scholarly journals AR-sieve Bootstrap for High-dimensional Time Series

2021 ◽  
Author(s):  
Daning Bi ◽  
Han Lin Shang ◽  
Yanrong Yang ◽  
Huanjun Zhu
Keyword(s):  
Time Series ◽  
High Dimensional ◽  
Sieve Bootstrap

Weak Signal Detection in Underwater Acoustic Based on Time-Series Characteristic

2011 ◽  
Vol 216 ◽  
pp. 548-552
Author(s):  
Hai Ping Wu ◽  
Shi Jian Zhu ◽  
Jing Jun Lou ◽  
Li Yang Yu
Keyword(s):  
Time Series ◽  
Signal Detection ◽  
Matched Filter ◽  
Doppler Frequency ◽  
High Dimensional ◽  
Filter Method ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Underwater Acoustic ◽  
Radar Echo

For limitation of the matched filter method in underwater acoustic detection,a method of underwater acoustic weak signal detection based on time series characteristic quantity is proposed．Chaotic waveforms, which have thumbtack type ambiguity function, is selected as the waveform of active sonar in the situation of High Dynamic Doppler Frequency Shift. According to the change of correlation dimension while chaotic radar echo appears in the chaotic background, chaotic radar echo is checked out by the means of simulation in the situation of high dimensional chaotic background and low dimensional chaotic background．The method proves out in high dimensional chaotic background．

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

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