Iskanje po katalogu digitalne knjižnice

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: 0
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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: 2
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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)

Opis: A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.
Ključne besede: community detection algorithms, dependency trees, relation extraction
Objavljeno v DKUM: 17.01.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (3,12 MB)

Opis: Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems. We compare the types of data partitioning and types of FL (horizontal partitioning and horizontal FL, vertical partitioning and vertical FL, and hybrid partitioning and transfer FL), architectures (centralized and decentralized), levels of federation (cross-device and cross-silo), and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. We also briefly review how the communication challenge is solved by different approaches. This work is useful for gaining an overview of the FL techniques used in agriculture and the progress made in this field.
Ključne besede: federated learning, agriculture, architecture, data partitioning, federation scal, aggregation algorithms, communication bottleneck
Objavljeno v DKUM: 05.06.2024; Ogledov: 142; Prenosov: 26
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