Symmetry

Research

27 pages, 1817 KiB

Open AccessArticle

Comparison of the Meta-Heuristic Algorithms for Maximum Likelihood Estimation of the Exponentially Modified Logistic Distribution

by Pelin Kasap and Adi Omaia Faouri

Symmetry 2024, 16(3), 259; https://doi.org/10.3390/sym16030259 - 20 Feb 2024

Viewed by 2125

Abstract

Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for widely used [...] Read more.

Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, log-logistic, and others. In this study, the unknown parameters of the Exponentially-Modified Logistic distribution are estimated using the maximum likelihood method. Five meta-heuristic algorithms, including the genetic algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and sine cosine algorithm, are applied in order to solve the nonlinear likelihood equations of the study model. The efficiencies of all maximum likelihood estimates for these algorithms are compared via an extensive Monte Carlo simulation study. The performance of the maximum likelihood estimates for the location and scale parameters of the Exponentially-Modified Logistic distribution developed with the genetic algorithm and grey wolf optimization algorithms is the most efficient among others, according to simulation findings. However, the genetic algorithm is two times faster than grey wolf optimization and can be considered better than grey wolf optimization considering the computation time criterion. Six real datasets are analyzed to show the flexibility of this distribution. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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29 pages, 4391 KiB

Open AccessArticle

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

by Witesyavwirwa Vianney Kambale, Mohamed Salem, Taha Benarbia, Fadi Al Machot and Kyandoghere Kyamakya

Symmetry 2024, 16(2), 241; https://doi.org/10.3390/sym16020241 - 16 Feb 2024

Cited by 2 | Viewed by 1697

Abstract

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for [...] Read more.

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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31 pages, 1750 KiB

Open AccessArticle

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

by Antoine Kazadi Kayisu, Witesyavwirwa Vianney Kambale, Taha Benarbia, Pitshou Ntambu Bokoro and Kyandoghere Kyamakya

Symmetry 2024, 16(1), 128; https://doi.org/10.3390/sym16010128 - 21 Jan 2024

Viewed by 2308

Abstract

In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, [...] Read more.

In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban environments are major issues to be resolved. In addition, the unavailability of realistic and various data of such systems makes their deployment, design, and performance evaluation very challenging. As a result, to avoid the constraints of real data collection, using generated artificial datasets is crucial for simulation to test and validate algorithms and models under various scenarios. These artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers and operators to evaluate performance and predict system behavior under various conditions. To generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging infrastructure, and environmental conditions must be considered. In all these elements, symmetry is a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining as much symmetry as possible is a core requirement. This review paper provides a comprehensive literature survey of the most relevant techniques generating synthetic datasets in the literature, with a particular focus on the shared electric automated and connected mobility context. Furthermore, this paper also investigates central issues of these complex and dynamic systems regarding how artificial datasets could be used in the training of ML models to address the repositioning problem. Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios to be effectively represented by the generated datasets. Then, this paper investigates the current challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world, and the validation of generative models. Additionally, it explores how ML-based algorithms can be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other important operational elements. In conclusion, this paper outlines a series of interesting new research avenues concerning the generation of artificial data for SEACM systems. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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20 pages, 823 KiB

Open AccessArticle

Analytical Explicit Formulas of Average Run Length of Homogenously Weighted Moving Average Control Chart Based on a MAX Process

by Rapin Sunthornwat, Saowanit Sukparungsee and Yupaporn Areepong

Symmetry 2023, 15(12), 2112; https://doi.org/10.3390/sym15122112 - 24 Nov 2023

Cited by 3 | Viewed by 1303

Abstract

Statistical process control (SPC) is used for monitoring and detecting anomalies in processes in the areas of manufacturing, environmental studies, economics, and healthcare, among others. Herein, we introduce an innovative SPC approach via mathematical modeling and report on its application via simulation studies [...] Read more.

Statistical process control (SPC) is used for monitoring and detecting anomalies in processes in the areas of manufacturing, environmental studies, economics, and healthcare, among others. Herein, we introduce an innovative SPC approach via mathematical modeling and report on its application via simulation studies to examine its suitability for monitoring processes involving correlated data running on advanced control charts. Specifically, an approach for detecting small to moderate shifts in the mean of a process running on a homogenously weighted moving average (HWMA) control chart, which is symmetric about the center line with upper and lower control limits, is of particular interest. A mathematical model for the average run length (ARL) of a moving average process with exogenous variables (MAX) focused only on the zero-state performance of the HWMA control chart is derived based on explicit formulas. The performance of our approach was investigated in terms of the ARL, the standard deviation of the run length (SDRL), and the median run length (MRL). Numerical examples are given to illustrate the efficacy of the proposed method. A detailed comparative analysis of our method for processes on HWMA and cumulative sum (CUSUM) control charts was conducted for process mean shifts in many situations. For several values of the design parameters, the performances of these two control charts are also compared in terms of the expected ARL (EARL), expected SDRL (ESDRL), and expected MRL (EMRL). It was found that the performance of the HWMA control chart was superior to that of the CUSUM control chart for several process mean shift sizes. Finally, the applicability of our method on a HWMA control chart is provided based on a real-world economic process. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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22 pages, 25538 KiB

Open AccessArticle

Implementation of Finite Element Method Simulation in Control of Additive Manufacturing to Increase Component Strength and Productivity

by Miloš Matúš, Peter Križan, Ján Kijovský, Stanislav Strigáč, Juraj Beniak and Ľubomír Šooš

Symmetry 2023, 15(11), 2036; https://doi.org/10.3390/sym15112036 - 9 Nov 2023

Cited by 2 | Viewed by 2029

Abstract

Additive manufacturing (AM) technologies are becoming a global phenomenon in the manufacturing industry. The progressiveness of additive manufacturing lies in its universality. AM makes it possible to produce parts with complex shapes from different materials without any tools, using only one device. Complex [...] Read more.

