Computation

15 pages, 2372 KiB

Open AccessArticle

Nonsingular Terminal Sliding Mode Control for Vehicular Platoon Systems with Measurement Delays and Noise

by Mengjie Li, Shaobao Li, Xiaoyuan Luo and Zhizhong Bai

Computation 2024, 12(10), 210; https://doi.org/10.3390/computation12100210 - 20 Oct 2024

Viewed by 501

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to [...] Read more.

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to deal with the measurement delays and noise while improving convergence performance, the platoon control problem of vehicular systems is studied under the nonsingular terminal sliding mode control (NTSMC) framework. A sliding mode observer (SMO) is proposed to estimate the states affected by measurement delays and noise. A distributed NTSMC scheme is developed for the platooning of the vehicular systems and ensures the convergence of the sliding mode surface affected by measurement delays and noise. One salient feature of the proposed SMO is that it can handle time-varying measurement delays rather than constant ones. Moreover, the control law is free of initial spacing error conditions under the employed coupled spacing policy. Numerical simulations are finally provided to demonstrate the effectiveness and efficiency of the proposed algorithm. Full article

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10 pages, 2132 KiB

Open AccessArticle

Stochastic Fusion Techniques for State Estimation

by Alaa H. Ahmed and Henrietta Tomán

Computation 2024, 12(10), 209; https://doi.org/10.3390/computation12100209 - 17 Oct 2024

Viewed by 652

Abstract

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since [...] Read more.

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since their information is most likely to be trustworthy. This study stochastically investigates the three most common fusion techniques: Kalman filtering, particle filtering and Bayesian probability (which is the basis of other techniques). The paper focuses on using fusion techniques in the context of state estimation for dynamic systems to improve reliability and accuracy. The fusion methods are investigated using different types of datasets to find out their performance and accuracy in state estimation. Full article

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20 pages, 1645 KiB

Open AccessArticle

Classification of Acoustic Tones and Cardiac Murmurs Based on Digital Signal Analysis Leveraging Machine Learning Methods

by Nataliya Shakhovska and Ivan Zagorodniy

Computation 2024, 12(10), 208; https://doi.org/10.3390/computation12100208 - 17 Oct 2024

Viewed by 801

Abstract

Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists’ knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of [...] Read more.

Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists’ knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of acoustic sounds and heart murmurs using digital signal analysis. Using advanced machine learning algorithms, we aim to improve the accuracy, speed, and accessibility of heart murmur detection. The proposed method includes feature extraction from digital auscultatory recordings, preprocessing using signal processing techniques, and classification using state-of-the-art machine learning models. We evaluated the performance of different machine learning algorithms, such as convolutional neural networks (CNNs), random forests (RFs) and support vector machines (SVMs), on a selected heart noise dataset. The results show that our framework achieves high accuracy in differentiating normal heart sounds from different types of heart murmurs and provides a robust tool for clinical decision-making. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)

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24 pages, 5846 KiB

Open AccessArticle

Enhanced Drag Force Estimation in Automotive Design: A Surrogate Model Leveraging Limited Full-Order Model Drag Data and Comprehensive Physical Field Integration

by Kalinja Naffer-Chevassier, Florian De Vuyst and Yohann Goardou

Computation 2024, 12(10), 207; https://doi.org/10.3390/computation12100207 - 16 Oct 2024

Viewed by 855

Abstract

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is [...] Read more.

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is to take advantage not only of the drag coefficients but also physical fields such as velocity, pressure, and kinetic energy evaluated on a cutting plane in the wake of the vehicle and perpendicular to the road. This additional “augmented” information provides a more accurate and robust prediction of the drag force compared to a standard surface response methodology. As a first step, an original reparametrization of the shape based on combination coefficients of shape principal components is proposed, leading to a low-dimensional representation of the shape space. The second step consists in determining principal components of the x-direction momentum flux through a cutting plane behind the car. The final step is to find the mapping between the reduced shape description and the momentum flux formula to achieve an accurate drag estimation. The resulting surrogate model is a space-parameter separated representation with shape principal component coefficients and spatial modes dedicated to drag-force evaluation. The algorithm can deal with shapes of variable mesh by using an optimal transport procedure that interpolates the fields on a shared reference mesh. The Machine Learning algorithm is challenged on a car concept with a three-dimensional shape design space. With only two well-chosen samples, the numerical algorithm is able to return a drag surrogate model with reasonable uniform error over the validation dataset. An incremental learning approach involving additional high-fidelity computations is also proposed. The leading algorithm is shown to improve the model accuracy. The study also shows the sensitivity of the results with respect to the initial experimental design. As feedback, we discuss and suggest what appear to be the correct choices of experimental designs for the best results. Full article

