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
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
(2022-09-12) Apruzzese, Giovanni; Andreolini, Mauro; Ferretti, Luca; Marchetti, Mirco; Colajanni, Michele; Department of Computer Science
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios, because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this article can help hardening defensive systems by letting cyber defenders address the most critical and real issues and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.
Tunable oriented mid-infrared wave based on metasurface with phase change material of GST
(2022-03) Guo, Kai; Li, Xiaoyu; Ai, Huifang; Ding, Xiya; Wang, Lulu; Wang, Wei; Guo, Zhongyi; Department of Engineering
Dynamically controlling the mid-infrared electromagnetic waves with tunable metasurfaces remains a challenging task. Phase change material of Ge2Sb2Te5 (GST) has shown tremendous advantages in constructing reconfigurable metasurface. In this work, we design a metasurface with GST to dynamically focus and deflect the beam at wavelength of 8.5 μm with a fixed structure. The designed metasurface is composed of GST nanocylinders on a Calcium Fluoride (CaF2) substrate. The focusing and deflection characteristics are manipulated by artificially controlling the value of crystalline fraction of each GST cylinder. In addition, we studied the diffraction pattern at a distance of 2 m for oriented deflection and its broadband performance. This method improves the degree of freedom of active modulation and provides a new route for tunable optical communication devices.
Quad-port multiservice diversity antenna for automotive applications
(2021-12-01) Kannappan, Lekha; Palaniswamy, Sandeep Kumar; Wang, Lulu; Kanagasabai, Malathi; Kumar, Sachin; Alsath, Mohammed Gulam Nabi; Rao, Thipparaju Rama; Department of Engineering
A quad-element multiple-input-multiple-output (MIMO) antenna with ultra-wideband (UWB) performance is presented in this paper. The MIMO antenna consists of four orthogonally arranged microstrip line-fed hexagonal monopole radiators and a modified ground plane. In addition, E-shaped and G-shaped stubs are added to the radiator to achieve additional resonances at 1.5 GHz and 2.45 GHz. The reliability of the antenna in the automotive environment is investigated, with housing effects taken into account. The housing effects show that the antenna performs consistently even in the presence of a large metal object. The proposed MIMO antenna has potential for various automotive applications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), intelligent transport system (ITS), automatic vehicle identifier, and RFID-based electronic toll collection.
Multiplexed multi-focal and multi-dimensional SHE (spin Hall effect) metalens
(2021-12-20) Wang, Wei; Yang, Qingyuan; He, Shan; Shi, Yan; Liu, Xiangmin; Sun, Jinghua; Guo, Kai; Wang, Lulu; Guo, Zhongyi; Department of Engineering
Metalenses are two-dimensional ultrathin metalenses composed of subwavelength artificial microstructures. In this paper, various multi-focal spin Hall effect (SHE)-based metalenses are designed to provide spin-dependent splitting in transverse and longitudinal directions, which possess spin-dependent two focal points under left-circularly polarized (LCP) or right-circularly polarized (RCP) incidence, and all four focal points can be observed under the linearly polarized (LP) incidence. A spin-independent bifocal metalens was investigated, which possesses the same bifocal focusing phenomena for LCP and RCP incidences. Our method is significant for designing high-efficiency multifunctional optics devices based on optical SHE.
Real‐time high‐performance laser welding defect detection by combining acgan‐based data enhancement and multi‐model fusion
(2021-11-01) Fan, Kui; Peng, Peng; Zhou, Hongping; Wang, Lulu; Guo, Zhongyi; Department of Engineering
Most of the existing laser welding process monitoring technologies focus on the detection of post‐engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real‐time identification of defect detection has more important practical signif-icance. The data set of laser welding process is often difficult to build and there is not enough ex-perimental data, which hinder the applications of the data‐driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten‐class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN‐CNN and ACGAN‐SVM‐CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real‐time classification and recognition through data enhancement and multi‐model fusion.
Flokkar í Opnum vísindum
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- 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