Value Of Function Research Articles

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

Analysis of communities and groups in social networks as a significant factor of influence on cryptocurrency rates

Currently, most existing cryptocurrency exchanges do not have tools in their arsenal that would allow them to verify and investigate information disseminated on social media regarding a particular cryptocurrency. This allows to conduct research with the subsequent development of an appropriate tool that, if used correctly, will provide users with recommendations on how to proceed with the cryptocurrency under investigation. Based on this advice, interested parties will be able to adjust their decisions regarding further financial steps. As part of this task, it is important to choose a social network that would best meet the requirements, as this is what determines the impact of celebrity publications on the formation of prices for a particular cryptocurrency at a certain point in time. The importance and existence of this influence has been previously proven by statistical methods. The purpose of the study is to identify and analyse the key aspects when choosing social networks for further monitoring of social groups in order to analyse the impact of posts on the course of the chosen cryptocurrency. The object of the study is social networks. A set of selection criteria, coefficients of their importance, and statistical data on the selected social networks were used as input data, on the basis of which the values of the alternatives (social networks) will be obtained. The objective of the study is to evaluate and rank social networks in order to choose the one that will best meet the specifics of analysing the impact of social media posts on the cryptocurrency rate. Research methods. The ranking of social networks was carried out by the value of the value function of alternatives, calculated using the linear convolution method. Results. As a result of the research, an algorithm has been developed that allows analysing the selected social networks for their compliance with the formulated criteria. The results of the experiment with the selected social networks were presented. As a result, a ranked list of them is obtained. Based on the results obtained, the authors will develop an information technology for determining the impact of posts of famous people in social networks on cryptocurrency rates.

Wind direction and speed calculation model with deep learning

Abstract In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

On maximal Roman domination in graphs: complexity and algorithms

For a simple undirected connected graph G = (V, E), a maximal Roman dominating function (MRDF) of G is a function f : V (G) → {0, 1, 2} with the following properties: (i) For every vertex v ∈ {v ∈ V|f(v) = 0}, there exists a vertex u ∈ N(v) such that f(u) = 2. (ii) The set {v ∈ V|f(v) = 0} is not a dominating set of G; In other words, there exists a vertex v ∈ {v ∈ V|f(v) ≠ 0} such that N(v) ∩ {u ∈ V|f(u) = 0} ∅. The weight of an MRDF of G is the sum of its function values over all vertices, denoted as f(G) = ∑v∈V (G) f(v), and the maximal Roman domination number of G, denoted by γmR(G), is the minimum weight of an MRDF of G. In this paper, we establish some bounds of the maximal Roman domination number of graphs. Additionally, we develop an integer linear programming formulation to compute the maximal Roman domination number of any graph. Furthermore, we prove that maximal Roman domination problem (MRD) is NP-complete even restricted to star convex bipartite graphs and chordal bipartite graphs. Lastly, we show the maximal Roman domination number of threshold graphs, trees, and block graphs can be computed in linear time.

AN METHOD FOR CREATING GEOINFORMATION 3D-MODEL OF AN AERODROME USING A DIGITAL TERRAIN MODEL

The creation of a three-dimensional geoinformation model of aerodrome is considered. An approach is proposed, consisting in supplementing a two-dimensional vector map with the values of the third coordinate, which are calculated using data from a digital terrain model. To implement this approach, the problem of interpolation of a function of two variables with a large number of nodal points is solved. The number of nodes where elevation values are set in a digital terrain model can be several thousand for each dimension. In this case, the use of global interpolation methods, for example, splines, in which function values at all nodal points are used to determine the values of a function at a given point falling into a grid cell, is computationally inefficient, since it requires a lot of machine time. It seems more promising to use a local approach to the interpolation problem. In this approach, the value of the function at an arbitrary point is determined by the values of the function at the nodal points of the grid closest to the specified point. Finite formulas are obtained that do not require solving equations, and allow interpolating functions for cases of linear and smooth approximation. An algorithm and a calculation program have been developed for a regular two-dimensional grid of nodes of a digital terrain model using the publicly available SRTM database. The results of numerical calculations of test cases with a comparison of linear and smooth approximations are presented. Using the proposed approach, a three-dimensional model of the Sochi aerodrome was created to simulate the movement of aircraft on its surface. The proposed approach and the interpolation method can be used to calculate the elevation of given points and to create three-dimensional cartographic models of the earth's surface.