Additive manufacturing (AM) technologies are becoming a global phenomenon in the manufacturing industry. The progressiveness of additive manufacturing lies in its universality. AM makes it possible to produce parts with complex shapes from different materials without any tools, using only one device. Complex and time-consuming production preparation is eliminated by using AM. It is used in a wide range of industries. Although additive manufacturing is a progressive technology, the currently applied conservative approach has significant limits. The presented work focuses on the development of a new methodology for controlling the AM process. This methodology is based on the outputs of the strength simulation of a specific component through the finite element method (FEM) and their implementation in the printing software of the production equipment. The developed algorithm for controlling the AM process consists of a sequence of successive steps. The designed CAD model of the component is subjected to FEM simulation in order to analyze the von Mises stress in the entire volume of the loaded component. Stresses are distributed asymmetrically in the volume of the component due to the shape and nature of the load. The results of the FEM analysis allow the definition of the volumes in the component with different levels of infill geometry and infill density based on different levels of stress. The FEM simulation also serves to define the effective fiber orientation. The goal of implementing FEM simulation into the building structure of the component is to achieve a symmetrical distribution of stresses in the entire volume. Through the symmetry of internal stresses, it is possible to obtain more efficient production with high productivity and component strength. The work also deals with experimental research on the effect of the building structure on flexural strength. The results of FEM simulation and experimental research are integrated into the developed slicer software to design a layering of the model and the setting of technological and material parameters of printing. This progressive approach makes it possible to generate data for 3D printing based on FEM analysis of components to obtain an optimized printed structure of components and optimized technological and material parameters with regard to maximizing the strength of components and minimizing production times and costs. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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11 pages, 283 KiB

Open AccessArticle

Euclidean Jordan Algebras, Symmetric Association Schemes, Strongly Regular Graphs, and Modified Krein Parameters of a Strongly Regular Graph

by Luís Almeida Vieira

Symmetry 2023, 15(11), 1997; https://doi.org/10.3390/sym15111997 - 30 Oct 2023

Viewed by 1144

Abstract

In this paper, in the environment of Euclidean Jordan algebras, we establish some inequalities over the Krein parameters of a symmetric association scheme and of a strongly regular graph. Next, we define the modified Krein parameters of a strongly regular graph and establish [...] Read more.

In this paper, in the environment of Euclidean Jordan algebras, we establish some inequalities over the Krein parameters of a symmetric association scheme and of a strongly regular graph. Next, we define the modified Krein parameters of a strongly regular graph and establish some admissibility conditions over these parameters. Finally, we introduce some relations over the Krein parameters of a strongly regular graph. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

21 pages, 18836 KiB

Open AccessArticle

Learning by Autonomous Manifold Deformation with an Intrinsic Deforming Field

by Xiaodong Zhuang and Nikos Mastorakis

Symmetry 2023, 15(11), 1995; https://doi.org/10.3390/sym15111995 - 29 Oct 2023

Cited by 1 | Viewed by 1404

Abstract

A self-organized geometric model is proposed for data dimension reduction to improve the robustness of manifold learning. In the model, a novel mechanism for dimension reduction is presented by the autonomous deforming of data manifolds. The autonomous deforming vector field is proposed to [...] Read more.

A self-organized geometric model is proposed for data dimension reduction to improve the robustness of manifold learning. In the model, a novel mechanism for dimension reduction is presented by the autonomous deforming of data manifolds. The autonomous deforming vector field is proposed to guide the deformation of the data manifold. The flattening of the data manifold is achieved as an emergent behavior under the virtual elastic and repulsive interaction between the data points. The manifold’s topological structure is preserved when it evolves to the shape of lower dimension. The soft neighborhood is proposed to overcome the uneven sampling and neighbor point misjudging problems. The simulation experiment results of data sets prove its effectiveness and also indicate that implicit features of data sets can be revealed. In the comparison experiments, the proposed method shows its advantage in robustness. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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16 pages, 2333 KiB

Open AccessArticle

Electricity Management Policy Applying Data Science and Machine Learning Techniques to Improve Electricity Costs

by Chun-Yao Lee, Kuan-Yu Huang, Chun-Chi Chen, Guang-Lin Zhuo and Maickel Tuegeh

Symmetry 2022, 14(10), 2104; https://doi.org/10.3390/sym14102104 - 10 Oct 2022

Viewed by 1578

Abstract

This paper studies the actual electricity case of a national university in northern Taiwan, pointing out that many schools will face certain asymmetrical information and practical problems in the development of power systems, such as energy-savings and carbon-reduction policies, collecting electricity fees in [...] Read more.

This paper studies the actual electricity case of a national university in northern Taiwan, pointing out that many schools will face certain asymmetrical information and practical problems in the development of power systems, such as energy-savings and carbon-reduction policies, collecting electricity fees in each division, reducing the loss of power outages, expanding the power system capacity, and maintaining power distribution equipment. These problems are closely related to electricity costs, which include general electricity fees, unexpected losses caused by power outages, purchases of replacement power equipment, and maintenance fees of distribution equipment. This paper proposes corresponding improvement plans for each of the problems in the above-mentioned actual case studies and assists school power managers in using symmetrical information to formulate the best strategies to improve electricity costs. Full article

(This article belongs to the Special Issue Simulation and Modelling in Natural Sciences, Biomedicine and Engineering III)

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