(This article belongs to the Special Issue Synergy between Multiphysics/Multiscale Modeling and Machine Learning)

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18 pages, 1234 KiB

Open AccessArticle

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

by Dadiana Dabija, Carmen-Eugenia Nastase, Ancuţa Chetrariu and Adriana Dabija

Computation 2024, 12(10), 206; https://doi.org/10.3390/computation12100206 - 15 Oct 2024

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Abstract

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk [...] Read more.

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices. Full article

(This article belongs to the Special Issue Computational Approaches in Corporate Finance, Risk Management and Financial Markets)

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24 pages, 988 KiB

Open AccessArticle

Refining the Eel and Grouper Optimizer with Intelligent Modifications for Global Optimization

by Glykeria Kyrou, Vasileios Charilogis and Ioannis G. Tsoulos

Computation 2024, 12(10), 205; https://doi.org/10.3390/computation12100205 - 14 Oct 2024

Viewed by 675

Abstract

Global optimization is used in many practical and scientific problems. For this reason, various computational techniques have been developed. Particularly important are the evolutionary techniques, which simulate natural phenomena with the aim of detecting the global minimum in complex problems. A new evolutionary [...] Read more.

Global optimization is used in many practical and scientific problems. For this reason, various computational techniques have been developed. Particularly important are the evolutionary techniques, which simulate natural phenomena with the aim of detecting the global minimum in complex problems. A new evolutionary method is the Eel and Grouper Optimization (EGO) algorithm, inspired by the symbiotic relationship and foraging strategy of eels and groupers in marine ecosystems. In the present work, a series of improvements are proposed that aim both at the efficiency of the algorithm to discover the total minimum of multidimensional functions and at the reduction in the required execution time through the effective reduction in the number of functional evaluations. These modifications include the incorporation of a stochastic termination technique as well as an improvement sampling technique. The proposed modifications are tested on multidimensional functions available from the relevant literature and compared with other evolutionary methods. Full article

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Graphical abstract

15 pages, 3423 KiB

Open AccessArticle

Comparative Study of Deflector Configurations under Variable Vertical Angle of Incidence and Wind Speed through Transient 3D CFD Modeling of Savonius Turbine

by Hady Aboujaoude, Guillaume Polidori, Fabien Beaumont, Sébastien Murer, Yessine Toumi and Fabien Bogard

Computation 2024, 12(10), 204; https://doi.org/10.3390/computation12100204 - 14 Oct 2024

Viewed by 961

Abstract

The demand for clean and sustainable energy has led to the exploration of innovative technologies for renewable energy generation. The Savonius turbine has emerged as a promising solution for harnessing wind energy in urban environments due to its unique design, simplicity, structural stability, [...] Read more.

The demand for clean and sustainable energy has led to the exploration of innovative technologies for renewable energy generation. The Savonius turbine has emerged as a promising solution for harnessing wind energy in urban environments due to its unique design, simplicity, structural stability, and ability to capture wind energy from any direction. However, the efficiency of Savonius turbines poses a challenge that affects their overall performance. Extensive research efforts have been dedicated to enhancing their efficiency and optimizing their performance in urban settings. For instance, an axisymmetric omnidirectional deflector (AOD) was introduced to improve performance in all wind directions. Despite these advancements, the effect of wind incident angles on Savonius turbine performance has not been thoroughly investigated. This study aims to fill this knowledge gap by examining the performance of standard Savonius configurations (STD) compared to the basic configuration of the deflector (AOD1) and to the optimized one (AOD2) under different wind incident angles and wind speeds. One key finding was the consistent superior performance of this AOD2 configuration across all incident angles and wind speeds. It consistently outperformed the other configurations, demonstrating its potential as an optimized configuration for wind turbine applications. For instance, at an incident angle of 0°, the power coefficient of the configuration of AOD2 was 61% more than the STD configuration. This ratio rose to 88% at an incident angle of 20° and 125% at an incident angle of 40°. Full article

(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Heat and Mass Transfer (ICCHMT 2023))

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15 pages, 2268 KiB

Open AccessArticle

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

by Ivan Izonin, Roman Tkachenko, Pavlo Yendyk, Iryna Pliss, Yevgeniy Bodyanskiy and Michal Gregus

Computation 2024, 12(10), 203; https://doi.org/10.3390/computation12100203 - 11 Oct 2024

Viewed by 744

Abstract

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do [...] Read more.