Zeroth-order gradient tracking for decentralized learning with privacy guarantees

This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving optimization problems of decentralized systems, where the gradient information of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations.

Photodegradation, kinetics and non-linear error functions of methylene blue dye using SrZrO3 perovskite photocatalyst

The degradation of methylene blue dye-contaminated wastewater via photocatalysis is an efficient approach towards environmental remediation. The SrZrO3 perovskite photocatalyst was synthesized using the modified Pechini sol-gel method, and characterized using XRD, FESEM, FTIR, and UV–visible spectrophotometer. Crystallite size obtained by the Scherrer and Williamson-Hall methods were 45.56 and 44.50 nm, respectively. The sample exhibited an orthorhombic crystal structure. The optical bandgap was estimated to be 5.31 eV. Kinetic study for the degradation of methylene blue dye using SrZrO3 in the presence of H2O2 showed complete decontamination of the methylene blue dye in the time interval of 90–120 min under visible light irradiation. The degradation efficiency was above 80.1 % under illumination and less than 40.1 % in dark. Kinetic studies were performed by varying the dose of photocatalyst and initial concentration of methylene blue. It was observed that higher dose of the photocatalyst and lower concentration of the contaminant produced higher rate of degradation. The solution pH 3 and catalysts dosage of 200 mg yielded the highest rate of degradation. The experimental data fitted best into the Psuedosecond order kinetic model with the highest R2 value, indicating a strong linear relationship. The experimental data was subjected into nonlinear error functions, where the Psuedo-second order kinetic model demonstrated lower values of error functions. The results suggest that the prepared SrZrO3 photocatalyst coupled H2O2 is a promising photocatalyst for the decontamination of methylene blue dye under visible light irradiation.

Peak estimation of rational systems using convex optimization

This paper presents algorithms that upper-bound the peak value of a state function along trajectories of a continuous-time system with rational dynamics. The finite-dimensional but nonconvex peak estimation problem is cast as a convex infinite-dimensional linear program in occupation measures. This infinite-dimensional program is then truncated into finite-dimensions using the moment-Sum-of-Squares (SOS) hierarchy of semidefinite programs. Prior work on treating rational dynamics using the moment-SOS approach involves clearing dynamics to common denominators or adding lifting variables to handle reciprocal terms under new equality constraints. Our solution method uses a sum-of-rational method based on absolute continuity of measures. The Moment-SOS truncations of our program possess lower computational complexity and (empirically demonstrated) higher accuracy of upper bounds on example systems as compared to prior approaches.

An exploratory study of structural and microvascular changes in the skin following electrical shaving using optical coherence topography.

Consumer products such as electrical shavers exert a combination of dynamic loading in the form of pressure and shear on the skin. This mechanical stimulus can lead to discomfort and skin tissue responses characterised as "Skin Sensitivity". To minimise discomfort following shaving, there is a need to establish specific stimulus-response relationships using advanced tools such as optical coherence tomography (OCT). To explore the spatial and temporal changes in skin morphology and microvascular function following an electrical shaving stimulus. Ten healthy male volunteers were recruited. The study included a 60-s electrical shaving stimulus on the forearm, cheek and neck. Skin parameters were recorded at baseline, 20 min post stimulus and 24 h post stimulus. Structural and dynamic skin parameters were estimated using OCT, while transepidermal water loss (TEWL) was recorded to provide reference values for skin barrier function. At baseline, six of the eight parameters revealed statistically significant differences between the forearm and the facial sites, while only surface roughness (Rq) and reflectivity were statistically different (p<0.05) between the cheek and neck. At 20 min post shaving, there was a significant increase in the TEWL values accompanied by increased blood perfusion, with varying magnitude of change dependent on the anatomical site. Recovery characteristics were observed 24 h post stimulus with most parameters returning to basal values, highlighting the transient influence of the stimulus. OCT parameters revealed spatial and temporal differences in the skin tissue response to electrical shaving. This approach could inform shaver design and prevent skin sensitivity.

