Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Sibilla : A Tool for Reasoning about Collective Systems
(Springer Science and Business Media Deutschland GmbH, 2022) Del Giudice, Nicola; Matteucci, Lorenzo; Quadrini, Michela; Rehman, Aniqa; Loreti, Michele; ter Beek, Maurice H.; Sirjani, Marjan; Department of Computer Science
Sibilla is a Java framework designed to support the analysis of Collective Adaptive Systems. These are systems composed by a large set of interactive agents that cooperate and compete to reach local and global goals. Sibilla is thought of container where different tools supporting specification and analysis of concurrent and distributed large scaled systems can be integrated. In this paper, a brief overview of Sibilla features is provided together with a simple example showing some of the tool’s practical capabilities.
Impossible ecologies: Interaction networks and stability of coexistence in ecological communities
(2023) Meng, Y.; Horvát, S.; Modes, C.D.; Haas, P.A.; Department of Computer Science
Community Detection in Directed Weighted Networks using Voronoi Partitioning
(2023) Molnár, Botond; Márton, Ildikó-Beáta; Horvát, Szabolcs; Ercsey-Ravasz, Mária; Department of Computer Science
Community detection is a ubiquitous problem in applied network analysis, yet efficient techniques do not yet exist for all types of network data. Most techniques have been developed for undirected graphs, and very few exist that handle directed and weighted networks effectively. Here we present such an algorithm based on Voronoi partitionings. As an added benefit, this method can directly employ edge weights that represent lengths, in contrast to algorithms that operate with connection strengths, requiring ad-hoc transformations of length data. We demonstrate the method on inter-areal brain connectivity, air transportation networks, as well as on randomly generated benchmark networks. The algorithm can handle dense graphs where weights are the main factor determining communities. The hierarchical structure of networks can also be detected, as shown for the brain. Its time efficiency is comparable with other state-of-the-art algorithms, the most costly part being Dijkstra's shortest paths algorithm.
Iterative Learning Robust PD-SDRE Control for Active Transfemoral Prostheses
(Science and Technology Publications, Lda, 2025) Bavarsad, Anna; August, Elias; Gislason, Magnus Kjartan; Yang, Xin-She; Drogoul, Alexis; Wagner, Gerd; Department of Engineering
In this paper, we present a novel control strategy for active prosthetic legs. The approach uses an intelligent robust Proportional-Derivative State-Dependent Riccati Equation controller to reduce the use of biomechanical energy, enhance performance and robustness. We include an Iterative Learning Control algorithm, to minimise control errors and allow the controller gains to adapt over time, and robust Sliding Mode Control to specifically address potential parametric and non-parametric uncertainties, disturbances, and noise. We conduct tests to demonstrate that the proposed controller not only maintains stability but also outperforms existing methods in terms of energy efficiency and tracking. Application of the proposed method in simulations shows significant improvements when compared to other methods from the literature, with up to 98.3% reduction in position tracking error and up to 91.9% reduction in control cost. Furthermore, for angular tracking of the hip and knee, improvements of up to 32.6% and 44.9%, along with torque reductions of up to 67.5% and 87.5%, are observed. This study represents a step forward in providing an effective solution for controlling active prosthetic devices.
Iterative Learning Robust PD-SDRE Control for Active Transfemoral Prostheses
(Science and Technology Publications, Lda, 2025) Bavarsad, Anna; August, Elias; Gislason, Magnus Kjartan; Yang, Xin-She; Drogoul, Alexis; Wagner, Gerd; Department of Engineering
In this paper, we present a novel control strategy for active prosthetic legs. The approach uses an intelligent robust Proportional-Derivative State-Dependent Riccati Equation controller to reduce the use of biomechanical energy, enhance performance and robustness. We include an Iterative Learning Control algorithm, to minimise control errors and allow the controller gains to adapt over time, and robust Sliding Mode Control to specifically address potential parametric and non-parametric uncertainties, disturbances, and noise. We conduct tests to demonstrate that the proposed controller not only maintains stability but also outperforms existing methods in terms of energy efficiency and tracking. Application of the proposed method in simulations shows significant improvements when compared to other methods from the literature, with up to 98.3% reduction in position tracking error and up to 91.9% reduction in control cost. Furthermore, for angular tracking of the hip and knee, improvements of up to 32.6% and 44.9%, along with torque reductions of up to 67.5% and 87.5%, are observed. This study represents a step forward in providing an effective solution for controlling active prosthetic devices.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts