11. Sensitivity analysis of RF+clust for leave-one-problem-out performance predictionAna Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning Published in DiRROS: 13.11.2023; Views: 567; Downloads: 360 Full text (4,94 MB) This document has many files! More... |
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13. Models for forecasting the traffic flow within the city of LjubljanaGašper Petelin, Rok Hribar, Gregor Papa, 2023, original scientific article Abstract: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data. Keywords: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison Published in DiRROS: 28.09.2023; Views: 564; Downloads: 258 Full text (5,05 MB) This document has many files! More... |
14. Hierarchical learning of robotic contact policiesMihael Simonič, Aleš Ude, Bojan Nemec, 2023, original scientific article Abstract: The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly. Keywords: autonomous robot learning, learning, experience, compliance and impedance contro Published in DiRROS: 21.09.2023; Views: 618; Downloads: 346 Full text (1,73 MB) This document has many files! More... |
15. Algorithm instance footprint : separating easily solvable and challenging problem instancesAna Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: black-box optimization, algorithms, problem instances, machine learning Published in DiRROS: 15.09.2023; Views: 496; Downloads: 292 Full text (2,03 MB) This document has many files! More... |
16. Assessing the generalizability of a performance predictive modelAna Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: algorithms, predictive models, machine learning Published in DiRROS: 15.09.2023; Views: 515; Downloads: 338 Full text (935,67 KB) This document has many files! More... |
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18. DynamoRep : trajectory-based population dynamics for classification of black-box optimization problemsGjorgjina Cenikj, Gašper Petelin, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: black-box single-objective optimization, optimization problem classification, problem representation, meta-learning Published in DiRROS: 30.08.2023; Views: 524; Downloads: 346 Full text (650,13 KB) This document has many files! More... |
19. Optimal sensor set for decoding motor imagery from EEGArnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control. Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning Published in DiRROS: 03.04.2023; Views: 650; Downloads: 301 Full text (670,67 KB) This document has many files! More... |
20. Having fun while learning, SKI EASY Snow day : 1st SKI EASY multiplier sport event, Pamporovo, Bulgaria, 14-16 March 2022Saša Pišot, 2022, other component parts Keywords: skiing, training, learning, projects, Snow day Published in DiRROS: 03.03.2023; Views: 569; Downloads: 280 Full text (742,79 KB) This document has many files! More... |