1. An autonomous field robot Farmbeast - the field robot event 2023 editionGregor Popič, Urban Naveršnik, Jaša Jernej Rakun Kokalj, Erik Rihter, Jurij Rakun, 2024, original scientific article Abstract: In contemporary agricultural automation, the demand for highly adaptive autonomous systems is rapidly increasing. Addressing this need, we introduce the latest iteration of FarmBeast, an advanced autonomous robot designed for precise navigation and operation within the complex terrain of cornfields. This paper details the technical specifications and functionalities of FarmBeast, developed by a Slovenian student team from the University of Maribor for the international Field Robot Event (FRE) 2023. The enhanced version features significant hardware and software upgrades, including a completely new robotic platform, a multichannel LIDAR system, an Xsens IMU, and advanced algorithms for efficient row navigation and weed removal. These integrated technologies aim to improve the efficiency and reliability of agricultural processes, reflecting the broader trend towards digitization and precision farming. Participation in international competitions like FRE provides a valuable platform for students to apply interdisciplinary knowledge, fostering the development of practical skills and understanding the interconnectedness of various scientific disciplines. As highlighted in the results section, FarmBeast performed notably compared to other 14 robots, securing top-five finishes in navigation, plant treatment, and obstacle detection tasks, demonstrating its capabilities in dynamic agricultural settings.
Keywords: precision agriculture, robotics, sensors, algorithms
Published in DKUM: 23.04.2025; Views: 0; Downloads: 1
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2. Probability and certainty in the performance of evolutionary and swarm optimization algorithmsNikola Ivković, Robert Kudelić, Matej Črepinšek, 2022, original scientific article Abstract: 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.
Keywords: algorithmic performance, experimental evaluation, metaheuristics, quantile, confidence interval, stochastic algorithms, evolutionary computation, swarm intelligence, experimental methodology
Published in DKUM: 28.03.2025; Views: 0; Downloads: 8
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3. Maximum number of generations as a stopping criterion considered harmfulMiha Ravber, Shih-Hsi Liu, Marjan Mernik, Matej Črepinšek, 2022, original scientific article Abstract: Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Keywords: evolutionary algorithms, stopping criteria, benchmarking, algorithm termination, algorithm comparison
Published in DKUM: 28.03.2025; Views: 0; Downloads: 2
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4. Statistical modeling and optimization of the drawing process of bioderived polylactide/poly(dodecylene furanoate) wet-spun fibersDaniele Rigotti, Giulia Fredi, Davide Perin, Dimitrios Bikiaris, Alessandro Pegoretti, Andrea Dorigato, 2022, original scientific article Abstract: 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.
Keywords: fibers, poly(lactic acid), furanoate polyesters, drawing, response surface methodology, genetic algorithms
Published in DKUM: 24.03.2025; Views: 0; Downloads: 2
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5. Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learningLucijano Berus, Jernej Hernavs, David Potočnik, Kristijan Šket, Mirko Ficko, 2024, original scientific article Abstract: Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Keywords: smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Published in DKUM: 10.03.2025; Views: 0; Downloads: 6
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6. Study of environmental impacts on overhead transmission lines using genetic algorithmsKristijan Šket, Mirko Ficko, Nenad Gubeljak, Miran Brezočnik, 2023, original scientific article Abstract: 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.
Keywords: Overhead Transmission Lines (OTL), machine learning, modelling, optimization, genetic algorithms (GA)
Published in DKUM: 10.03.2025; Views: 0; Downloads: 3
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8. Survey of inter-prediction methods for time-varying mesh compressionJan Dvořák, Filip Hácha, Gerasimos Arvanitis, David Podgorelec, Konstantinos Moustakas, Libor Váša, 2025, original scientific article Abstract: 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.
Keywords: compression algorithms, data compression, modelling, polygonal mesh reduction
Published in DKUM: 07.02.2025; Views: 0; Downloads: 2
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9. Modeling and optimization of anaerobic digestion technology : current status and future outlookTina Kegl, Eloisa Torres Jiménez, Breda Kegl, Anita Kovač Kralj, Marko Kegl, 2025, review article Keywords: renewable energy, anaerobic digestion, biogas plant, mathematical models, optimization algorithms, products utilization
Published in DKUM: 31.01.2025; Views: 0; Downloads: 13
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10. Improved relation extraction through key phrase identification using community detection on dependency treesShuang Liu, Xunqin Chen, Jiana Meng, Niko Lukač, 2025, original scientific article Abstract: 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.
Keywords: community detection algorithms, dependency trees, relation extraction
Published in DKUM: 17.01.2025; Views: 0; Downloads: 5
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