Autocorrelation Research Articles

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian white noise as a random factor and uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to decompose the data set finely. Each modal component is evaluated by sample entropy (SE) analysis so that the data set can be reconstructed according to the sample entropy value to retain key information. In addition, the model uses partial autocorrelation function (PACF) to determine the correlation between intrinsic modal function (IMF) and historical data. Then, the global search whale optimization algorithm (GSWOA) is used to accurately determine the parameters of kernel extreme learning machine (KELM), which forms the basis of the dam deformation prediction model based on multi-scale adaptive kernel function. The case analysis shows that CEEMDAN-SE-PACF can effectively extract signal features and identify significant components and trends so as to better understand the internal deformation trend of the dam. In terms of algorithm optimization, compared with the WOA algorithm and other algorithms, the results of the GSWOA algorithm are significantly better than other algorithms and have the optimal convergence. In terms of prediction performance, CEEMDAN-SE-PACF-GSWOA-KELM is superior to the CEEMDAN-WOA-KELM, GSWOA-KELM, CEEMDAN-KELM, and KELM models, showing higher accuracy and stronger stability. This improvement is manifested in the decrease of root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) and the improvement of the R square (R2) value close to 1. These research results provide a new method for dam safety monitoring and evaluation.

Modelling and predicting annual rainfall over the Vietnamese Mekong Delta (VMD) using SARIMA

Climate and rainfall are extremely non-linear and complicated phenomena, which require numerical modelling to simulate for accurate prediction. We obtained local historical rainfall data for 12 meteorological stations in the Vietnamese Mekong Delta (VMD) for the 45-year period 1978–2022, to predict annual rainfall trends. A statistical time series predicting technique was used based on the autoregressive integrated moving average (ARIMA) model. We utilized the seasonal ARIMA process of the form (p,1,q)(P,1,Q) for our study area. The best seasonal autoregressive integrated moving average (SARIMA) models were then selected based on the autocorrelation function (ACF) and partial autocorrelation function (PACF), the minimum values of Akaike Information Criterion (AIC) and the Schwarz Bayesian Information (SBC). The seasonal autoregressive integrated moving average model with external regressors (SARIMAX) was discovered, and a series of SARIMA models of various orders were estimated and diagnosed. To evaluate model fitting, we used the Nash–Sutcliffe coefficient (Nash) and the root-mean-square error (RMSE). The study has shown that the SARIMA (1, 1, 1)(2, 1, 1)11 and SARIMA (1, 1, 1)(2, 1, 1)12 model were appropriate for analyzing and forecasting future rainfall patterns at particular meteorological station in the VMD. The results showed the SARIMA model is more reliable and provides more accurate projections than other commonly used statistical methods, notably interval forecasts. We found that interpretable and reliable near-term location-specific rainfall predicts can be provided by the SARIMA-based statistical predicting model.

Investigation of the variability applying classical (MK-SR) and modern (ITTA-TPSC) trend methods to meteorological parameters of Marmara Basin in Turkey

Scientific studies for countries experiencing climate change have accelerated recently. Turkey is among the countries experiencing climate change. Studies carried out for this country or its basins are of great importance both for the basin and the country. Therefore, Marmara Basin, where Turkey's population density is highest and industrialization is at the most advanced level, is selected in this study. The length of meteorological data is 45 years. The analysis of these data is made with Mann-Kendall (MK) and Spearman Rho (SR), which are classical trend methods, and Innovative Triangle Trend Analysis (ITTA) and Trend Polygon Star Concept (TPSC), which are modern trend methods. When the studies on Marmara basin are examined, there is no study that includes these four methods. Therefore, this study stands out in this respect. Because these analysis methods will reveal a new idea by applying them to hydro-meteorological data within the basin area. Before applying MK method, serial correlation method is applied to the meteorological parameter data of all stations. When the analysis results are examined; a general increasing trend is observed in precipitation data for ITTA and TPSC methods. No trend is observed in precipitation data for MK and SR methods. A general increasing trend is observed in temperature data for ITTA, MK and SR methods. However, a generally decreasing trend is observed in temperature data for TPSC method. While there is generally no trend in relative humidity data for MK and SR methods, an increasing trend is observed for ITTA and TPSC methods.

