Iskanje po katalogu digitalne knjižnice

Opis: The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known merit factor value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors.
Ključne besede: binary sequences, Golay's merit factor, autocorrelation, algorithms
Objavljeno v DKUM: 30.05.2025; Ogledov: 0; Prenosov: 0
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Opis: Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms.
Ključne besede: algorithmic performance, experimental evaluation, metaheuristics, quantile, confidence interval, stochastic algorithms, evolutionary computation, swarm intelligence, experimental methodology
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 9
Celotno besedilo (490,48 KB)
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Opis: Drawing is a well-established method to improve the mechanical properties of wet-spun fibers, as it orients the polymer chains, increases the chain density, and homogenizes the microstructure. This work aims to investigate how drawing variables, such as the draw ratio, drawing speed, and temperature affect the elastic modulus (E) and the strain at break (εB) of biobased wet-spun fibers constituted by neat polylactic acid (PLA) and a PLA/poly(dodecamethylene 2,5-furandicarboxylate) (PDoF) (80/20 wt/wt) blend. Drawing experiments were conducted with a design of experiment (DOE) approach following a 24 full factorial design. The results of the quasi-static tensile tests on the drawn fibers, analyzed by the analysis of variance (ANOVA) and modeled through the response surface methodology (RSM), highlight that the presence of PDoF significantly lowers E, which instead is maximized if the temperature and draw ratio are both low. On the other hand, εB is enhanced when the drawing is performed at a high temperature. Finally, a genetic algorithm was implemented to find the optimal combination of drawing parameters that maximize both E and εB. The resulting Pareto curve highlights that the temperature influences the mechanical results only for neat PLA fibers, as the stiffness increases by drawing at lower temperatures, while optimal Pareto points for PLA/PDoF fibers are mainly determined by the draw ratio and the draw rate.
Ključne besede: fibers, poly(lactic acid), furanoate polyesters, drawing, response surface methodology, genetic algorithms
Objavljeno v DKUM: 24.03.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,23 MB)
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Opis: In our study, we explored the complexities of overhead transmission line (OTL) engineering, specifically focusing on their responses to varying atmospheric conditions (ambient temperature, ambient humidity, solar irradiance, ambient pressure, wind speed, wind direction), and electric current usage. Our goal was to comprehend how these independent variables impact critical responses (dependent variables) such as conductor temperature, conductor sag, tower leg stress, and vibrations – parameters crucial for electric distribution. We modelled the target output variable as a polynomial of a certain degree of the input variables. The precise forms of the polynomial were determined using the genetic algorithms (GA). Developed models are essential for quantifying the influence of each input parameter, enriching our understanding of essential system elements. They provide long-term predictions for assessing transmission line lifespan and structural stability, with particularly high precision in forecasting temperature and sag angle. It is important to note that certain engineering parameters, such as material properties and load considerations, were not included in our research, potentially influencing accuracy.
Ključne besede: Overhead Transmission Lines (OTL), machine learning, modelling, optimization, genetic algorithms (GA)
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (417,77 KB)
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Opis: Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapesevolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence.This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. Whilethe problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individualframes, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from itmakes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is stillconsidered an open problem. We describe and categorize existing approaches while pointing out the current challenges in thefield and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reportedperformance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discusspotential future trends in the field.
Ključne besede: compression algorithms, data compression, modelling, polygonal mesh reduction
Objavljeno v DKUM: 07.02.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (3,11 MB)