Temat: outlier correction - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Comparison of methods for correcting outliers in ECG-based biometric identification
Autorzy:: Jun, Su
Szmajda, Miroslaw
Khoma, Volodymyr
Khoma, Yuriy
Sabodashko, Dmytro
Kochan, Orest
Wang, Jinfei
Powiązania:: https://bibliotekanauki.pl/articles/221531.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Euclidean distance
autoencoders
outlier correction
ECG signal
human identification
biometrics
Opis:: The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.
Źródło:: Metrology and Measurement Systems; 2020, 27, 3; 387-398
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Outlier detection in ocean wave measurements by using unsupervised data mining methods
Autorzy:: Mahmoodi, K.
Ghassemi, H.
Powiązania:: https://bibliotekanauki.pl/articles/260330.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: ocean wave data
data mining
outlier detection
data correction
Opis:: Outliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unknown conditions such as tsunami, windstorm and etc. To improve the accuracy and reliability of an built ocean wave model, or to extract important and valuable information from collected wave data, detecting of outlying observations in wave measurements is very important. In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave height (Hs) records. The historical wave data are taken from National Data Buoy Center (NDBC). Finally, those data points are considered as outlier identified by at least two methods which are presented and discussed. Then, Hs prediction has been modelled with and without the presence of outliers by using Regression trees (RTs).
Źródło:: Polish Maritime Research; 2018, 1; 44-50
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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