prikaz prve stranice dokumenta Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data
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Scientific paper - Original scientific paper
Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data
Journal of Marine Science and Engineering, 9 (2021), 4; 439. https://doi.org/10.3390/jmse9040439
Lopac, Nikola; Jurdana, Irena; Lerga, Jonatan; Wakabayashi, Nobukazu

Institutional repository: Repository of the University of Rijeka, Faculty of Maritime Studies - FMSRI Repository

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Lopac, N., Jurdana, I., Lerga, J. & Wakabayashi, N. (2021). Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data. Journal of Marine Science and Engineering, 9. (4), 439. doi: 10.3390/jmse9040439

Lopac, Nikola, et al. "Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data." Journal of Marine Science and Engineering, vol. 9, no. 4, 2021, pp. 439. https://doi.org/10.3390/jmse9040439

Lopac, Nikola, Irena Jurdana, Jonatan Lerga and Nobukazu Wakabayashi. "Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data." Journal of Marine Science and Engineering 9, no. 4 (2021): 439. https://doi.org/10.3390/jmse9040439

Lopac, N., et al. (2021) 'Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data', Journal of Marine Science and Engineering, 9(4), pp. 439. doi: 10.3390/jmse9040439

Lopac N, Jurdana I, Lerga J, Wakabayashi N. Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data. Journal of Marine Science and Engineering [Internet]. 2021 April 18 [cited 2024 December 29];9(4):439. doi: 10.3390/jmse9040439

N. Lopac, I. Jurdana, J. Lerga and N. Wakabayashi, "Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data", Journal of Marine Science and Engineering, vol. 9, no. 4, pp. 439, April 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:187:840262. [Accessed: 29 December 2024]

Metadata
Title (english)Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data
AuthorNikola Lopac
AuthorIrena Jurdana
AuthorJonatan Lerga
AuthorNobukazu Wakabayashi
Author's institutionUniversity of Rijeka
Faculty of Maritime Studies
Author's institutionUniversity of Rijeka
Faculty of Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Abstract (english)
The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleJournal of Marine Science and Engineering
Numberingvol. 9, no. 4, p. 439, 439
e-ISSN2077-1312
DOIhttps://doi.org/10.3390/jmse9040439
URN:NBNurn:nbn:hr:187:840262
Publication2021-04-18
Document URLhttps://www.mdpi.com/2077-1312/9/4/439
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-12-23 08:27:29