Abstract
It is often hard to relate the sensor's electrical output to the physical scenario when a multidimensional measurement is of interest. An artificial neural network may be a solution. Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources. The same issue arises when the physical measurement is expected to extend across a wide range of values. This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network. Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest. We discuss to which systems our method may be applied. We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array. We present the experimental process, its results, and the resulting accuracies.
Original language | English |
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Article number | 9254315 |
Journal | Journal of Sensors |
Volume | 2019 |
DOIs | |
State | Published - 1 Jan 2019 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Instrumentation
- Electrical and Electronic Engineering