A novel numerical reconstruction method of coal based on characteristics of the cleat system

To effectively characterize the characteristics of the pore network of coal and to provide a realistic flow channel for flow simulation, a numerical reconstruction method of coal based on characteristics of the cleat system was proposed. Based on the real coal obtained through high-resolution micrometer CT, the cleat system characteristic parameters are defined and extracted. Then, a series of coals with different pore characteristics at the same scale and the same pore characteristics at different scales were reconstructed. Besides, the applicability of box dimension in calibrating the pore characteristics of coals was quantitatively analyzed. The results show that the coals reconstructed based on the method proposed in this paper can truly restore the pore characteristics of the real coal, especially the non-extension features of cleats; When the cleat system characteristic parameters remain consistent, the reconstructed coals maintain the same pore characteristics, with an average error of 3.37 % in the autocorrelation function, 0.78 % in the variation function and 1.94 % in the average percolation probability function. The box dimension can effectively identify the differences in pore characteristics of coals. At the same scale, as the distance between face cleats increases, the value of the box dimension of the reconstructed coals monotonically decreases, with a fitting slope of −0.22; At different scales, when the cleat system characteristic parameters remain the same, the value of the box dimension of the reconstructed coals is consistent, with an average relative error of 0.34 %. The research results provide a reasonable and effective flow channel for the microscopic/mesoscopic flow simulation of coalbed methane and provide a reference for the quantitative calibration of the pore characteristics of coals.

Automated construction of effective potential via algorithmic implicit bias

We introduce a novel approach for decomposing and learning every scale of a given multiscale objective function in Rd, where d⩾1. This approach leverages a recently demonstrated implicit bias of the optimization method of gradient descent [44], which enables the automatic generation of data that nearly follow Gibbs distribution with an effective potential at any desired scale. One application of this automated effective potential modeling is to construct reduced-order models. For instance, a deterministic surrogate Hamiltonian model can be developed to substantially soften the stiffness that bottlenecks the simulation, while maintaining the accuracy of phase portraits at the scale of interest. Similarly, a stochastic surrogate model can be constructed at a desired scale, such that both its equilibrium and out-of-equilibrium behaviors (characterized by auto-correlation function and mean path) align with those of a damped mechanical system with the original multiscale function being its potential. The robustness and efficiency of our proposed approach in multi-dimensional scenarios have been demonstrated through a series of numerical experiments. A by-product of our development is a method for anisotropic noise estimation and calibration. More precisely, Langevin model of stochastic mechanical systems may not have isotropic noise in practice, and we provide a systematic algorithm to quantify its covariance matrix without directly measuring the noise. In this case, the system may not admit closed form expression of its invariant distribution either, but with this tool, we can design friction matrix appropriately to calibrate the system so that its invariant distribution has a closed form expression of Gibbs.

