the analysis of a statistic. Quantitatively, we find that the cross-correlations between three factors and the return of American industries were strongly multifractal, and applying MF-DCCA we also investigate the cross-correlation of industry returns and residuals. We find that there exists multifractality of industry returns and residuals. The result of correlation coefficients we can verify that there exist other factors which influence the industry returns except Fama three factors.

Robust Acoustical Gas Leak Detection in the Presence of Correlated, Uncorrelated and Impulsive Noises, Using Wavelet-Based Optimized Residual Complexity

Leak detection based on acoustic emission needs robust time delay estimation in the presence of various types of noise. Considering several aspects of leakage signal and noise, the present work introduces wavelet based optimized residual complexity (WORC), in order to minimize the probability of false alarms in the presence of correlated, uncorrelated, and impulsive noise. The proposed method utilizes two approaches simultaneously. First, it optimizes residual complexity “for robustness against both correlated and uncorrelated noise”, then it employs wavelet basis in order to reduce the complexity of mixture of correlated and impulsive noise. In order to compare the resolution and percentage of false alarms of WORC with those of optimized residual complexity and cross correlation, we used both experimental and simulated leakage signals of a gas pipe. The results demonstrate the superiority of WORC in term of robustness in the presence of correlated, uncorrelated, and impulsive noise.

Transfer Function Models for Forecasting Domestic Water Use

The ability of transfer function models to forecast domestic water use is investigated. Five years monthly time series data on domestic water use, total rainfall and average temperature from Muscat was taken for this study. The transfer function models aim to describe the relationship between input and output systems using a ratio of the polynomials representing the Laplace Transforms of the output, input and the noise in the system. Total rainfall and average temperature were considered as the input series and the domestic water use as an out series. The input series were pre-whiten using Seasonal Autoregressive Integrated Moving Average (SARIMA) models which were identified by Sample Autocorrelation (SAC) and Partial Sample Autocorrelation (PSAC). Four preliminary transfer function models were postulated to describe the output series. The graphs of Sample Cross Correlation (SCC) of water use with rainfall and temperature were made. The final transfer function model was identified by investigating the Residual Sample Cross Correlation (RSSC) which had the form SARIMA(1,1,1)x(1,1,1). This model was then used to generate twelve months out of sample forecasts. The accuracy of forecast error was assessed by mean absolute deviation (MAD), mean square error (MSE) and mean absolute percent error (MAPE). All of these measures had reasonably small values which were 0.105, 0.013 and 1.37% respectively.

Comparison of decadal global water vapor changes derived from independent satellite time series

AbstractWe analyze trends in total column water vapor (TCWV) retrieved from independent satellite observations and retrieval schemes. GOME‐SCIAMACHY (Global Ozone Monitoring Experiment‐SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) measurements are carried out in the visible part of the solar spectrum and present a partly cloud‐corrected climatology that is available over land and ocean. The HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) product, provided by EUMETSAT's Satellite Application Facility on Climate Monitoring is based on passive microwave observations from the Special Sensor Microwave/Imager. It also includes the TCWV from cloudy pixels but is only available over oceans. The common observation time period is between 1996 and 2005. Due to the relatively short length of the period, the strong interannual variability with strong contributions from El Niño and La Niña events and the strong anomaly at the start of the common period, caused by the 1997/1998 El Niño, the observed trends should not be interpreted as long‐term climate trends. After subtraction of average seasonality from monthly gridded data, a linear model and a level shift model have been fitted to the HOAPS and GOME‐SCIAMACHY data, respectively. Autocorrelation and cross correlation of fit residuals are accounted for in assessing uncertainties in trends. The trends observed in both time series agree within uncertainty margins. This agreement holds true for spatial patterns, magnitudes, and global averages. The consistency increases confidence in the reliability of the trends because the methods, spectral range, and observation technique as well as the satellites and their orbits are completely independent of each other. The similarity of the trends in both data sets is an indication of sufficient stability in the observations for the time period of ≈ 10 years.

Localization and Tracking of Objects Using Cross-Correlation of Shadow Fading Noise

Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless transmitter localization scheme that combines the measured cross-correlation in shadow fading residuals between adjacent receivers using a Student-t Copula likelihood function is proposed. However, the long convergence time for this highly non-convex copula function might render our method unsuitable for tracking applications. Therefore, we present a faster tracking method where the velocity and heading of a mobile transmitter are estimated from α-Divergence between shadow fading signals and an onboard gyroscope respectively. To bind the localization error in this tracking method, the transmitter location estimates are smoothed by a Bayesian particle filter. The performance of our proposed localization and tracking method is validated over simulations and hardware experiments.

Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China

Optimal selection of observation locations is an essential task in designing an effective ecohydrological process monitoring network, which provides information on ecohydrological variables by capturing their spatial variation and distribution. This article presents a geostatistical method for multivariate sampling design optimization, using a universal cokriging (UCK) model. The approach is illustrated by the design of a wireless sensor network (WSN) for monitoring three ecohydrological variables (land surface temperature, precipitation and soil moisture) in the Babao River basin of China. After removal of spatial trends in the target variables by multiple linear regression, variograms and cross-variograms of regression residuals are fit with the linear model of coregionalization. Using weighted mean UCK variance as the objective function, the optimal sampling design is obtained using a spatially simulated annealing algorithm. The results demonstrate that the UCK model-based sampling method can consider the relationship of target variables and environmental covariates, and spatial auto- and cross-correlation of regression residuals, to obtain the optimal design in geographic space and attribute space simultaneously. Compared with a sampling design without consideration of the multivariate (cross-)correlation and spatial trend, the proposed sampling method reduces prediction error variance. The optimized WSN design is efficient in capturing spatial variation of the target variables and for monitoring ecohydrological processes in the Babao River basin.

Continuous correction of differential path length factor in near-infrared spectroscopy

In continuous-wave near-infrared spectroscopy (CW-NIRS), changes in the concentration of oxyhemoglobin and deoxyhemoglobin can be calculated by solving a set of linear equations from the modified Beer-Lambert Law. Cross-talk error in the calculated hemodynamics can arise from inaccurate knowledge of the wavelength-dependent differential path length factor (DPF). We apply the extended Kalman filter (EKF) with a dynamical systems model to calculate relative concentration changes in oxy- and deoxyhemoglobin while simultaneously estimating relative changes in DPF. Results from simulated and experimental CW-NIRS data are compared with results from a weighted least squares (WLSQ) method. The EKF method was found to effectively correct for artificially introduced errors in DPF and to reduce the cross-talk error in simulation. With experimental CW-NIRS data, the hemodynamic estimates from EKF differ significantly from the WLSQ (p < 0.001). The cross-correlations among residuals at different wavelengths were found to be significantly reduced by the EKF method compared to WLSQ in three physiologically relevant spectral bands 0.04 to 0.15 Hz, 0.15 to 0.4 Hz and 0.4 to 2.0 Hz (p < 0.001). This observed reduction in residual cross-correlation is consistent with reduced cross-talk error in the hemodynamic estimates from the proposed EKF method.

Using residual analysis, auto- and cross-correlations to identify key processes for the calibration of the SWAT model in a data scarce region

Abstract. Hydrological modeling poses a particular challenge in data scarce regions, which are often subject to dynamic change and thus of specific interest to hydrological modeling studies. When a small amount of data available for a catchment is opposed by extensive data requirements by the chosen hydrologic model, ways have to be found to extract as much information from the available data as possible. In a study conducted in the Xiangxi Catchment in the Three Gorges Region in China, the use of residual analysis as well as auto- and cross-correlations for enhanced model evaluation and for the identification of key processes governing the hydrological behavior of the catchment prior to model calibration was tested. The residuals were plotted versus various variables such as time, discharge and precipitation. Also, auto-correlations were calculated for measured and simulated discharge and cross-correlations of measured and simulated discharge with precipitation were analyzed. Results show that the analysis of residuals as well as auto- and cross-correlations can provide valuable information about the catchment response to rainfall events, which can be very helpful for calibration of hydrologic models in data scarce regions.

Performance analysis of multi user chaotic communication system using orthogonal chaotic vector

The quasi-orthogonal characteristic of the chaotic vectors leads to residual cross-correlation which results in Multiuser Interface (MUI) under multiple user scenarios. The effect of MUI increases in proportion to the number of users. In order to eliminate the effect of MUI we propose the use of Orthogonal Chaotic Vector (OCV) as spreading sequence to spread the message data in multi user chaotic communication system. Simulation work is carried out considering both AWGN and fading channel. Analytical expressions for BER are derived for both AWGN and fading channels and are compared with that of simulation results.