Special functions for the extended calibration of charge-mode accelerometers

Tomczyk, Krzysztof; Piekarczyk, Marcin; Sieja, Marek; Sokal, Grzegorz

doi:10.1016/j.precisioneng.2021.02.002

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Recently, deep learning models, based on various variants of neural networks, such as CNN [ 26 ], RNN [ 27 ] or GNN [ 28 ], are also experiencing a renaissance. Depending on the area and specificity of applications, very complex approaches are used, often utilising integration and combination of many techniques, which is particularly visible in case of interdisciplinary problems occurring, for example, in such selected fields as medicine [ 29 , 30 ], education [ 31 , 32 ], metrology [ 33 , 34 , 35 ], biometrics [ 36 , 37 ], learning of motor activities [ 38 , 39 ] or gesture recognition [ 40 , 41 ].…”

Section: Ann-based Methods To Remove Artefactsmentioning

confidence: 99%

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Piekarczyk

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Bibrzycki

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Sensors

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Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

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