Trend Analysis of Climatic Variables in the Cross River Basin, Nigeria

There have been several incidences of flood recently, which are believed to be aggravated by increased climatic variables as a result of perceived changes in climatic conditions (due to climate change) in the Cross River Basin. The basin is the most extensively developed and used river basin in the management of the water resources of the Cross River and Akwa Ibom States in Nigeria. In this paper, 30 years (from 1992 to 2021) of hydro-meteorological data (annual average rainfall, maximum and minimum temperatures, hu midity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure) from four stations in the Cross River Basin were obtained from the Nigerian Meteorological Agency (NIMET), Abuja and subjected to trend detection analysis using the Mann–Kendall test to determine the trend in climatic parameters. The results indicate that there is a significant upward trend in annual rainfall in Ogoja but a downward trend in Calabar. The evaporation trend is significantly downward in Eket, whereas in Calabar, there is an upward trend in solar radiation. Generally, there is a significant rise in annual maximum temperature across the basin. Serial correlation and segmented regression analyses were performed to measure the impact of fluctuations in monthly and long-term Tahiti and Darwin’s Sea level pressures on the climatic variables at the Cross River Basin catchment. These analyses were necessary to determine the extent of the influence of the El Nino Southern Oscillation climatic cycle. The analyses show no significant association between the El Niño Southern Oscillation (ENSO) and rainfall or between the ENSO and runoff in the catchment. This implies that the impact of the ENSO on rainfall and runoff in the Cross River Basin catchment is not considerable. The intercepts derived from the segmented regression in Eket and Ogoja show significant positive trends in both areas for rainfall and runoff. The trends in intercepts suggest that there are external factors influencing rainfall and runoff other than ENSO events, thus strengthening the assertion of climate change. Results from this study will facilitate the understanding of the variability in climatic parameters by stakeholders in the basin, researchers, policymakers, and water resource managers.

Photon correlations in the collective emission of hybrid gold-(CdSe/CdS/CdZnS) nanocrystal supraparticles

We investigate the photon statistics of the light emitted by single self-assembled hybrid gold-CdSe/CdS/CdZnS colloidal nanocrystal supraparticles through the detailed analysis of the intensity autocorrelation function g (2)(τ). We first reveal that, despite the large number of nanocrystals involved in the supraparticle emission, antibunching can be observed. We then present a model based on non-coherent Förster energy transfer and Auger recombination that well captures photon antibunching. Finally, we demonstrate that some supraparticles exhibit a bunching effect at short time scales corresponding to coherent collective emission.

Sliding time synchronous averaging based on independent extended autocorrelation function for feature extraction of bearing fault

Extracting fault feature is a key challenge during the early failure stage of bearings due to weak fault components and strong background noises. To solve this problem, an extension operation is introduced, and a novel algorithm called independent extended autocorrelation function is proposed. Subsequently, based on time synchronous averaging, the sliding time synchronous averaging method is developed to further enhance periodic components. The time dimension is added to generate a series of periodic impulses, which is advantageous to directly extract the fault characteristic. Therefore, the presented algorithm is called the sliding time synchronous averaging based on independent autocorrelation function (STSA-IEACF). Numerically simulated signals and two experimental datasets are employed to compare the performance of the STSA-IEACF with the mainstream blind deconvolution methods in fault feature extraction of bearing. The results show that the proposed algorithm performs better than the others in this terms of extracting fault characteristics.

Single channel sound source localization by using combinations of multiple tubes

A method for localizing the direction arrival of the sound source with a single channel is proposed with the configuration of system consists of a single channel acoustic sensor and multiple inlets with specific path length of tubes or pipes. The time delay of arrival at each inlet can be estimated from the autocorrelation function measured at the sensor and the positions of peak in autocorrelation function are changed according to the incidence angle. By using this concept, the basic configurations are considered and the related investigations are conducted as solutions to several problems that need to be solved. The proposed concept is validated experimentally in anechoic chamber and outdoor conditions. As a method to expand the range of observation angle, the configuration with difference length is investigated and it is confirmed that the unique solution for the whole direction can be obtained by a combination of tubes and inlets.

Research on predicting heat loads based on extracting temporal and spatial features of multiple buildings using data-driven methods

Accurate heat load prediction is the key to ensure the stable operation of thermal system and effective planning of thermal resources. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this study, we propose a data-driven model based on spatiotemporal coupling to predict short-term heat load. Firstly, the spatial and temporal correlation and the correlation among features of various buildings are analyzed by using the autocorrelation function and Spearman correlation coefficient. Secondly, synchronous wavelet transform is used to eliminate high frequency noise of heat load. Secondly, the spatial and temporal characteristics of building heat load are extracted by using the improved Informer model which is concerned with multi-scale graphs. The experimental results show that the proposed model has better predictive performance than the baseline model. It provides a reference for the accurate regulation of thermal system.

Molecular dynamics study of the sonic horizon of microscopic Laval nozzles.

A Laval nozzle can accelerate expanding gas above supersonic velocities, while cooling the gas in the process. This work investigates this process for microscopic Laval nozzles by means of nonequilibrium molecular dynamics simulations of stationary flow, using grand-canonical Monte Carlo particle reservoirs. We study the steady-state expansion of a simple fluid, a monoatomic gas interacting via a Lennard-Jones potential, through an idealized nozzle with atomically smooth walls. We obtain the thermodynamic state variables pressure, density, and temperature but also the Knudsen number, speed of sound, velocity, and the corresponding Mach number of the expanding gas for nozzles of different sizes. We find that the temperature is well defined in the sense that the each velocity components of the particles obey the Maxwell-Boltzmann distribution, but it is anisotropic, especially for small nozzles. The velocity autocorrelation function reveals a tendency towards condensation of the cooled supersonic gas, although the nozzles are too small for the formation of clusters. Overall we find that microscopic nozzles act qualitatively like macroscopic nozzles in that the particles are accelerated to supersonic speeds while their thermal motion relative to the stationary flow is cooled. We find that, like macroscopic Laval nozzles, microscopic nozzles also exhibit a sonic horizon, which is well defined on a microscopic scale. The sonic horizon is positioned only slightly further downstream compared to isentropic expansion through macroscopic nozzles, where it is situated in the most narrow part. We analyze the sonic horizon by studying space-time density correlations, i.e., how thermal fluctuations at two positions of the gas density are correlated in time and find that after the sonic horizon there are indeed no upstream correlations on a microscopic scale.

Divergence or convergence: trade globalization, regulatory and environmental quality in QUAD countries – a robust ARDL and decoupling index inquiry

PurposeThe Quadrilateral Group for Security Dialogues (QUAD), comprising Japan, Australia, India, and the USA, has experienced a noteworthy escalation in both environmental depletion and economic performance over the preceding 2 decades. Consequently, it becomes crucial to outline strategies through which the QUAD can attain a harmonious equilibrium between sustainable economic growth and ecological well-being. This research endeavors to dissect the intricate relationships among trade globalization, regulatory quality, and environmental sustainability within the QUAD for the period 1990–2021. The study checks for the Pollution Haven Hypothesis (PHH), and Pollution Halo Effect Hypothesis (PHEH).Design/methodology/approachThe long-run association is based on the autoregressive distributed lag (ARDL) model and bounds test approach to cointegration while divergence or convergence is studied with the help of decoupling index (DI). Results have been verified by applying serial correlation LM test, autoregressive conditional heteroskedasticity (ARCH), and cumulative sum of recursive residuals (CUSUM) tests to ensure the robustness and stability of the model.FindingsThe empirical results of this study affirm the applicability of the PHEH in the contexts of India, Japan, and the USA, whereas the PHH is validated in the case of Australia. Furthermore, the analysis reveals the existence of relative decoupling solely in the case of India. This testifies that the rate of growth of the Indian economy surpasses the rate of growth in ecological footprint (EF), indicating a relative reduction in the intensity of environmental impact per unit of economic growth.Research limitations/implicationsThe empirical findings of our study suggest that countries with effective regulatory systems are better positioned to control and mitigate the potential adverse environmental effects resulting from increased global trade. Thus, policymakers are prompted to reassess the development policies for sustainable economic growth that will minimize adverse environmental repercussions. The implication of the negative relation between urbanization and EF is paramount for policymakers in developing countries seeking strategies for balanced urban development that aligns with environmental sustainability.Originality/valueThe present study is a unique exploration of the impact of trade globalization and regulatory quality on EF, specifically on PHH/PHEH in the context of QUAD.

