Smoothing Approach Research Articles

PERBANDINGAN METODE MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN EXPONENTIAL SMOOTHING WITH TREND DALAM PERAMALAN KEBUTUHAN MATERIAL CV KARYA TEKNIK MANDIRI

Using historical data, forecasting is a technique for estimating future values. The CV Karya Teknik Mandiri was the site of this study. Buildings in industries, civil workers, backfilling, bridges, cementing/irrigation, mechanical & electrical workers, plumbing, electrical installations, ducting/ac, and other services are all provided by CV Karya Teknik Mandiri. Material buildup occurs often at CV Karya Teknik Mandiri. Material buildup at CV Karya Teknik Mandiri leads to losses, including unnecessary storage space usage and a higher risk of material deterioration, which lowers the caliber of produced items. This study examines the comparison of moving average methods, exponential smoothing, and exponential smoothing with trend in forecasting material needs, particularly cement at CV Karya Teknik Mandiri, as the company must make precise projections for material needs in the coming months. The Exponential Smoothing approach with α = 0.5 has a lower error rate than other approaches, according to data gathering and processing. The Exponential Smoothing technique with α = 0.5 gets a rating of 19.646 (Bias). MAPE 52.132%, MAD 312.391, MSE 179758.9, and standard error 434.715. The chosen approach is Exponential Smoothing α = 0.5, it may be concluded. The quantity of Material Demand (Cement) that CV Karya Teknik Mandiri is expected to have in July 2021 is 865 / (sack / 50 kg), according to the study of the forecast.

Decentralized Learning of Quantile Regression: a Smoothing Approach

Distributed estimation has attracted a significant amount of attention recently due to its advantages in computational efficiency and data privacy preservation. In this article, we focus on quantile regression over a decentralized network. Without a coordinating central node, a decentralized network improves system stability and increases efficiency by communicating with fewer nodes per round. However, existing related works on decentralized quantile regression have slow (sub-linear) convergence speed. We propose a novel method for decentralized quantile regression which is built upon the smoothed quantile loss. However, we argue that the smoothed loss proposed in the existing literature using a single smoothing bandwidth parameter fails to achieve fast convergence and statistical efficiency simultaneously in the decentralized setting. We propose a novel quadratic approximation of the quantile loss using a big bandwidth for the Hessian and a small bandwidth for the gradient. Our method enjoys a linear convergence rate and has optimal statistical efficiency. Numerical experiments and real data analysis are conducted to demonstrate the effectiveness of our method.

Assessing Statistical Methods for Generating Forecasts for COVID-19

The COVID-19 pandemic, a persistent global health emergency that has affected almost all facets of daily life, was initially discovered in Wuhan, China, in December 2019. Since that time, the virus has rapidly spread over the globe, causing serious social and economic upheavals necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile real-world data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing and filtering strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. This study also discovered that the ARIMA model, when applied directly to the original data without using any smoothing or filtering approaches, failed to forecast adequately, thereby demonstrating the insufficiency of the ARIMA model on its own to provide credible forecasts when given a volatile set of data.

Multi-level attention graph feature fusion smooth prognostics approach for aircraft engines remaining useful life prediction

In the development of a prognostic and health management system, it is a challenge to mine the degradation characteristics, estimate the RUL reliably and smoothly, and explain the RUL prediction results from the multi-sensor condition monitoring data collected under the non-fixed-length equipment operation cycle and multi-operating conditions. In this paper, a Multi-level Attention Graph feature Fusion Smooth Prognostics Approach (MAGF-SPA) is proposed to achieve accurate, smooth, and interpretable RUL prediction results. In this method, firstly, multivariate time series (MTS) are transformed into graph signals from a purely data-driven perspective. Secondly, the four-level feature extraction strategy, which includes node-level, subgraph-level, cyclical-level, and temporal-level feature extraction, is designed to extract the inherent hidden degradation characteristics of MTS. Finally, a smoothing trick proposed in this paper can optimise the RUL prediction value corresponding to the degradation characteristics in real time. Experimental results show that the proposed method achieves state-of-the-art on the N-CMAPSS dataset.