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)

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35 pages, 15835 KiB

Open AccessArticle

Explainable Boosting Machine Learning for Predicting Bond Strength of FRP Rebars in Ultra High-Performance Concrete

by Alireza Mahmoudian, Maryam Bypour and Mahdi Kioumarsi

Computation 2024, 12(10), 202; https://doi.org/10.3390/computation12100202 - 9 Oct 2024

Viewed by 894

Abstract

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and [...] Read more.

Aiming at evaluating the bond strength of fiber-reinforced polymer (FRP) rebars in ultra-high-performance concrete (UHPC), boosting machine learning (ML) models have been developed using datasets collected from previous experiments. The considered variables in this study are rebar type and diameter, elastic modulus and tensile strength of rebars, concrete compressive strength and cover, embedment length, and test method. The dataset contains two test methods: pullout tests and beam tests. Four types of rebar, including carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), basalt, and steel rebars, were considered. The boosting ML models applied in this study include AdaBoost, CatBoost, Gradient Boosting, XGBoost, and Hist Gradient Boosting. After hyperparameter tuning, these models demonstrated significant improvements in predictive accuracy, with XGBoost achieving the highest R² score of 0.95 and the lowest Root Mean Square Error (RMSE) of 2.21. Shapley values analysis revealed that tensile strength, elastic modulus, and embedment length are the most critical factors influencing bond strength. The findings offer valuable insights for applying ML models in predicting bond strength in FRP-reinforced UHPC, providing a practical tool for structural engineering. Full article

(This article belongs to the Special Issue Computational Methods in Structural Engineering)

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18 pages, 2871 KiB

Open AccessArticle

A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss

by Xuefeng Chen and Liangyu Huang

Computation 2024, 12(10), 201; https://doi.org/10.3390/computation12100201 - 4 Oct 2024

Viewed by 1241

Abstract

This paper proposes a facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model effectively balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) [...] Read more.

This paper proposes a facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model effectively balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depth-wise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while capturing the dependencies between different facial regions. Additionally, a new loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets demonstrate that the LFNSB model achieves competitive recognition accuracy, with 63.12% accuracy on AffectNet-8, 66.57% accuracy on AffectNet-7, and 91.07% accuracy on RAF-DB, while significantly reducing the model complexity. Full article

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18 pages, 4031 KiB

Open AccessArticle

Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part B: Hypersonic Vehicles

by Angelos Klothakis and Ioannis K. Nikolos

Computation 2024, 12(10), 200; https://doi.org/10.3390/computation12100200 - 4 Oct 2024

Viewed by 736

Abstract

In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper [...] Read more.

In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper initially explores the current state of hypersonic vehicle development and deployment, as well as the propulsion technologies involved. At next, two additional test cases, used for the evaluation of DSMC code SPARTA are analyzed: a Mach 12.4 flow over a flared cylinder and a Mach 15.6 flow over a 25/55-degree biconic. These (2D-axisymmetric) test cases have been selected as they are tailored for the assessment of flow and heat transfer characteristics of present and future hypersonic vehicles, for both their external and internal aerodynamics. These test cases exhibit (in a larger range compared to the test cases presented in Part A of this work) shock–boundary and shock–shock interactions, which can provide a fair assessment of the SPARTA DSMC solver accuracy, in flow conditions which characterize hypersonic flight and can adequately test its ability to qualitatively and quantitatively capture the complicated physics behind such demanding flows. This validation campaign of SPARTA provided valuable experience for the correct tuning of the various parameters of the solver, especially for the use of adequate computational grids, thus enabling its subsequent application to more complicated three-dimensional test cases of hypersonic vehicles. Full article

(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)

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21 pages, 4973 KiB

Open AccessArticle

Solving the Problem of Fuzzy Partition-Distribution with Determination of the Location of Subset Centers

by Anatoly Bulat, Elena Kiseleva, Sergiy Yakovlev, Olga Prytomanova and Danylo Lebediev

Computation 2024, 12(10), 199; https://doi.org/10.3390/computation12100199 - 3 Oct 2024

Viewed by 613

Abstract

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional [...] Read more.

