Iskanje po katalogu digitalne knjižnice

Opis: Metaheuristic methods are approximate solution techniques that are widely used to solve complex optimization problems. Drawing inspiration from nature, physics, or human behavior, researchers have developed a diverse range of metaheuristic algorithms, either population-based or single-solution-based, with varying neighborhood structures and memory usage. Due to the diversity and complexity of optimization problems in different domains, no single algorithm can be universally effective for all types of problems. This study proposes a novel metaheuristic algorithm, the Golden Drop Algorithm (GDA), which is simple, efficient, and easy to implement. GDA is a physics-inspired method that simulates the propagation of circular waves formed by raindrops falling into water, with wave expansion governed by the golden ratio. The algorithm was evaluated using the Congress on Evolutionary Computation (CEC) 2017 benchmark functions and was compared with three well-known metaheuristics: Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO). The results demonstrate that GDA is not only straightforward and computationally efficient, but also capable of producing competitive and feasible solutions.In 50 % of the CEC 2017 benchmark functions, GDA ranked first, while in 32 % of the functions, it secured second place. In addition, its performance was validated through three benchmark engineering design problems: welded beam design, pressure vessel design, and compression spring design, highlight its robustness and effectiveness in solving constrained nonlinear optimization tasks.
Ključne besede: metaheuristic optimization, physics-inspired metaheuristics, water wave, golden ratio, CEC 2017 benchmark functions, engineering design problems
Objavljeno v DKUM: 22.01.2026; Ogledov: 0; Prenosov: 0
Celotno besedilo (2,41 MB)
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Opis: Artificial intelligence (AI) is set to greatly impact and transform workflows and employment in medicine and healthcare. This study explores how AI can enhance job roles in the healthcare sector and improve the quality and efficiency of services. A two-stage approach is proposed. In the first stage, doctors and healthcare workers are involved in developing AI systems. Their participation ensures ethical use, boosts efficiency, and prevents biases from the data or algorithms. In the second stage, continuous monitoring of AI systems by healthcare profession als is crucial. They act as filters for AI-generated results during decision-making processes. This ongoing oversight helps maintain accuracy and reliability. This two-stage approach highlights the importance of doctor-machine interaction. By integrating AI with human expertise, healthcare services can see significant improvements in quality while minimizing potential risks from AI technologies.
Ključne besede: data bias, human complementation, social physics
Objavljeno v DKUM: 22.07.2025; Ogledov: 0; Prenosov: 9
Celotno besedilo (1,32 MB)
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Opis: Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed directly from in situ near-infrared absorbance spectra acquired during depressurization trials of mixtures of carbon dioxide and a distilled petroleum fraction, with an anomaly detection approach to identify phase transitions. We show that changes in nearest neighbor entropy effectively signal transitions from initially homogeneous mixtures to two-phase equilibria, thereby enabling accurate out-of-sample online predictions of transition pressures. Our approach requires minimum data preprocessing, no specialized detection techniques or visual inspection of the spectra, and is sufficiently general to be adapted for studying phase behavior in other high-pressure systems monitored via spectrophotometry.
Ključne besede: nearest neighbors, permutation entropy, phase transition, complex system, social physics
Objavljeno v DKUM: 04.07.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (3,19 MB)
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Opis: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency. The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process. Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy. The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods. Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method. By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations. In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Ključne besede: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Objavljeno v DKUM: 15.05.2025; Ogledov: 0; Prenosov: 167
Celotno besedilo (17,79 MB)

Opis: Criminal activities are predominantly due to males, with females exhibiting a significantly lower involvement, especially in serious offenses. This pattern extends to organized crime, where females are often perceived as less tolerant to illegal practices. However, the roles of males and females within corruption networks are less understood. Here, we analyze data from political scandals in Brazil and Spain to shed light on gender differences in corruption networks. Our findings reveal that females constitute 10% and 20% of all agents in the Brazilian and Spanish corruption networks, respectively, with these proportions remaining stable over time and across different scandal sizes. Despite this disparity in representation, centrality measures are comparable between genders, except among highly central individuals, for which males are further overrepresented. Additionally, gender has no significant impact on network resilience, whether through random dismantling or targeted attacks on the largest component. Males are more likely to be involved in multiple scandals than females, and scandals predominantly involving females are rare, though these differences are explained by a null network model in which gender is randomly assigned while maintaining gender proportions. Our results further reveal that the underrepresentation of females partially explains gender homophily in network associations, although in the Spanish network, male-to-male connections exceed expectations derived from a null model.
Ključne besede: gender disparity, corruption network, political scandal, social physics, social physics
Objavljeno v DKUM: 25.04.2025; Ogledov: 0; Prenosov: 4
Celotno besedilo (3,50 MB)
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