Smoothing Technique Research Articles

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Despite the great achievements in various SCI-based studies, accurate and efficient estimation of TC intensity still remains a challenge. In recent years, machine learning (ML) techniques have gained fast development and shown significant potential in dealing with big data, particularly with images. This study focuses on the objective estimation of TC intensity based on SCIs via a comprehensive usage of some advanced deep learning (DL) techniques and smoothing methods. Two estimation strategies are proposed and examined which, respectively, involve one and two functional stages. The one-stage strategy uses Vision Transformer (ViT) or Deep Convolutional Neutral Network (DCNN) as the regression model for directly identifying TC intensity, while the second strategy involves a classification stage that aims to stratify SCI samples into a few intensity groups and a subsequent regression stage that specifies the TC intensity. Further efforts are made to improve the estimation accuracy by using smoothing manipulations (via four specific smoothing techniques) in the scenarios of the aforementioned two strategies and their fusion. Results show that DCNN performs better than ViT in the one-stage strategy, while using ViT as the classification model and DCNN as the regression model can result in the best performance in the two-stage strategy. It is interesting that although the strategy of singly using DCNN wins out over any concerned two-stage strategy, the fusion of the two strategies outperforms either the one-stage strategy or the two-stage strategy. Results also suggest that using smoothing techniques are beneficial for the improvement of estimation accuracy. Overall, the best performance is achieved by using a hybrid strategy that consists of the one-stage strategy, the two-stage strategy and smoothing manipulation. The associated RMSE and MAE values are 9.81 kt and 7.51 kt, which prevail over those from most existing studies.

Identifying Multi-Scale Gravity and Magnetic Anomalies Using Statistical Empirical Mode Decomposition: A Case Study from the Eastern Tianshan Orogenic Belt

Identifying multi-scale anomalies that have simple forms and geological significance is critical for enhancing the interpretability of gravity and magnetic survey data. In recent years, empirical mode decomposition (EMD), which was developed as a significant data-driven approach for analyzing complex signals, has been widely used in identifying gravity and magnetic anomalies due to its advantages of adaptability to nonlinear and nonstationary data. Nevertheless, the traditional EMD method is usually sensitive to outliers and irregularly spaced data because of the interpolation process in the construction of envelopes. In this regard, an extended algorithm called statistical EMD (SEMD) has been proposed based on the smoothing technique. In this study, for validation purposes, the novel SEMD method has been employed to identify multi-scale gravity and magnetic anomalies. The sensitivities of local polynomial and cubic spline smoothing methods in SEMD to combination and arrangement patterns of field sources including the size, depth, and distance in gravity and magnetic anomaly identification were investigated and compared by forward modeling under the same conditions. The results demonstrated that the local polynomial smoothing method performed better than the cubic spline smoothing method. Thus, in the case study, the SEMD method using the local polynomial smoothing technique was employed for identifying multi-scale gravity and magnetic anomalies in the eastern Tianshan orogenic belt, northwestern China. It has illustrated that the SEMD method provides a novel and useful data-driven method for extracting gravity and magnetic anomalies.

Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.

Remaining useful lifetime prediction for predictive maintenance in manufacturing

Traditional maintenance approaches often result in either premature replacement of machine parts or downtime in production lines due to malfunctions. Consequently, these lead to significant amount waste in material, time and, ultimately, money. In this study, a machine learning-based predictive maintenance approach is proposed to predict the Remaining Useful Life of production lines in manufacturing. Using data collected from integrated IoT sensors in a real-world factory, we attempted to address the problem of predicting potential equipment failures on assembly-lines before they occur through machine learning models in real-time. To evaluate the effectiveness of the approach, we developed several predictive models using ML algorithms, including Random Forests (RF), XGBoost (XGB), Multilayer Perceptron (MLP) and Support Vector Regression (SVR) and compared the results for all possible variations. Furthermore, the impact of noise filtering, smoothing and clustering techniques on the performance of ML models were investigated. Among all the methods evaluated, RF, an ensemble bagging method, showed the best performance, followed by XGB and implemented in production systems. The implemented prediction model achieved successful results and was able to prevent about 42 percent of actual production line failures.