Robust Tensor Completion via Capped Frobenius Norm.

Tensor completion (TC) refers to restoring the missing entries in a given tensor by making use of the low-rank structure. Most existing algorithms have excellent performance in Gaussian noise or impulsive noise scenarios. Generally speaking, the Frobenius-norm-based methods achieve excellent performance in additive Gaussian noise, while their recovery severely degrades in impulsive noise. Although the algorithms using the lp -norm ( ) or its variants can attain high restoration accuracy in the presence of gross errors, they are inferior to the Frobenius-norm-based methods when the noise is Gaussian-distributed. Therefore, an approach that is able to perform well in both Gaussian noise and impulsive noise is desired. In this work, we use a capped Frobenius norm to restrain outliers, which corresponds to a form of the truncated least-squares loss function. The upper bound of our capped Frobenius norm is automatically updated using normalized median absolute deviation during iterations. Therefore, it achieves better performance than the lp -norm with outlier-contaminated observations and attains comparable accuracy to the Frobenius norm without tuning parameter in Gaussian noise. We then adopt the half-quadratic theory to convert the nonconvex problem into a tractable multivariable problem, that is, convex optimization with respect to (w.r.t.) each individual variable. To address the resultant task, we exploit the proximal block coordinate descent (PBCD) method and then establish the convergence of the suggested algorithm. Specifically, the objective function value is guaranteed to be convergent while the variable sequence has a subsequence converging to a critical point. Experimental results based on real-world images and videos exhibit the superiority of the devised approach over several state-of-the-art algorithms in terms of recovery performance. MATLAB code is available at https://github.com/Li-X-P/Code-of-Robust-Tensor-Completion.

A geometric integration approach to smooth optimization: foundations of the discrete gradient method

Abstract Discrete gradient methods are geometric integration techniques that can preserve the dissipative structure of gradient flows. Due to the monotonic decay of the function values, they are well suited for general convex and nonconvex optimization problems. Both zero- and first-order algorithms can be derived from the discrete gradient method by selecting different discrete gradients. In this paper, we present a thorough analysis of the discrete gradient method for optimization that provides a solid theoretical foundation. We show that the discrete gradient method is well-posed by proving the existence of iterates for any positive time step, as well as uniqueness in some cases, and propose an efficient method for solving the associated discrete gradient equation. Moreover, we establish an $\text{O}(1/k)$ convergence rate for convex objectives and prove linear convergence if instead the Polyak–Łojasiewicz inequality is satisfied. The analysis is carried out for three discrete gradients—the Gonzalez discrete gradient, the mean value discrete gradient, and the Itoh–Abe discrete gradient—as well as for a randomised Itoh–Abe method. Our theoretical results are illustrated with a variety of numerical experiments, and we furthermore demonstrate that the methods are robust with respect to stiffness.

Application of melt pool profiles for parameter calibration of Goldak’s heat source model