Fundamental Frequency Extraction by Utilizing Accumulated Power Spectrum based Weighted Autocorrelation Function in Noisy Speech

This research suggests an efficient idea that is better suited for speech processing applications for retrieving the accurate pitch from speech signal in noisy conditions. For this objective, we present a fundamental frequency extraction algorithm and that is tolerant to the non-stationary changes of the amplitude and frequency of the input signal. Moreover, we use an accumulated power spectrum instead of power spectrum, which uses the shorter sub-frames of the input signal to reduce the noise characteristics of the speech signals. To increase the accuracy of the fundamental frequency extraction we have concentrated on maintaining the speech harmonics in their original state and suppressing the noise elements involved in the noisy speech signal. The two stages that make up the suggested fundamental frequency extraction approach are producing the accumulated power spectrum of the speech signal and weighting it with the average magnitude difference function. As per the experiment results, the proposed technique appears to be better in noisy situations than other existing state-of-the-art methods such as Weighted Autocorrelation Function (WAF), PEFAC, and BaNa.

Energy fluctuations of a Brownian particle freely moving in a liquid

We study the statistical properties of the variation of the kinetic energy of a spherical Brownian particle that freely moves in an incompressible fluid at constant temperature. Based on the underdamped version of the generalized Langevin equation that includes the inertia of both the particle and the displaced fluid, we derive an analytical expression for the probability density function of such a kinetic energy variation during an arbitrary time interval, which exactly amounts to the energy exchanged with the fluid in absence of external forces. We also determine all the moments of this probability distribution, which can be fully expressed in terms of a function that is proportional to the velocity autocorrelation function of the particle. The derived expressions are verified by means of numerical simulations of the stochastic motion of a particle in a viscous liquid with hydrodynamic backflow for representative values of the time-scales of the system. Furthermore, we also investigate the effect of viscoelasticity on the statistics of the kinetic energy variation of the particle, which reveals the existence of three distinct regimes of the energy exchange process depending on the values of the viscoelastic parameters of the fluid.

Statistical analysis of bistable feature in a 1.55-[formula omitted] vertical cavity emitting laser

Optical bistability in telecom-band lasers has attracted significant attention because of its immense potential for applications in the fields of optical communications and optical computing. In this paper, we report on an experimental investigation of the statistical properties of a 1.55-μm bistable VCSEL. The VCSEL exhibits mode switching at a current of 5.41 mA and 4.15 mA when the pump continuously increases and decreases, respectively. Both of these switching exhibit a bistable region, indicating a polarization bistability induced by the gain saturation. By calculation of second-order autocorrelation functions with injection current, we identify different regions associated with the dynamics of different dominant modes. In particular, the statistical analysis reveals more properties of the bistable laser, such as the intensity fluctuations induced by different kinds of noise.

MONTE CARLO AND NUMERICAL METHODS TO SOLVE THE TIME SERIES MODEL

In this paper we will solve the nonlinear system of equations in the parameters of the time series model by Monte Carlo methods and by numerical methods. When we identify the variance and the inter-covariances of time series, we obtain, dividing by variance, a quadratic nonlinear system of equation that does not contain the variance of wihite noise. We use only the autocorrelation function. AMS Subject Classification: 62M10, 91B84, 65C05.

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model.

A time averaged semiclassical approach to IR spectroscopy.

We propose a new semiclassical approach to the calculation of molecular IR spectra. The method employs the time averaging technique of Kaledin and Miller upon symmetrization of the quantum dipole-dipole autocorrelation function. Spectra at high and low temperatures are investigated. In the first case, we are able to point out the possible presence of hot bands in the molecular absorption line shape. In the second case, we are able to reproduce accurate IR spectra as demonstrated by a calculation of the IR spectrum of the water molecule, which is within 4% of the exact intensity. Our time averaged IR spectra can be directly compared to time averaged semiclassical power spectra as shown in an application to the CO2molecule, which points out the differences between IR and power spectra and demonstrates that our new approach can identify active IR transitions correctly. Overall, the method features excellent accuracy in calculating absorption intensities and provides estimates for the frequencies of vibrations in agreement with the corresponding power spectra. In perspective, this work opens up the possibility to interface the new method with the semiclassical techniques developed for power spectra, such as the divide-and-conquer one, to get accurate IR spectra of complex and high-dimensional molecular systems.

