TY - GEN
T1 - Methodology for an Offline Outlier Detection in a Dynamic Measurement Data Set
AU - Aksu, Osman
AU - Eisenmann, Bastian
AU - Schmid, Michael
AU - Bierwirth, Florian
AU - Radosavac, Mišel
AU - Herzog, Hans Georg
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The data acquisition process translates physical magnitudes into discrete, sampled data sets. Theoretically, this process executes undisturbed and without delay. But especially if accurate statistic distributions or extremal values are desired, outliers caused by physical errors in the measurement process falsify the result. Conventional approaches have difficulties with detecting outliers in dynamic and distorted data sets. This work shows two methods to classify and reliably improve these aspects. The first approach involves multiplying two factors to yield a quality factor determining outliers. In contrast, the second approach determines outlier status by identifying specific points through the addition of multiple factors, rather than by multiplying two factors. The combination of both methods leads to a more effective detection process, offering a time-efficient and robust technique for pinpointing outlying sampling points in transient measurement data sets.
AB - The data acquisition process translates physical magnitudes into discrete, sampled data sets. Theoretically, this process executes undisturbed and without delay. But especially if accurate statistic distributions or extremal values are desired, outliers caused by physical errors in the measurement process falsify the result. Conventional approaches have difficulties with detecting outliers in dynamic and distorted data sets. This work shows two methods to classify and reliably improve these aspects. The first approach involves multiplying two factors to yield a quality factor determining outliers. In contrast, the second approach determines outlier status by identifying specific points through the addition of multiple factors, rather than by multiplying two factors. The combination of both methods leads to a more effective detection process, offering a time-efficient and robust technique for pinpointing outlying sampling points in transient measurement data sets.
KW - digital filter
KW - distorted data
KW - dynamic measurement data
KW - outlier detection
KW - outlier filtering
UR - http://www.scopus.com/inward/record.url?scp=85213032641&partnerID=8YFLogxK
U2 - 10.1109/VTC2024-Fall63153.2024.10758038
DO - 10.1109/VTC2024-Fall63153.2024.10758038
M3 - Conference contribution
AN - SCOPUS:85213032641
T3 - IEEE Vehicular Technology Conference
BT - 2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 100th IEEE Vehicular Technology Conference, VTC 2024-Fall
Y2 - 7 October 2024 through 10 October 2024
ER -