Pregled bibliografske jedinice broj: 1255690
Sensor characterization for supporting de-noising and auto-calibration of sensor data
Sensor characterization for supporting de-noising and auto-calibration of sensor data // 2022 International Joint Conference on Neural Networks (IJCNN)
Padova, Italija: Institute of Electrical and Electronics Engineers (IEEE), 2022. 2325, 7 doi:10.1109/ijcnn55064.2022.9892967 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1255690 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Sensor characterization for supporting de-noising
and auto-calibration of sensor data
Autori
Van Hauwermeiren, Wout ; Hou, Yuanbo ; Filipan, Karlo ; Botteldooren, Dick
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2022 International Joint Conference on Neural Networks (IJCNN)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2022
Skup
2022 International Joint Conference on Neural Networks (IJCNN 2022)
Mjesto i datum
Padova, Italija, 18.07.2022. - 23.07.2022
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
mobile sensors ; road quality ; representation learning ; pooling ; set2set
Sažetak
In earlier work, a mobile sensor network had been deployed in passenger cars to measure road quality using sound and vibration sensors. A self- calibration and confounder removal (SCCR) algorithm eliminated the effect of the measurement device in sound and vibration measurements. It relied on the hypothesis that two measurements of the same object should give the same result. However, once the deep learning model has been trained, the model cannot easily include more devices, because it relies on a one-hot encoded device identification. Therefore, additional characterization of both sensor and vehicle is proposed here using a batch of observations (a bag) from the same sensors. A set-encoder is trained with a device classification head. This set-encoder compresses the input bag into a latent representation, irrespective of the input order. This latent vector, which characterizes the sensor, is injected into the SCCR framework to steer the calibration. New devices are characterized by the pre-trained set-encoder. This relies on the hypothesis that closely related sensors with a similar response should be close to each other in the latent space. Therefore, SCCR is now possible with new devices, previously unseen at the training time of SCCR. However, an increase in reconstruction error is observed for the unseen devices.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
