Smoothing Method Research Articles

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

CONSTRUCTION OF INDONESIA MORBIDITY TABLE FOR GENITOURINARY SYSTEM-RELATED DISEASES TO FACILITATE INDONESIA’S ECONOMIC EXPANSION

The disease of the genitourinary system is one of the most common diseases in Indonesia. The disease shows some risk, especially in health and economic aspect. To overcome the risk posed by disease of the genitourinary system, a morbidity table can be used to reduce the number of people infected by the disease, and in this research, morbidity table based on sample data of BPJS Kesehatan from 2015 to 2016 will be constructed. Data processing starts with obtaining the empirical probability value of the disease of the genitourinary system. Then, the result obtained was interpolated in numerous preferences of methods. After the best interpolation model was known, the extrapolation process was run at an age range from age 80 to age 85 using Whittaker-Henderson smoothing method. The age range chosen is due to the fact that for ages over 80 years, the number of exposures is minimal, which can affect the number of claims in that age group. Extrapolation is extended up to the age of 85 years because the Morbidity Table for Critical Illness sets the age of 85 years as the upper limit for extrapolation. The model derived from interpolating age groups with a knot shows the highest R-square value, making it the most optimal model. It shows that Indonesians' probability to contract the disease is increasing significantly from about age 25 until around age 65, and the probability slowly declines after age 65. This result can be used as a reference by the Government of Indonesia to produce regulations leading to health protection for all citizens of Indonesia, especially for those who are classified as the labor force. Health protection provided by the government should improve welfare in Indonesian society. Moreover, the regulation should protect the productivity of the manpower and raise Indonesia’s Gross Domestic Product (GDP). BPJS Kesehatan can also use this predicted morbidity table to determine the right contribution fee. Hence, the contribution can be beneficial to pay any expense of the patient who contracted the disease, but on the other hand, not sending BPJS Kesehatan into bankruptcy. All of abovementioned efforts have one bold intention—to support Indonesia’s economic expansion as Indonesia aims to reach Golden Indonesia Vision by 2045.

A 3-mV Precision Dual-Mode-Controlled Fast Charge Balancing for Implantable Biphasic Neural Stimulators.

This paper presents a novel charge balancing (CB) with a current-control (CC) mode and a voltage-control (VC) mode for implantable biphasic stimulators, which can achieve one-step accurate anodic pulse generating. Compared with the conventional short-pulse-injection-based CB, the proposed method could reduce the balancing time and avoid inducing undesired artifact. The CC operation compensates the majority stimulation charge at high speed, while the VC operation guarantees a high CB precision. In order to eliminate the oscillation during the mode transition, a smooth CC-VC transition method is adopted. In addition, a digital auxiliary monitoring loop is introduced against the variations of the tissue-electrode interface impedance during the stimulation process to meet long-term CB requirement. The proposed stimulator has been fabricated in a 0.18 μm BCD process with 10 V voltage compliance, and the measured CB precision is less than 3 mV. The functionalities of the proposed CB have been verified successfully through in vitro experiments.

Enhancing Morphological Measurements of the Cosmic Web with Delaunay Tessellation Field Estimation

The density fields constructed by traditional mass assignment methods are susceptible to irritating discreteness, which hinders morphological measurements of cosmic large-scale structure (LSS) through Minkowski functionals (MFs). To alleviate this issue, fixed-kernel smoothing methods are commonly used in the literature, at the expense of losing substantial structural information. In this work, we propose to measure MFs with the Delaunay tessellation field estimation (DTFE) technique, with the goal of maximizing the extraction of morphological information from sparse tracers. We perform our analyses starting from matter fields and progressively extending to halo fields. At the matter-field level, we elucidate how discreteness affects morphological measurements of LSS. Then, by comparing with the traditional Gaussian smoothing scheme, we preliminarily showcase the advantages of DTFE for enhancing measurements of MFs from sparse tracers. At the halo-field level, we first numerically investigate various systematic effects on MFs of DTFE fields, which are induced by finite voxel sizes, halo number densities, halo weightings, and redshift space distortions (RSDs), respectively. Then, we explore the statistical power of MFs measured with DTFE for extracting the cosmological information encoded in RSDs. We find that MFs measured with DTFE exhibit improvements by ∼2 orders of magnitude in discriminative power for RSD effects and by a factor of ∼3–5 in constraining power on the structure growth rate over the MFs measured with Gaussian smoothing. These findings demonstrate the remarkable enhancements in statistical power of MFs achieved by DTFE, showing enormous application potentials for our method in extracting various key cosmological information from galaxy surveys.

Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.

Sparse reconstruction methods for wideband source localization in spherical harmonics domain

Wideband acoustic source localization is a challenging task especially in the presence of noise. Generally wideband source localization is done by averaging the Direction of Arrival (DOA) estimates obtained over multiple frequencies or narrow subbands by the method of frequency smoothing. Localization of multiple wideband sources which are correlated is even more challenging. In this work acoustic source localization under these challenging conditions is addressed. A sparse reconstruction framework is developed for wideband source localization in the Spherical Harmonics (SH) domain. The proposed framework jointly computes both the azimuth and elevation. In contrast to earlier methods of source localization in the SH domain, this work utilizes the expression for the SH coefficients of the amplitude density of the plane wave, to develop the sparse reconstruction framework. An expression for Cramér Rao Lower Bound (CRLB) on the DOA using this framework is also derived for the wideband sources. This CRLB is shown to easily generalize for narrowband sources as well. The performance of the proposed method is compared with MUltiple Signal Classification in SH domain (MUSIC-SH), Steered Response Power in SH domain (SRP-SH), MUSIC with Direct Path Dominance test in SH domain (MUSIC-DPD-SH) and Pressure based Compressive Sensing in SH domain (PCS-SH). The performance is evaluated for correlated narrowband and wideband sources through simulations, conducting experiments in an anechoic chamber and under reverberant conditions. Using the proposed framework, it is shown that correlated narrowband and wideband sources can be resolved reasonably well when compared to conventional methods of acoustic source localization in SH domain.

Application of Surface-Based Smoothing Methods for Topology Optimization Results

Topology optimization (TO) is becoming an essential part of designing new or modifying current parts to improve their properties. A typical problem of TO in structural applications is minimizing compliance with volume (mass) constraints. TO is often complemented by additive manufacturing which allows the printing of complex geometries. Resulting geometries are dependent on the used method, most of the time it is the finite element method. The exported geometry from the optimization procedure, however, tends to have a lot of sharp edges, which causes problems with stress distribution, aesthetics and manufacturing (even if it is additive manufactured). The aim of this paper is to review, apply smoothing techniques on the surface of bodies, Laplacian and Taubin smoothing, and compare the results of smoothing. To fulfill this aim, we developed a smoothing procedure and evaluated the criteria of smoothed-out polymer geometries.

Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.

Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods

Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods

A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.

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

Comparison Of Exponesial Smoothing With Linear Regression Predicting Amount Of Goods Sales

A trading business is a business that operates in the sales sector with the aim of obtaining maximum profits through sales activities. To be able to sell efficiently, a prediction system is needed, so that there is no excess or shortage of inventory and the sales process can run smoothly. Human limitations in solving prediction problems without using tools that apply prediction methods are one of the obstacles in finding the right prediction value. Therefore, we need a prediction system that can help find accurate and fast values. So the problem formulation is how to design and build a sales prediction system using exponential smoothing and linear regression methods, then compare the two and find out which method is the best, both of which use periodic data prediction models. The data collection method used is secondary data from previous research and journals, as well as combining library study methods, namely information obtained from books, references and scientific works related to predictions. The tool used to build applications is MS-Visual Studio 2010 and WEB based system

Spatiotemporal smoothing of water quality in a complex riverine system with physical barriers

Freshwater ecosystems offer a variety of ecosystem services, and water quality is essential information for understanding their environment, biodiversity, and functioning. Interpolation by smoothing methods is a widely used approach to obtain temporal and/or spatial patterns of water quality from sampled data. However, when these methods are applied to freshwater systems, ignoring terrestrial areas that act as physical barriers may affect the structure of spatial autocorrelation and introduce bias into the estimates. In this study, we applied stochastic partial differential equation (SPDE) smoothing methods with barriers to spatial interpolation and spatiotemporal interpolation on water quality indices (chemical oxygen demand, phosphate phosphorus, and nitrite nitrogen) in a freshwater system in Japan. Then, we compared the estimation bias and accuracy with those of conventional non-barrier models. The results showed that the estimation bias of spatial interpolations of snapshot data was improved by considering physical barriers (5.8 % for (chemical oxygen demand, 22.5 % for phosphate phosphorus, and 21.6 % for nitrite nitrogen). The prediction accuracy was comparable to that of the non-barrier model. These were consistent with the expectation that accounting for physical barriers would capture realistic spatial correlations and reduce estimation bias, but would increase the variance of the estimates due to the limited information that can be gained from the neighbourhood. On the other hand, for spatiotemporal smoothing, the barrier model was comparable to the non-barrier model in terms of both estimation bias and prediction accuracy. This may be due to the availability of information in the time direction for interpolation. These results demonstrate the advantage of considering barriers when the available data are limited, such as snapshot data. SPDE smoothing methods can be widely applied to interpolation of various environmental and biological indices in river systems and are expected to be powerful tools for studying freshwater systems spatially and temporally.

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

Voltage stability assessment and contingency ranking in power systems based on modern stability assessment index

Electric loads steadily increase each year due to economic and population expansion, placing electric systems close to their stability limits. Also, the electrical network is a sophisticated system with various control, protection devices, and power electronics. Any malfunction in these components can disrupt system reliability. Therefore, ensuring safe and dependable power system operation requires continuous and precise voltage stability monitoring to prevent instability and cascading outages. Despite the existence of numerous stability indexes, their limitations often make them ineffective under varying operating conditions. In response to these challenges, this article proposes a modern stability assessment index (MSAI) designed specifically to assess voltage stability in different operating scenarios. The MSAI aims to accurately assess voltage stability and stress levels in power lines, enabling operators to proactively manage system reliability. One key advantage of the MSAI is its ability to determine the maximum allowable load and loading margin, which is crucial for maintaining stable operation under changing load conditions. In order to validate the effectiveness of the proposed index, extensive evaluations are conducted using IEEE 30, 57, and 118 bus test systems. The study meticulously compares the performance of the MSAI with other prominent stability indicators, highlighting its superiority in providing predictive insights into areas prone to stress and potential contingencies. By empowering operators with preemptive correction capabilities, the MSAI reduces the risk of successive outages and enhances overall system flexibility. The voltage stability assessment and contingency ranking were evaluated using the proposed MSAI via a smooth method in the MATLAB environment.

A correction algorithm of geometric unsharpness for neutron radiographs via adaptive fusion and total variation

The mobility of accelerator-based neutron sources has laid the foundation for the miniaturization of neutron radiography facilities in response to the increasing industrial requirements. Compared to traditional reactors and spallation neutron sources, miniaturized neutron radiography facilities based on accelerator neutron sources face challenges in achieving a sufficiently high collimation ratio. It leads to a pronounced level of geometric unsharpness on neutron radiographs, which arises from a finite source size and beam divergence. The presence of blurriness in radiographs significantly impairs the ability to accurately visualize the internal structures of the test objects, thus hindering observation. An algorithm combining adaptive fusion with total variation was proposed to correct the geometric unsharpness for neutron radiographs in this paper. In contrast to most traditional restoration algorithms that relied on regularization terms or filters, our proposed method offered both initial restoration and further restoration. The initial restoration procedure involved the utilization of the Richardson-Lucy algorithm coupled with the l0 smoothing method. This adaptive fusion technique was employed to enhance the sharpness of edges and denoise the neutron radiographs. Finally, the result of the adaptive fusion was embedded into a deblurring model based on total variation to further preserve the edges and restore more details during further restoration. In the experiments, it was demonstrated that our correction algorithm effectively preserved the prominent edges and the details in both the simulated and real neutron radiographs of thin and thick objects, exhibiting fewer ringing effects than traditional algorithms. It is worth mentioning that our work is primarily applicable and significant to neutron generators, but not for reactors or spallation neutron sources that can deliver much better images.