Advanced Excitation Symmetry Analysis Method (ESAM) for improved automated ultrasonic testing of polished materials

One of the strategic plans of the international NDT community is to define standards for developing advanced and digitalized non-destructive testing for automated set-up in mass production [1,2]. Excitation Symmetry Analysis Method (ESAM) are new coded signal processing tools exploring symmetry properties [3]. ESAM method aims to extract the nonlinearity from an ultrasound output by studying the property of symmetry of the transfer function. More precisely, the extraction of nonlinearity is done using basis function associated to a group which is associated to an a priori transfer function. As soon as the linear tomography uses the invariant properties of amplitude dependence, nonlinear tomography should exploit the invariance properties (or symmetry properties) of the complex system. Consequently, the invariance of the stationary properties of a complex medium would be supposed to be associated to a signature of the degradation, that will be extracted with advanced signal processing tools. The idea remains the same: studying the symmetry and more precisely the time reversal one and the inversion. A signal processing is used to measure discrepancies in the autocorrelation function (output signal called chirp coda) for two inputs: a signal and its inversion (pulse inversion). The experiments were done on different sample with and without cracks. It has been shown that when the nonlinearities are extracted from the chirp coda amplitude are non- negligible in crack area and nonlinearities are out of phase. Then, mixing symmetries like time reversal and pulse inversion enable the detection of defect that are difficult to detect with conventional ultrasound method. Indeed, for such crack a destructive mechanical analysis called Jomini is done where the material is polished and examined with microscope. The sensitivity improvement of chirp-coded signal processing has been validated in various domains. Coded excitation techniques, used in communication systems such as radar and sonar provide improved SNR without increasing the amplitude of excitation. The typical test equipment consists of a preamplifier Juvitek TRA-02 (0.02 - 5 MHz) connected to a computer, an amplifier ENI model A150 (55 dB at 0.3-35 MHz), a shear wave transducer Technisonic (2.25 MHz), and a longitudinal wave transducer Panametrics V155 (5 MHz). The experimental device was tested with the V3 calibration block, improved, and specially scaled in order to access to a wide range of multivalued parameters: mechanical properties, ultrasonic parameters (celerity and attenuation) and local geometric data.

Pliocene–Pleistocene Paleomagnetic Secular Variation and Time‐Averaged Field From the Northern Volcanic Zone of the Andes, Colombia

AbstractPaleomagnetic results obtained from 38 Pliocene—Pleistocene volcanic flows from the Knot of the Pastos and surroundings of Puracé volcano and Popayán (southwestern Colombia) are presented. Using stringent quality criteria and excluding sites that classify as representatives of transitional states of Earth's field, a selected group of 27 sites (16 with normal polarity and 11 with reversed polarity) was obtained with a mean direction (Dec = 357.8°, Inc = 6.4°, α95 = 7.5° and κ = 15) that coincides with the geocentric axial dipole field (GAD: Dec = 0o, Inc = 3.2o) and, unlike similar studies at similar latitudes, does not coincide with the GAD plus a 5% axial quadrupolar component (Dec = 0°, Inc = −1.08°). However, when serial correlation (SC) among several sites with high inclination anomalies is taken into consideration, the mean direction of two resulting groups of sites shows greater consistency with a field that includes a quadrupolar component. It is interpreted that the sites treated for SC record states of the field similar to today's field in the area of study, which is being affected by the South Atlantic Anomaly.