A large number of real-world problems from various fields of human activity can be reduced to optimal partitioning-allocation problems with the purpose of minimizing the partitioning quality criterion. A typical representative of such problem is an infinite-dimensional transportation problem and more generalized problems—infinite-dimensional problems of production centers placement along with the partitioning of the area of continuously distributed consumers with the purpose of minimizing transportation and production costs. The relevant problems are characterized by some kind of uncertainty level of a not-probabilistic nature. A method is proposed to solve an optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. The method is based on the synthesis of the methods of fuzzy theory and optimal partitioning-allocation theory, which is a new science field in infinite-dimensional mathematical programming with Boolean variables. A theorem was proved that determines the form of the optimal solution of the corresponding optimal fuzzy partitioning-allocation problem with the subsets centers placement for sets of n-dimensional Euclidean space. An algorithm for solving fuzzy partitioning-allocation problems is proposed, which is based on the proved theorem and on a variant of Shor’s r-algorithm—a non-differential optimization method. The application of the proposed method is demonstrated on model tasks, where the coefficient of mistrust is integrated to interpret the obtained result—the minimum value of the membership function, which allows each point of the set partition to be assigned to a specific fuzzy subset. Full article

(This article belongs to the Special Issue Integrated Computer Technologies in Mechanical Engineering—Synergetic Engineering III)

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24 pages, 16220 KiB

Open AccessArticle

Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part A: Fundamentals

by Angelos Klothakis and Ioannis K. Nikolos

Computation 2024, 12(10), 198; https://doi.org/10.3390/computation12100198 - 1 Oct 2024

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Abstract

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation [...] Read more.

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations. Full article

(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)

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17 pages, 1370 KiB

Open AccessArticle

A New Investigation on Dynamics of the Fractional Lengyel-Epstein Model: Finite Time Stability and Finite Time Synchronization

by Hani Mahmoud Almimi, Ma’mon Abu Hammad, Ghadeer Farraj, Issam Bendib and Adel Ouannas

Computation 2024, 12(10), 197; https://doi.org/10.3390/computation12100197 - 30 Sep 2024

Viewed by 625

Abstract

In this paper, we present an investigation into the stability of equilibrium points and synchronization within a finite time frame for fractional-order Lengyel–Epstein reaction-diffusion systems. Initially, we utilize Lyapunov theory and multiple criteria to examine the finite-time stability of equilibrium points. Following this [...] Read more.

In this paper, we present an investigation into the stability of equilibrium points and synchronization within a finite time frame for fractional-order Lengyel–Epstein reaction-diffusion systems. Initially, we utilize Lyapunov theory and multiple criteria to examine the finite-time stability of equilibrium points. Following this analysis, we design efficient, interdependent linear controllers. By applying a Lyapunov function, we define new adequate conditions to ensure finite-time synchronization within a specified time interval. Finally, we provide two illustrative examples to demonstrate the effectiveness and practicality of our proposed method and validate the theoretical outcomes. Full article

(This article belongs to the Special Issue Mathematical Modeling and Study of Nonlinear Dynamic Processes)

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11 pages, 352 KiB

Open AccessArticle

Model Predictive Control of Spatially Distributed Systems with Spatio-Temporal Logic Specifications

by Ikkei Komizu, Koichi Kobayashi and Yuh Yamashita

Computation 2024, 12(10), 196; https://doi.org/10.3390/computation12100196 - 30 Sep 2024

Viewed by 540

Abstract

In this paper, for spatially distributed systems, we propose a new method of model predictive control with spatio-temporal logic specifications. We formulate the finite-time control problem with specifications described by SSTL_f (signal spatio-temporal logic over finite traces) formulas. In the problem formulation, [...] Read more.

In this paper, for spatially distributed systems, we propose a new method of model predictive control with spatio-temporal logic specifications. We formulate the finite-time control problem with specifications described by SSTL_f (signal spatio-temporal logic over finite traces) formulas. In the problem formulation, the feasibility is guaranteed by representing control specifications as a penalty in the cost function. Time-varying weights in the cost function are introduced to satisfy control specifications as well as possible. The finite-time control problem can be written as a mixed integer programming (MIP) problem. According to the policy of model predictive control (MPC), the control input can be generated by solving the finite-time control problem at each discrete time. The effectiveness of the proposed method is presented through a numerical example. Full article

(This article belongs to the Special Issue Nonlinear System Modelling and Control)

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12 pages, 384 KiB

Open AccessArticle

Networks Based on Graphs of Transient Intensities and Product Theorems in Their Modelling

by Gurami Tsitsiashvili

Computation 2024, 12(10), 195; https://doi.org/10.3390/computation12100195 - 27 Sep 2024

Viewed by 488

Abstract

This paper considers two models of queuing with a varying structure based on the introduction of additional transient intensities into known models or their combinations, which create stationary distributions convenient for calculation. In the first model, it is a probabilistic mixture of known [...] Read more.