A smooth method for primary frequency of wind turbine based on variable power point tracking and energy storage cooperative control

AbstractThe conventional deloading control has certain problems, such as low power generation efficiency and a small speed adjustment range. To improve the system's frequency quality and enhance the power grid's stability, this study comprehensively considers the effect of random source‐load power fluctuations on the system frequency. Moreover, this study proposes a smooth primary frequency control strategy for wind turbine based on the coordinated control of the variable power point tracking and supercapacitor energy storage. The impact of wind power fluctuations on the system frequency at different timescales for wind turbine is studied based on the historical data of wind power fluctuations in a strong wind meteorological cycle of a wind farm. The method determines the capacity of the energy storage device required for frequency smoothing at the optimal timescale. In combination with the required capacity of the wind turbine to participate in the system's primary frequency regulation, the supercapacitor energy storage device is optimally configured, and a set of supercapacitor energy storage device with the lowest cost under the highest charging/discharging efficiency is designed. Simulation and experimental results show that the primary frequency adjustment capability of the control strategy proposed in this study is significantly improved compared with the conventional primary frequency modulation control.

3D measurement of precise part in complex circumstance using line structured light and improved U-Net

Abstract Line structured light scanning is extensively utilized for the 3D measurement of precise metal parts, but the curvature surfaces and specific materials of these parts generate specular reflection, making it challenging to accurately extract the center of the laser stripe in complex circumstances. Therefore, the primary challenges are the noise separation in the laser stripe image and the extraction of the laser stripe center under complex circumstances. To solve the above problems, an improved U-Net semantic segmentation algorithm is proposed by adding an attention mechanism and modifying skip connections to the classical U-Net network structure for accurate laser stripe segmentation. Secondly, the dual smoothing method of mean smoothing and Savitzky–Golay smoothing is combined with the Hessian matrix algorithm to complete the subpixel extraction of the center point of the laser stripe. Finally, taking the blade and shaft part as the measurement objects, the experimental results demonstrate that the method can obtain more complete, smoother, and denser results than the traditional method under highly reflective surfaces, vital interference spots, and strong ambient light. The proposed method is used for the 3D measurement of the shaft parts, and the diameter measurement maximum error is 0.029 mm, verifying the feasibility of the proposed method.

Forecast of Corruption: From Ethical to Pragmatic Considerations

From an ethical standpoint, combating corruption is crucial for promoting justice and equality. The rule of law and ethical governance involves clear standards of behavior for public servants and mechanisms for ensuring these standards are upheld. Corruption undermines public trust in government and democratic institutions and exacerbates social inequality and injustice (disproportionately affects the poor and marginalized groups, denying them access to essential services and opportunities). The fight against corruption during a full-scale war has ethical and purely pragmatic implications for Ukraine, the data of which formed the basis of this study. The lack of tangible progress in the fight against corruption in general, the lack of transparency of many institutional mechanisms in public administration, the revealed corruption schemes in the distribution of international military and humanitarian aid, as well as in the field of public defence procurement, the lack of punishment for corrupt officials in the highest echelons of power threaten the loyalty of international donors and allies, reduce Ukraine’s authority in the international arena, slows down Ukraine’s movement towards the EU, and significantly affects the decisions of foreign partners. The article demonstrates the results of forecasting the future level of corruption in Ukraine (for 2024‒2027) based on the retrospective dynamics of the Corruption Perceptions Index by Transparency International for 1998‒2023. Two economic and mathematical models are used for forecasting: Autoregressive Integrated Moving Average (ARIMA), which better reflects long-term historical trends and fluctuations, and the exponential smoothing method, which is more sensitive to the latest values of the time series. The statistical analysis package STATISTICA was used for the calculations. The forecasting results are disappointing since both methods showed an expected decrease in the level of corruption in 2024-2027, but in critically low volumes: by 1 point according to the ARIMA model and by 3-4 points according to the exponential smoothing method. The results of this study can serve as a basis for public advocacy campaigns as an argument for the need to radically revise the existing format of anti-corruption policy in Ukraine given its European future.

Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods.