Loading...
Please wait, while we are loading the content...
Similar Documents
Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series
| Content Provider | MDPI |
|---|---|
| Author | Barzegar, Vahid Laflamme, Simon Hu, Chao Dodson, Jacob |
| Copyright Year | 2021 |
| Description | Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 |
| Starting Page | 1954 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21061954 |
| Journal | Sensors |
| Issue Number | 6 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-10 |
| Access Restriction | Open |
| Subject Keyword | Sensors Transportation Science and Technology Sensor Measurement Deep Learning Nonlinear Recurrent Neural Networks Long Short-term Memory Non-stationary Time Series High-rate Prediction |
| Content Type | Text |
| Resource Type | Article |
