Unsupervised Time Series Pattern Recognition for Purpose of Electronic Surveillance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144806" target="_blank" >RIV/00216305:26220/22:PU144806 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G43__/23:00558842

Výsledek na webu

<a href="https://mrw2022.org/mikon/" target="_blank" >https://mrw2022.org/mikon/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/MIKON54314.2022.9924999" target="_blank" >10.23919/MIKON54314.2022.9924999</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Time Series Pattern Recognition for Purpose of Electronic Surveillance

Popis výsledku v původním jazyce

Signal classification is one of the main tasks of electronic surveillance. This paper focuses on extracting patterns from time series and testing robustness of pre-trained Neural Network (NN). A dataset of 10 different time series was created and used to train a neural network based on the Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC) model. The logits layer of the NN model was removed from this pre-trained model to obtain the feature vectors. A dataset containing 87 real signals acquired from passive surveillance sensors was passed to the NN to obtain embeddings that represent the features of the signals extracted from the NN. The dataset was then corrupted with missing pulses and spurious pulses and tested on pre-trained NN. This unsupervised learning method was able to recognize 76% of the signals even with 50% of the missing input data. The research showed that an important step to improve NN performance is to choose suitable data scaling method. The best results were achieved using the StandardScaler from scikit-learn preprocessing library.

Název v anglickém jazyce

Unsupervised Time Series Pattern Recognition for Purpose of Electronic Surveillance

Popis výsledku anglicky

Signal classification is one of the main tasks of electronic surveillance. This paper focuses on extracting patterns from time series and testing robustness of pre-trained Neural Network (NN). A dataset of 10 different time series was created and used to train a neural network based on the Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC) model. The logits layer of the NN model was removed from this pre-trained model to obtain the feature vectors. A dataset containing 87 real signals acquired from passive surveillance sensors was passed to the NN to obtain embeddings that represent the features of the signals extracted from the NN. The dataset was then corrupted with missing pulses and spurious pulses and tested on pre-trained NN. This unsupervised learning method was able to recognize 76% of the signals even with 50% of the missing input data. The research showed that an important step to improve NN performance is to choose suitable data scaling method. The best results were achieved using the StandardScaler from scikit-learn preprocessing library.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of MIKON 2022

ISBN

978-83-956020-3-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Polsko

Místo konání akce

Gdansk

Datum konání akce

12. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—