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Pregled bibliografske jedinice broj: 1168374

Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning


Lopac, Nikola; Hržić, Franko; Petrijevčanin Vuksanović, Irena; Lerga, Jonatan
Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning // IEEE access, 10 (2021), 2408-2428 doi:10.1109/ACCESS.2021.3139850 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1168374 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning

Autori
Lopac, Nikola ; Hržić, Franko ; Petrijevčanin Vuksanović, Irena ; Lerga, Jonatan

Izvornik
IEEE access (2169-3536) 10 (2021); 2408-2428

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Non-stationary signals ; noisy signals ; time-frequency signal analysis ; convolutional neural networks ; deep learning ; gravitational waves

Sažetak
Analysis of non-stationary signals in a noisy environment is a challenging research topic in many fields often requiring simultaneous signal decomposition in the time and frequency domain. This paper proposes a method for the classification of noisy non- stationary time- series signals based on Cohen’s class of their time- frequency representations (TFRs) and deep learning algorithms. We demonstrated the proposed approach on the example of detecting gravitational-wave (GW) signals in intensive real- life, non- stationary, non-white, and non-Gaussian noise. For this purpose, we prepared a dataset based on the actual data from the Laser Interferometer Gravitational-Wave Observatory (LIGO) detector and the synthetic GW signals obtained by realistic simulations. Next, 12 different TFRs from Cohen’s class were calculated from the original noisy time-series data and used to train three state-of- the-art convolutional neural network (CNN) architectures: ResNet-101, Xception, and EfficientNet. The obtained classification results are compared to those achieved by the base model trained on the original time series. Analysis of the results suggests that the proposed approach combining deep CNN architectures with Cohen’s class TFRs yields high values of performance metrics and significantly improves the classification performance compared to the base model. The TFR-CNN models achieve the values of the classification accuracy of up to 97.10%, the area under the receiver operating characteristic curve (ROC AUC) of up to 0.9885, the recall of up to 95.87%, the precision of up to 99.51%, the F1 score of up to 97.03%, and the area under the precision-recall curve (PR AUC) of up to 0.9920. This classification performance is obtained on the dataset in which the signal- to- noise ratio (SNR) values of the raw, noisy time- series signals range from -123.46 to -2.27 dB. Therefore, this study suggests that using alternative TFRs of Cohen’s class can improve the deep learning-based detection of non-stationary GW signals in an intensive noise environment. Moreover, the proposed approach can also be a viable solution for deep learning-based analysis of numerous other noisy non-stationary signals in different practical applications.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
IP-2018-01-3739 - Sustav potpore odlučivanju za zeleniju i sigurniju plovidbu brodova (DESSERT) (Prpić-Oršić, Jasna, HRZZ - 2018-01) ( CroRIS)
MINGO-ESIF-KK.01.2.1.02.0179 - ABsistemDCiCloud (ABsistemDCiCloud) (Lerga, Jonatan, MINGO - Fond: Europski fond za regionalni razvoj Program: OP Konkurentnost i kohezija 2014. - 2020. Jačanje gospodarstva primjenom istraživanja i inovacija Područje: IRI - Povećanje razvoja novih proizvoda i usluga koji proizlaze iz aktivnosti istraživanja i raz) ( CroRIS)
VLASTITA-SREDSTVA-uniri-tehnic-17 - Računalom potpomognuta digitalna analiza i klasifikacija signala (UNIRI-TEHNIC-18-17) (Lerga, Jonatan, VLASTITA-SREDSTVA - UNIRI2018) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-15 - Razvoj postupaka temeljenih na strojnom učenju za prepoznavanje bolesti i ozljeda iz medicinskih slika (Štajduhar, Ivan, NadSve ) ( CroRIS)
COST-CA17137 - Mreža za gravitacijske valove, geofiziku i strojno učenje (G2NET) (COST ) ( CroRIS)

Ustanove:
Tehnički fakultet, Rijeka,
Pomorski fakultet, Rijeka

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Lopac, Nikola; Hržić, Franko; Petrijevčanin Vuksanović, Irena; Lerga, Jonatan
Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning // IEEE access, 10 (2021), 2408-2428 doi:10.1109/ACCESS.2021.3139850 (međunarodna recenzija, članak, znanstveni)
Lopac, N., Hržić, F., Petrijevčanin Vuksanović, I. & Lerga, J. (2021) Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning. IEEE access, 10, 2408-2428 doi:10.1109/ACCESS.2021.3139850.
@article{article, author = {Lopac, Nikola and Hr\v{z}i\'{c}, Franko and Petrijev\v{c}anin Vuksanovi\'{c}, Irena and Lerga, Jonatan}, year = {2021}, pages = {2408-2428}, DOI = {10.1109/ACCESS.2021.3139850}, keywords = {Non-stationary signals, noisy signals, time-frequency signal analysis, convolutional neural networks, deep learning, gravitational waves}, journal = {IEEE access}, doi = {10.1109/ACCESS.2021.3139850}, volume = {10}, issn = {2169-3536}, title = {Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning}, keyword = {Non-stationary signals, noisy signals, time-frequency signal analysis, convolutional neural networks, deep learning, gravitational waves} }
@article{article, author = {Lopac, Nikola and Hr\v{z}i\'{c}, Franko and Petrijev\v{c}anin Vuksanovi\'{c}, Irena and Lerga, Jonatan}, year = {2021}, pages = {2408-2428}, DOI = {10.1109/ACCESS.2021.3139850}, keywords = {Non-stationary signals, noisy signals, time-frequency signal analysis, convolutional neural networks, deep learning, gravitational waves}, journal = {IEEE access}, doi = {10.1109/ACCESS.2021.3139850}, volume = {10}, issn = {2169-3536}, title = {Detection of Non-Stationary GW Signals in High Noise from Cohen’s Class of Time-Frequency Representations Using Deep Learning}, keyword = {Non-stationary signals, noisy signals, time-frequency signal analysis, convolutional neural networks, deep learning, gravitational waves} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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