Predictive Power Control of PMSG based WECS: Development and Implementation for Smooth Grid Synchronisation, Balanced and Unbalanced Grid

In this paper, the distributed generation unit is controlled to operate in both stand-alone (SA) and grid-connected (GC) modes and offers a smooth transition between these modes using the model predictive control scheme. The permanent magnet synchronous generator (PMSG), along with a 2-level back-to-back converter, is named a DGU. A simple and intuitive approach using predictive control is presented in a stationary reference frame for a load-side converter (LSC). A voltage control loop is used during SA mode to fix the voltage and frequency at the load end. The smooth synchronization technique is developed without a phase-locked loop. During GC, a predictive power control regulates active and reactive power during the symmetry and unbalanced grid voltage conditions. The designed strategy makes the power factor improvement, reduction of grid current harmonics, and elimination of twice grid frequency ripple from power during grid unbalance possible. This work has proposed a solution to reduce the overall computational burden on the processor by eliminating the state prediction in each sampling period. The machine-end converter control is formulated in the rotor flux reference frame to keep the power flow across the dc-link capacitor constant. Comprehensive experiments are conducted with the designed scaled laboratory prototype to successfully validate the control’s theoretical claim.

Deterministic Bi-Criteria Model for Solving Stochastic Mixed Vector Variational Inequality Problems

In this paper, we consider stochastic mixed vector variational inequality problems. Firstly, we present an equivalent form for the stochastic mixed vector variational inequality problems. Secondly, we present a deterministic bi-criteria model for giving the reasonable resolution of the stochastic mixed vector variational inequality problems and further propose the approximation problem for solving the given deterministic model by employing the smoothing technique and the sample average approximation method. Thirdly, we obtain the convergence analysis for the proposed approximation problem while the sample space is compact. Finally, we propose a compact approximation method when the sample space is not a compact set and provide the corresponding convergence results.

Multi-Network-Based Ensemble Deep Learning Model to Forecast Ross River Virus Outbreak in Australia

Ross River virus (RRV) disease is one of the most epidemiological mosquito-borne diseases in Australia. Its major consequences on public health require building a precise and accurate model for predicting any forthcoming outbreaks. Several models have been developed by machine learning (ML) researchers, and many studies have been published as a result. Later, deep learning models have been introduced and shown tremendous success in forecasting, mainly the long short-term memory (LSTM), which performs significantly better than the traditional machine learning approaches. There are four common problems that previously developed models need to solve. They are exploding gradient, vanishing gradient, uncertainty and parameter bias. LSTM has already solved the first two problems, i.e. exploding and vanishing gradient problems, and the remaining two are overcome by [Formula: see text]-LSTM. However, developing a prediction model for the RRV disease is a challenging task because it presents a wide range of symptoms, and there needs to be more accurate information available on the disease. To address these challenges, we propose a data-driven ensemble deep learning model using multi-networks of LSTM neural network for RRV disease forecasting in Australia. Data is collected between 1993 and 2020 from the Health Department of the Government of Australia. Data from 1993 to 2016 is taken to train the model, while the data of 2016–2020 is used as a test dataset. Previous research has demonstrated the efficacy of both ARIMA and exponential smoothing techniques in the field of time-series forecasting. As a result, our study sought to evaluate the performance of our proposed model in comparison to these established parametric methods, including ARIMA and ARMA, as well as the more recent deep learning approaches such as encoder–decoder and attention mechanism models. The results show that [Formula: see text]-LSTM achieves higher accuracy and has a less mean-square error. We have also discussed the comparison of the models in detail. Such forecasting gives an insight into being well prepared and handling the situation of the outbreak.

Crustal Structure Beneath Mexico City From Joint Inversion of Receiver Functions and Dispersion Curves

AbstractOver the years, it has been clear to seismologists that the crustal structure beneath Mexico City is highly complex. This fact may influence the seismic waves' behavior when traveling through it. Consequently, many studies have been done to understand this crustal structure. This article presents a high‐resolution S‐wave velocity model obtained from joint inversion of radial receiver functions and group‐velocity dispersion curves beneath Mexico City (VMRFDC). This model includes azimuthal variations observed with receiver functions, which may be interpreted as crustal discontinuities. Receiver functions were retrieved from velocity and acceleration records, and dispersion curves were collected from a previous tomographic study. We performed a geostatistical analysis that revealed a horizontal spatial correlation of the crustal structure for up to 7.5 km. This distance was obtained from the S‐wave velocity omnidirectional semivariogram range per depth. Using ordinary kriging, we utilized this spatial correlation to estimate S‐wave velocities per depth and introduced two novel techniques: a resolution test for kriging and a geostatistical‐dependent smoothing technique. Finally, we compare our results with previous works, making our model consistent with geological and geophysical features reported in the literature.

IS THE RACEWALKING BIOMECHANICS SIGNIFICANTLY INFLUENCED BY COACHING?

While there is a significant number of analyses of influence of coaching and training content on performance, detailed analyses linking teaching the technique and biomechanics indicators in literature are rather scarce. The purpose of the study was to determine the differences between two groups of racewalkers in the selected variables describing their gaits. The research method consisted of measuring ground reaction forces as well as kinematics of motion recorded by video cameras and the OptoJumpNext system of 14 athletes from two distinct training groups of athletes walking at individually determined speed. To identify the differences in 9 key variables between the two groups, a two-sample unpaired T-test was performed, which was also controlled by Cohens' effect size indicator. The main finding of the study is that 5 key variables unrelated to walking speed were statistically different between the two groups, with Group A (predominantly "M"-shaped) having a lower ratio of peak ground reaction force (GRF) to GRF at 70% of the contact phase (p=0.0000), lower ratio of total GRF at the end and beginning of the interval 70% - 80% (p=0.0006), greater pelvic rotation (p=0.0056) and a more upright posture with lower forward pelvic tilt (p=0.0001) and lower backward thoracic tilt (p=0.0000). There were no significant differences between the two groups in two variables describing upper body movement i.e. arm-swing angle and thoracic rotation. Another variable (peak GRF) was also statistically different between the two group (p=0.0000), but this variable is related to the walking speed, which was not identical for the two groups. In conclusion, differences in the selected biomechanical indicators, that are trainable according to literature, may have been influenced by apparently different training approaches applied within the two groups of athletes. We suggest that, although the gait in racewalking is rather strictly defined by the rules, the above variables can and should be controlled and influenced by training to develop a smooth racewalking technique with lower peak ground reaction forces.

Optimal Capacity of a Battery Energy Storage System Based on Solar Variability Index to Smooth out Power Fluctuations in PV-Diesel Microgrids

Battery energy storage systems can be integrated with photovoltaic (PV)-diesel microgrids as an enabling technology to increase the penetration of PV systems and aid microgrid stability by smoothing out the power fluctuations of the PV systems. This paper focuses on this topic and aims to derive correlations between the optimal capacity of the smoothing batteries and variabilities in the daily solar irradiance. To this end, the two most commonly used moving average and ramp rate control techniques are employed on a real solar irradiance dataset with a 1-min resolution for a full calendar year across 11 sites in Australia. The paper then presents the developed empirical model, based on linear regressions, to estimate the batteries’ optimal capacity without requiring detailed simulation studies, which are useful for practitioners at the early stages of a project’s feasibility evaluation. The performance of the developed technique is validated through numerical simulation studies in MATLAB®. The study demonstrates that the empirical model provided reasonably accurate estimates when using the moving average smoothing technique but had limited accuracy under the ramp rate control technique.

Adaptive quadtree polygonal based edge-based smoothed finite element method for quasi-incompressible hyperelastic solids

This paper discusses an adaptive framework based on the edge-based strain smoothing approach with polygonal meshes for largely deformable quasi-incompressible hyperelasticity. The proposed approach employs the quadtree decomposition for spatial discretization and the strain smoothing technique to compute the bilinear/linear form. The local refinement is based on the stress distribution and the element that has hanging nodes due to adaptive local refinement are treated as polygonal element within the strain smoothing framework. The accuracy and the robustness of the proposed framework are numerically studied with a few examples. When compared to uniform refinement, it is seen that the proposed framework yields comparable results with fewer degrees of freedom.

A Dynamic Smoothing Technique for a Class of Nonsmooth Optimization Problems on Manifolds

A Dynamic Smoothing Technique for a Class of Nonsmooth Optimization Problems on Manifolds

Exploring Seismic Hazard in the Groningen Gas Field Using Adaptive Kernel Smoothing

The discovery of gas in Groningen in 1959 has been a massive boon to the Dutch economy. From the 1990s onwards, though, gas production has led to induced seismicity. In this paper, we carry out a comprehensive exploratory analysis of the spatio-temporal earthquake catalogue. We develop a non-parametric adaptive kernel smoothing technique to estimate the spatio-temporal hazard map and to interpolate monthly well-based gas production statistics. Second- and higher-order inhomogeneous summary statistics are used to show that the state-of-the-art rate-and-state models for the prediction of seismic hazard fail to capture inter-event interaction in the earthquake catalogue. Based on these findings, we suggest a modified rate-and-state model that also takes into account changes in gas production volumes and uncertainty in the pore pressure field.

A centroid-enriched strain-smoothed radial point interpolation method for nearly incompressible elastoplastic problems in solid mechanics

While edge-based (2D) and face-based (3D) smoothed radial point interpolation methods (ES-RPIM and FS-RPIM) have demonstrated excellent performance in solid mechanics, the volumetric locking problem limits their applications in nearly incompressible problems. This paper develops a centroid-enriched edge-based/face-based smoothed RPIM (CE-ES-RPIM and CE-FS-RPIM) to address the volumetric locking problem in nearly incompressible problems. The proposed method comprises the following elements: (1) a centroid-enriched scheme is creatively used and implemented into the traditional ES-RPIM and FS-RPIM framework, in which the centroids of triangular mesh are added as spatial discretization nodes; (2) the strain-smoothing technique is performed on triangle (2D) or tetrahedron (3D) mesh to maintain the linear exactness of the radial point interpolation method; and (3) the combination of the centroid-enriched scheme and strain smoothing technique makes the ratio of the number of displacement equations to the number of incompressible constraints reach the optimal value of 2, effectively eliminating volumetric locking. The accuracy, effectiveness and efficiency of the proposed method are validated on several 2D and 3D nearly incompressible benchmark examples. All results demonstrate that the proposed method can provide accurate numerical results under an incompressible limitation and provide better performance than conventional quadratic element methods.

Diffuse interface-lattice Boltzmann modeling for heat and mass transfer with Neumann boundary condition in complex and evolving geometries

In this research, a diffuse interface-lattice Boltzmann method is developed to model heat and mass transfer problems with Neumann boundary condition in complex and evolving geometries. In this model, the physical domain with curved and moving boundaries is extended to a larger and regular domain based on the concept of fictitious domain. The Neumann boundary condition is incorporated into the jump conditions of field variable at the inner interface. Then a unified convection-diffusion equation of field variable with two singular source terms is built. Using the smoothing technique, a diffuse interface equation is obtained. Moreover, an energy method is presented to derive the diffuse interface equation in a special case. We utilized a multiple-relaxation-time lattice Boltzmann scheme to numerically solve the diffuse interface equation. A simple example with analytical solutions validates the numerical accuracy of the present model. Finally, several thermal convection and mass transport problems are also simulated. The numerical results denotes the validity of the proposed diffuse interface model.

The edge-based smoothed FEM with [formula omitted]-Bathe implicit temporal discretization scheme for the analyses of underwater wave propagation problems

The underwater acoustic wave propagation problem is an important issue in ocean engineering field. The classical finite element approach with edge-based smoothed gradient smoothing technique is incorporated into the novel ρ∞-Bathe implicit direct time integration scheme in this paper for solving underwater transient acoustic wave propagation problems. This edge-based smoothed FEM (ES-FEM) and ρ∞-Bathe method are employed to perform the space and time domain discretization, respectively. Due to the appropriate softening effects from the ES-FEM, the space discretization errors can be markedly reduced. Meanwhile, the possible numerical error related to the temporal discretization also can be significantly suppressed by the novel ρ∞-Bathe method which possesses the controllable numerical damping effects. Through detailed analysis of the total dispersion error, we find that the numerical results from the proposed scheme can converge monotonically to the exact solutions when employing the decreasing temporal discretization intervals and the possible numerical anisotropy effects are also effectively suppressed. These excellent numerical performance clearly distinguish the present method from the conventional approaches and make it to be particularly suitable for complicated acoustic wave propagation analysis. Finally, various typical numerical experiments are performed to illustrate the capacities of the proposed scheme in tackling underwater transient acoustic wave propagation problems.