Simulating 2D Fluid Motion with the Smooth Particle Hydrodynamic Approach

Abstract Virtual two-dimensional (2D) fluid simulation is useful for directly simulating fluids in various situations, including geological simulations for landslides and fluid simulations for teaching. This research aims to simulate the behaviour of three different types of fluids (water, coconut oil, and glycerine) in a 2D container and analyse how these three types of fluids behave under various conditions, including interactions with boundaries. The research used Python programming to simulate fluids and the Wondershare Filmora X application to combine images. The Smooth Particle Hydrodynamics method simulated fluid in a 2D container with 10,000 particles by deriving the force density field directly from the Navier-Stokes equations. The Navier-Stokes equations were utilized to find the acceleration and velocity of fluid particles by considering external forces, internal forces, and gravity through this method. Acceleration and velocity were validated due to wall collisions, collisions with boundaries, and collisions between particles, which caused changes in particle position and velocity. During visualization, the fluid velocity decreased over time due to attenuation caused by interactions between neighbour particles, particles with boundaries, and particles with walls. From the simulation, it was observed that the fluid flowed from a higher place to a lower place, with fluid particles taking the shape of the container and the surface of the fluid forming waves. On the other hand, simulations with boundaries indicated that smaller gap sizes and higher viscosities led to increased difficulty in fluid penetration into the gap within the container.

Does the material recycling rate matter in the effect of the generated waste on environmental pollution? Panel smooth transition regression approach

This study examined the effect of material recycling on the relationship between the waste amount and environmental pollution in EU-15 countries for the 1995-2019 period through panel smooth regression analysis by using the material recycling rate as the threshold variable. Based on the analysis results, the material recycling rate threshold level was estimated as 11.79. In these countries, if the material recycling rate is below the threshold level, the rise in the waste amount will increase environmental pollution. If the material recycling rate is above the threshold value, the rise in the waste amount will still increase environmental pollution, but the pollution increase rate will decrease. With the increase in the waste amount in the long term, environmental pollution can only be reduced by raising the material recycling rate. For the reduction of environmental pollution, which is one of the most prioritized issues in Europe in recent years, policy makers should take measures to increase the material recycling rate by taking the results of this study into consideration and pay attention to the implementation of these measures.

The “effect modifier” of US interest rate in the economic policy uncertainties and economic conditions of fifty (50) US states: A semi-parametric smooth varying-coefficient approach

In this study, we investigate the relationship between economic policy uncertainty [EPU] and the economic conditions of the 50 US states, as well as the role of interest rates. We use a semi-parametric smooth varying coefficient model (SVCM) to examine how interest rate affects the nexus of EPU-economic conditions. Our findings suggest a negative relationship between EPU and economic conditions and that when the interest rate is around 3 %, the negative impact of EPU on economic conditions decreases in more than 60 % of US states. Furthermore, we find that the rate of change in the interest rate between 2 % and 3 % helps mitigate the negative effects of EPU and improves economic conditions in several states. Our results remain consistent across different interest rate periods, regardless of whether the uncertainty is of internal or external origin.

Adaptive Gaussian Markov random fields for child mortality estimation.

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985 to 2019, a time period which includes the Rwandan civil war and genocide.

A smooth maximum regularization approach for robust topology optimization in the ground structure setting

A smooth maximum regularization approach for robust topology optimization in the ground structure setting

Digital Image Noise Reduction Based on Proposed Smoothing and Sharpening Filters

In order to reduce noise in digital photographs, this paper provides a thorough analysis of sophisticated sharpness enhancement and image smoothing approaches. The research compares and contrasts different approaches, including well-known filters like Gaussian filter, Mean filter, Weighted Averaging filter, Laplacian filters, and Unsharp mask, with suggested strategies aimed at achieving optimal efficiency. The performance of these techniques is evaluated in several circumstances with speckle noise and Gaussian noise corrupted images. The results show how well the suggested strategy performs in terms of reducing noise, preserving image features, and improving overall image quality. The comparative advantage of the suggested technique over conventional filters is highlighted by comparative analysis. The study's conclusion offers information on the possible uses and future developments of these methods in practical image processing situations.

The association between triglyceride -glucose index and stress urinary incontinence in adult American women: A population-based study.