Goldak’s ellipsoidal heat source model is widely accepted in laser powder bed fusion (LPBF) process simulations. The model is dependent on three inherent flux distribution parameters and absorptivity which need to be calibrated before application. However, the uniqueness of the calibrated heat source parameters has not been established, which is crucial to accurately represent the energy density distribution in numerical studies. This paper proposed a novel parametric optimization method that can uniquely calibrate these four heat source parameters with high accuracy. Both the simulated cross-sectional (CS) profile and the mid-front (MF) profile of the melt pool were used during calibration. The differences between the experimental and simulated profiles formed the objective function of the parametric optimization problem, which was calculated by using the least power norm method on the deviations between melt pool boundary curves. A genetic algorithm (GA) with elitism selection approach was employed to optimize the heat source parameters by minimizing this single objective function value. To theoretically verify the applicability of the method, two theoretical case studies involving two distinct sets of heat source parameters were carried out. A unique solution of Goldak’s heat source parameters with less than 2 % error can be achieved within 50 generations of the optimization process. A single-track experiment was conducted to demonstrate the application of the proposed method in a real scenario. The CS and MF melt pool profiles were extracted and the calibration procedure was applied. The profiles from simulation with the calibrated heat source parameters exhibited a good agreement with experimental results. The calibrated parameters were used to simulate multi-track printing on a single layer for validation of the proposed method. The CS melt pool profiles obtained from the multi-track simulation and multi-track experiment agreed well, which validated the accuracy of the proposed calibration method and its utility in obtaining the key Goldak heat source parameters for LPBF simulation.

Quantum optimization algorithm based on multistep quantum computation

We present a quantum algorithm for finding the minimum of a function based on multistep quantum computation, and apply the algorithm for solving optimization problems with continuous variables. We construct the state space of the problem by discretizing the variables of the problem, and divide the state space according to the function values of the vectors of the state space. By comparing the function values of the vectors with a series of threshold values in decreasing order, we construct a sequence of Hamiltonians where the search space of a Hamiltonian is nested in that of the previous one. By applying a multistep quantum computation process for finding the ground state of the last Hamiltonian, the optimal vector of the state space of the problem is located in a small search space and can be determined efficiently. One of the most difficult problems in optimization algorithms is that a trial vector is trapped in a deep local minimum while the global minimum is missed, this problem can be alleviated in our algorithm and the run time is proportional to the number of the steps of the algorithm, provided that the reduction rate of the search spaces is polynomial large. We discuss the implementation of the algorithm and test the algorithm for some test functions.

The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction III: convergence analysis

Abstract In the former parts of this paper, the spectral conjugate gradient (SCG) algorithm was proposed to identify the optimal bottom terrain used for predicting the meteorological fields. In this part, the convergence analysis of the SCG method is presented to verify its validity when solving a typical ill-posed inverse problem. The scaled cost function, the scaled gradient norm, the linear convergence rate, and the Hessian approximation condition number are evaluated for convergence analysis. The SCG method reduces the cost functional value and the gradient norm sufficiently. It always performs linear rates of convergence with a quickly reduced condition number of the Hessian matrix. Sometimes it provides very fast linear convergence rates. The SCG code with optimal step size and Andrei’s equation is effective on ill-posed test problems.

A novel machine learning method for the design optimization of diamond waveguides fabricated by femtosecond laser writing

We report on a novel machine learning method for the design optimization of femtosecond (fs) laser written dielectric waveguides. Experimental results previously obtained from the optical characterization of fs laser written depressed cladding diamond waveguides have been used to form statistically generated regression models. Design variables such as core diameter and number of written tracks were varied to both minimize the propagation loss as well as to establish a full-factorial experimental design. The regression models were used to conduct a multi-objective optimization study to optimize the competing objectives such as maximizing the refractive index contrast while minimizing the propagation loss and V-number by using a genetic algorithm. Optimization was subject to a nonlinear Rayleigh range constraint to ensure that the structure was in the waveguiding regime. Results from the optimization revealed the optimum variables to achieve low-loss and nearly single-mode guiding for a fs laser written diamond waveguide. Using the solution sets of design parameters resulting from the optimization study and their corresponding objective function values, important correlations between the design parameters and the objective functions have been revealed. With this regard, it has been shown that the number of written tracks is a much more dominant parameter, when compared to core diameter, during the design of a fs laser written circular depressed cladding diamond waveguide. The proposed method should be applicable not only to diamond waveguides but also to a wide range of dielectric waveguides fabricated by fs laser writing.