A Novel Adaptive Power Smoothing Approach for PV Power Plant with Hybrid Energy Storage System

Clouds passing over solar photovoltaic (PV) power system causes power fluctuations, which contributes to power quality issues. Power fluctuations are usually compensated by an energy storage system (ESS) integrated with a filtering or smoothing controller. However, there is a great burden to tune and determine the filtering time constant (TF) that leads to better alleviation of PV power fluctuations because the irradiance variability varies from day to day. Lack of cloud information leads to incorrect selection of TF values which leads to poor smoothing performance when setting low values on cloudy days, or to unnecessary ESS operation when setting high values on clear days. This paper introduces a novel power smoothing framework to avoid the arbitrary selection of TF values and reduce the stress on ESS based on predictive and adaptive smoothing mechanisms. The proposed controller contains two layers, the first layer predicts the next day's irradiance profile and uses a clear sky model to identify the cloud class. Next, the controller sets the initial values of the filter time constants for cloudy, moderate, and mild days, and disables smoothing on clear and overcast days. The second layer adaptively adjusts the initial filter time constant based on the current power ramp rate and shares power for a hybrid energy storage system (HESS). The performance of the proposed controller is tested against the forecasting errors and compared to fixed time constant-based smoothing techniques and ramp rate control in a range of scenarios, including smoothing, ramp rate reduction, and battery degradation assessment.

A dynamic large-deformation particle finite element method for geotechnical applications based on Abaqus

In this paper, the application of Abaqus-based particle finite element method (PFEM) is extended from static to dynamic large deformation. The PFEM is based on periodic mesh regeneration with Delaunay triangulation to avoid mesh distortion. Additional mesh smoothing and boundary node smoothing techniques are incorporated to improve the mesh quality and solution accuracy. The field variables are mapped from the old to the new mesh using the closest point projection method to minimize the mapping error. The procedures of the proposed Abaqus-based dynamic PFEM (Abaqus-DPFEM) analysis and its implementation in Abaqus are detailed. The accuracy and robustness of the proposed approach are examined via four illustrative numerical examples. The numerical results show a satisfactory agreement with published results and further confirm the applicability of the Abaqus-DPFEM to solving dynamic large-deformation problems in geotechnical engineering.

On interquantile smoothness of censored quantile regression with induced smoothing.

Quantile regression has emerged as a useful and effective tool in modeling survival data, especially for cases where noises demonstrate heterogeneity. Despite recent advancements, non-smooth components involved in censored quantile regression estimators may often yield numerically unstable results, which, in turn, lead to potentially self-contradicting conclusions. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest via the induced smoothing technique to circumvent the difficulty. Our proposed estimator can be shown to be asymptotically equivalent to its original unsmoothed version, whose consistency and asymptotic normality can be readily established. Extensions to handle functional covariate data and recurrent event data are also discussed. To alleviate the heavy computational burden of bootstrap-based variance estimation, we also propose an efficient resampling procedure that reduces the computational time considerably. Our numerical studies demonstrate that our proposed estimator provides substantially smoother model parameter estimates across different quantile levels and can achieve better statistical efficiency compared to a plain estimator under various finite-sample settings. The proposed method is also illustrated via four survival datasets, including the HMO (health maintenance organizations) HIV (human immunodeficiency virus) data, the primary biliary cirrhosis (PBC) data, and soforth.

Implied volatility smoothing at COVID-19 times

This work aims at studying the impact of the SARS-CoV-2 pandemic on the global financial markets. In particular, such impact is analysed through the changes of the shape of the implied volatility smile of the options written on several equity indexes and on several stocks. The implied volatility function is estimated using the market-based information of liquid options and applying a semi-parametric smoothing technique that exploits a kernel function and no-arbitrage conditions. Such approach is applied to an extensive set of data to study the evolution of the implied volatility functions through the months of the pandemic. We show, in several cases, a sudden and massive change in the shape of the implied volatility functions.