This investigation seeks to determine the triglyceride glucose (TyG) index's link to stress urinary incontinence (SUI) in American females of adult age. The investigation relied on data acquired via the National Health and Nutrition Examination Survey (NHANES) conducted over the period from 2011 to 2018. The independent relationship between TyG index and SUI was tested using multivariate logistic regression analysis. We applied a smooth curve fitting approach to analyze the interrelation of them. In addition, subgroup analysis was conducted and interaction experiments were conducted. Among 4459 female participants aged 20 and above, TyG index and SUI demonstrated a favorable correlation. Model 3 indicated that with every single-unit rise in the TyG index, the incidence of SUI increases by 18% [1.18 (1.01, 1.38)]. In contrast to individuals in the lowest tertile, subjects within the highest tertile of the TyG index exhibited a 68% increase in SUI incidence [1.68 (95% CI: 1.26, 2.23), 0.0004]. By using smooth curve fitting, a nonlinear positive evidence of an interconnection of the TyG index to SUI was identified. Women exhibiting increased TyG index levels are at a heightened risk of SUI. TyG index displays a stronger correlation than that observed with BMI. According to our findings, the TyG index is viewed as a potential tool for identifying SUI in women, and monitoring the value of TyG index may be helpful for predicting the occurrence of SUI.

K-Nearest Neighbors Estimator for Functional Asymmetry Shortfall Regression

This paper deals with the problem of financial risk management using a new expected shortfall regression. The latter is based on the expectile model for financial risk-threshold. Unlike the VaR model, the expectile threshold is constructed by an asymmetric least square loss function. We construct an estimator of this new model using the k-nearest neighbors (kNN) smoothing approach. The mathematical properties of the constructed estimator are stated through the establishment of the pointwise complete convergence. Additionally, we prove that the constructed estimator is uniformly consistent over the nearest neighbors (UCNN). Such asymptotic results constitute a good mathematical support of the proposed financial risk process. Thus, we examine the easy implantation of this process through an artificial and real data. Our empirical analysis confirms the superiority of the kNN-approach over the kernel method as well as the superiority of the expectile over the quantile in financial risk analysis.

Peramalan Jumlah Kebutuhan Packaging Papper Photocopy (PPC) PT. Y Menggunakan Metode Moving Avarage dan Exponential Smoothing

In the era of globalization, industry and trade in Indonesia face fierce competition. Technological advances require quality human resources from formal and non-formal educational institutions with supporting facilities. This research focuses on solving safety stock problems using the Exponential Smoothing and Moving Average methods. Due to the current changing market demand, the results of data processing carried out using the Quality Management (QM) program for Windows show that the forecasting method with the Moving Average approach has the highest Mean Absolute Percentage Error (MAPE) value of 16.03%, while the Exponential Smoothing method has a MAPE value of 13.7%. So with this, the lowest MAPE value is in the forecasting method with the Exponential Smoothing approach of 13.7%. Keywords: Demand Forecasting, Exponential Smoothing Method, Moving Average Method, MAPE, Safety Stock

The impact of diffusion tensor imaging tractography settings on muscle fascicle architecture and diffusion parameter estimates: Tract length, completion, and curvature are most sensitive to tractography settings.

Diffusion-tensor (DT)-MRI tractography provides information about properties relevant to muscle health and function, including estimates of architectural properties such as fascicle length, pennation angle, and curvature and diffusion properties such as mean diffusivity (MD) and fractional anisotropy (FA). Tractography settings, including integration algorithms, thresholds for early tract termination, and tract smoothing approaches, impact the accuracy of the muscle property estimates. However, muscle DT-MRI tractography is performed using a variety of these settings, complicating comparisons between different studies. The effects of different tractography settings on muscle architecture estimates have not been fully explored, and optimized settings for muscle tractography have not yet been determined. We examined the influence of integration algorithm and termination check settings combined with a range of step sizes, termination criteria, and smoothing polynomial orders on tract characteristics, completion/reason for termination, and goodness of fit between fiber tracts and smoothing polynomials using 3-T DT-MR images of the lower leg muscles of seven healthy adults. We found that tract length and completion were highly sensitive to strict FA and intersegment angle thresholds (25%-69% reduction in complete fiber tracts from lowest to highest minimum FA threshold and 11%-36% reduction from highest to lowest intersegment angle threshold). Higher order polynomials (third and fourth order vs. second order) better fit the muscle fiber trajectories, but curvature estimates were highly sensitive to smoothing polynomial order (3.9-6.6 m-1 increase for second- vs. fourth-order fitting polynomials). Step size impacted curvature estimates, albeit to a lesser degree. Integration algorithm had little impact, and mean pennation angle, and tract-based FA and MD, were relatively insensitive to all parameters. The results demonstrate which muscle diffusion measures and architectural estimates are most sensitive to varying tractography settings and support the need for consistent reporting of tractography details to aid interpretation and comparison of results between studies.

The path-engulfment method for topology optimization of structures

To address the challenge of establishing and solving mathematical models for engineering structural optimization, a new topological optimization method that integrates load-transfer path theory with the engulfment algorithm is presented in this paper. The presented method applies the load-transfer path theory to identify the main load-bearing areas of the structure and utilizes the principle of concentrating more materials in relatively high-stress regions and fewer materials in relatively low-stress regions. An engulfment algorithm is introduced to optimize the material distribution. A comparative analysis between the presented and variable-density methods revealed that the path-engulfment method enhances the structural stiffness and strength while reducing its mass, confirming its precision and efficacy in structural optimization. The path-engulfment method was implemented on a truck crane frame, resulting in an optimized structure with increased stiffness and strength and reduced mass compared to the original design. Furthermore, this method eliminates the need for establishing and solving complex mathematical models while addressing issues related to checkerboards and gray-scale elements. A smooth boundary approach was introduced by leveraging the engulfment algorithm, enabling the direct application of the optimized structure for manufacturing purposes, particularly in engineering applications.

A variable-order fractional memristor neural network: Secure image encryption and synchronization via a smooth and robust control approach

In this research, we introduce and investigate a variable-order fractional memristor neural network, focusing on its engineering applications in synchronization and image encryption. This study stands out as a pioneering effort in proposing such an architecture for image encryption purposes. Distinct from conventional fractional-order systems, our model incorporates a time-varying fractional derivative, leading to more complex behaviors. Through numerical simulations, we vividly demonstrate the chaotic dynamics of the system. Our results further reveal the system's outstanding performance in image encryption applications. To augment the system's efficiency, we introduce a robust control strategy that guarantees smooth stabilization and synchronization of the variable-order fractional system. Considering the unique variable-order fractional nature of the system, we provide theoretical validations and empirical evidence supporting its stability and convergence properties. Additionally, we present synchronization outcomes between pairs of such neural networks employing our robust control approach. Our numerical analyses firmly substantiate the superiority of our control strategy, particularly highlighting its precision, robustness, and ability to maintain chattering-free performance under external disturbances.

The recital assessment of image fusion based on multi-resolution transforms using smoothness approach proach

Combining the information from the registered source images is the process involved in the image-fusion. In this present manuscript, two fusion rules are explored. The first one is based on the weightage-based rule. The second one is the Smoothness and weightage-based algorithm. Smoothness is used to reduce the noise from the source images. These two methods are independent of the selection of the transform. In this work, Discrete Wavelet Transform is considered to perform the experimentation. The recital comparison was made between multiresolution transforms using maximum selection method, weightage method, and smoothness along with weightage. The source images are generally multi-focused, satellite, panchromatic, and clinical medical images. The experimental results show that more smoothened (in addition with weightage) images (including edges and curves) provide high visual information. The advantage of this approach is proven using the performance metrics such as PSNR, NCC, MI, ESOP, and FSIM. The blocking artifacts are reduced by decomposing the transforms and the high frequency noise in the source image is smoothened by proposed approaches.

Enhancing five-axis CNC toolpath smoothing: Overlap elimination with asymmetrical B-splines

In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints.

A fast and smooth one-electron approach for investigating charge transfer states and D1-D0 crossings for systems with odd numbers of electrons.

We propose an efficient version of ensemble Hartree-Fock/density functional theory to calculate a set of two charge-transfer states for systems with odd-numbers of electrons. The approach does require definitions of donor/acceptor fragments; however, the approach is not very sensitive to such definitions-even in the limit of very strong electronic coupling. The key ansatz is that, by mandating that the vector space spanned by the active orbitals projects equally onto the donor and acceptor fragments, such a constraint eliminates all intra-molecular local excitations and makes it far easier to generate potential energy surfaces that are smooth over a wide region of configuration space. The method is fast, working with only two electron configurations, and should be useful for abinitio non-adiabatic dynamics in the near future.

An Adaptive Smooth Event-Triggered Approach to Recursive Estimation for Nonlinear Time-Varying Systems With Uncertain Communication Topologies

An Adaptive Smooth Event-Triggered Approach to Recursive Estimation for Nonlinear Time-Varying Systems With Uncertain Communication Topologies