Factors influencing muslims’ purchase intention of halal-certified over-the-counter (OTC) medicines in Bekasi, Indonesia

Self-medication in Indonesia has recently increased; consumers can easily purchase non-prescription medicines, many people use over-the-counter (OTC) medicines as an alternative for self-medication. In this study, the researchers have chosen to address the gap in the literature pertaining to purchase intention on halal pharmaceuticals, whereas, the focus is on purchase intention of halal certified OTC medicines. This study was conducted in Bekasi, Indonesia. 325 Muslim consumers in Bekasi, Indonesia, participated in this study. The information was gathered using questionnaires with convenient sampling techniques. Through utilizing the theory of consumption value, which consists of the factors of functional value (price), functional value (quality), social value (pharmacist advice), emotional value (halal logo), conditional value, and epistemic value of respondents' intentions to purchase halal-certified OTC medicines. The findings of this study, three factors: functional value (quality), emotional value, and epistemic value, have a significant relationship with the purchase intention of halal-certified OTC medicines in Bekasi, Indonesia.

Effect of Gamma Irradiation (Cesium-137) on the Properties of Chemical Functional Groups from Collagen Extraction of Snakehead Fish Scales (Channa striata)

This study aims to determine the absorption spectrum of the functional groups of snakehead fish scale collagen before irradiation and the effect of the dose of Cesium-137 irradiation on the functional groups in snakehead fish scale collagen. The extraction method used is acid extraction. Measurement of the absorption spectrum of collagen samples from snakehead fish scales using an FTIR spectrophotometer. Samples were irradiated using doses of 10 mGy, 30 mGy and 50 mGy. The results of the functional group values before irradiation produce amide A, amide I, amide II and amide III which fill the absorption area of the functional group. The results of the functional group values produced after irradiation for a dose of 10 mGy produced amide I, amide II and amide III which filled the absorption area. Amide A and amide B do not fill the absorption region at a dose of 10 mGy. Doses of 30 mGy and 50 mGy produced amide A, amide I, amide II and amide III which filled the absorption area. Amide B did not fill the absorption area at doses of 30 mGy and 50 mGy. Amide groups that are not detected or do not meet standard collagen functional group standards can be caused by the chemical composition of the fish scale extraction process.

An Analysis of Determinants of Consumer Dissatisfaction towards Plastic Packaging Containing Ready-to-Eat and Serve Meal

Packaging is an essential aspect of product and its protection, serving as the first point of interaction for consumers. It encompasses a diverse range of materials and designs, tailored to meet the specific needs of different industries and products. For this study, a total sample of 500 respondents was collected. Data were analyzed by using structural equation modeling technique (SEM). Our analysis suggests that functional value is not found to have significant impact on consumer attitude towards plastic packaging while sensory appeal, food safety concern and social value has significant impact on consumer attitude towards plastic packaging. Results show that consumers are worried about safety of food. Primary purpose of packaging is to keep food protected from spoilage and contamination by elements such as air, light, moisture and pests. Research shows that consumers tend to associate food packaged in non-plastic materials with higher status as compared to food packed in plastic packaging, evoking a premium perception linked to elevated societal class. Consumer perceptions of food packed in plastic packaging are significantly shaped by the social value attributed to packaging materials, influenced by environmental awareness and social status. Sensory appeal plays a crucial role in influencing consumer attitudes towards packaging, especially when it comes to ready-to-eat or ready-to-serve food products. The packaging of a product can have a substantial impact on a consumer's perception of the food's quality, freshness, and overall desirability. The results of our study further show that consumer attitude towards food in plastic packaging has a significant impact on consumer dissatisfaction. The study underscores three key implications: first, marketers should prioritize health-friendly packaging materials for safety, second innovation in packaging should enhance both product protection and sensory appeal, and third, Government intervention is essential to address food safety concern and protect consumers and the food supply chain.

A Study on the Structural Relationship Between Tourists' Customer Value, Functional Value, Attitude, and Behavioral Intention in Tourism Destinations

A Study on the Structural Relationship Between Tourists' Customer Value, Functional Value, Attitude, and Behavioral Intention in Tourism Destinations

Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.