Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series.
Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series.
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DOI：
10.3390/s21061954
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发表时间：
2021-03-10
期刊：
影响因子：
--
通讯作者：
Dodson J
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文献类型：
--
作者：
Barzegar V;Laflamme S;Hu C;Dodson J
经历高速动态事件的系统称为高速系统，通常会在不到 10 毫秒的时间内经历高于 100 克力的加速度。例子包括自适应安全气囊展开系统、高超音速飞行器和主动爆炸缓解系统。鉴于其关键功能，准确、快速的建模工具对于确保目标性能是必要的。然而，这些系统的独特特征，包括（1）外部载荷的巨大不确定性，（2）高度的非平稳性和严重的扰动，以及（3）系统配置变化产生的未建模动态，与快速变化的环境相结合，限制了物理建模工具的适用性。在本文中，深度学习算法用于对高速系统进行建模并预测其响应测量结果。它由一组同时训练的短序列长短期记忆 (LSTM) 细胞组成。为了实现多步提前预测，多速率采样器被设计为基于使用嵌入定理提取的局部动态单独选择每个 LSTM 单元的输入空间。所提出的算法在从高速系统获得的实验数据上进行了验证。结果表明，与启发式方法相比，使用多速率采样器可以更好地从非平稳时间序列中提取特征，从而显着提高预测精度和范围。该算法精简高效的架构导致平均计算时间为 25 秒，低于最大预测范围，因此证明了该算法在实时高速率应用中的前景。
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 s, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications.
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影响因子：
5.4
作者：
Hong, Jonathan;Laflamme, Simon;Dodson, Jacob
通讯作者：
Dodson, Jacob
DOI：
10.3390/s18010217
发表时间：
2018-01-13
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
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通讯作者：
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影响因子：
11.2
作者：
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通讯作者：
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DOI：
10.1016/j.trc.2020.102674
发表时间：
2020-09-01
影响因子：
8.3
作者：
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