This paper considers two models of queuing with a varying structure based on the introduction of additional transient intensities into known models or their combinations, which create stationary distributions convenient for calculation. In the first model, it is a probabilistic mixture of known stationary distributions with given weights. In the second model, this uniform distribution is repeatedly used in physical statistics. Both models are based on the selection of states, between which additional transient intensities are introduced. The algorithms used in this paper for introducing new transient intensities are closely related to the concept of flow in a deterministic transport network. The introduced controls are selected so that the marginal distribution of the combined system is a mixture of the marginal distributions of the combined systems with different weights determined by the introduced transient intensities. As a result, the process of functioning of the combined system is obtained by switching processes corresponding to different combined systems at certain points in time. Full article

(This article belongs to the Section Computational Engineering)

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20 pages, 1008 KiB

Open AccessArticle

Dynamics of Blood Flows in the Cardiocirculatory System

by Maria Pia D’Arienzo and Luigi Rarità

Computation 2024, 12(10), 194; https://doi.org/10.3390/computation12100194 - 25 Sep 2024

Viewed by 875

Abstract

Models and simulations of blood flow in vascular networks are useful to deepen knowledge of cardiovascular diseases. This paper considers a model based on partial differential equations that mimic the dynamics of vascular networks in terms of flow velocities and arterial pressures. Such [...] Read more.

Models and simulations of blood flow in vascular networks are useful to deepen knowledge of cardiovascular diseases. This paper considers a model based on partial differential equations that mimic the dynamics of vascular networks in terms of flow velocities and arterial pressures. Such quantities are found by using ad hoc numerical schemes to examine variations in the pressure and homeostatic conditions of a whole organism. Two different case studies are examined. The former uses 15 arteries—a network that shows the real oscillations in pressures and velocities due to variations in artery volume. The latter focuses on the 55 principal arteries, and blood flows are studied by using a model of a heart valve that opens and closes via the differences in the aortic and left ventricle pressures. This last case confirms the possibility of autonomously regulating blood pressure and velocity in arteries in general and when tilt tests are applied to patients. Full article

(This article belongs to the Section Computational Engineering)

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16 pages, 615 KiB

Open AccessArticle

Computational Modeling of the Coffee Consumer Experience and Its Impact on the Sustainability of the Regional Industry in Peru

by Emma Verónica Ramos Farroñán, Marco Agustín Arbulu-Ballesteros, Nancy Mercedes Soto Deza, Sandra Elizabeth Pagador Flores and Karla Paola Agurto Ruiz

Computation 2024, 12(10), 193; https://doi.org/10.3390/computation12100193 - 24 Sep 2024

Viewed by 925

Abstract

This study addresses the significant social value of understanding consumer experiences in the coffee market, which is crucial for enhancing local economic sustainability and consumer satisfaction in the cities of Piura, Trujillo, and Chiclayo in Peru. The objective of this research was to [...] Read more.

This study addresses the significant social value of understanding consumer experiences in the coffee market, which is crucial for enhancing local economic sustainability and consumer satisfaction in the cities of Piura, Trujillo, and Chiclayo in Peru. The objective of this research was to evaluate the coffee consumption experience of 1190 consumers using structural equation modeling. Methodologically, a detailed survey was employed to capture various dimensions of consumer experience. The results revealed a strong positive effect of perceived quality on hedonic value (β = 0.776; p < 0.001), underscoring the importance of high sensory standards. Brand experiences significantly influenced quality beliefs (β = 0.399; p < 0.001) and perceived utility (β = 0.733; p < 0.001), though there was no direct connection with hedonic valuation, indicating the need for further analysis. The findings highlighted that hedonic value, associated with emotional satisfaction, predominates over utilitarian value in driving brand loyalty (β = 0.908 vs. β = 0.076; p < 0.001). This provides strategic insights into incorporating symbolic and experiential benefits in marketing. In conclusion, the study offers quantitative evidence on shaping consumer experiences in the coffee market by focusing on sensory quality and affective brand identity. Full article

(This article belongs to the Section Computational Engineering)

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Computation, Volume 12, Issue 10 (October 2024) – 18 articles

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