Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor Based Dynamic State Estimation

A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

Neurocomputing ◽

10.1016/j.neucom.2009.12.002 ◽

2010 ◽

Vol 73 (7-9) ◽

pp. 1376-1383 ◽

Cited By ~ 10

Author(s):

Xueli Wu ◽

Jianhua Zhang ◽

Quanmin Zhu

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Dynamic Systems ◽

Nonlinear Dynamic ◽

Nonlinear Dynamic Systems ◽

Time Varying ◽

Network Identification ◽

And Control

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Learning Flatness-Based Controller Using Neural Network

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-8998 ◽

2019 ◽

Author(s):

Hailin Ren ◽

Jingyuan Qi ◽

Pinhas Ben-Tzvi

Keyword(s):

Neural Network ◽

Dynamic Systems ◽

Systems And Control ◽

And Control

Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046776/1082074/Learning-Flatness-Based-Controller-Using-Neural

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Dynamic Systems and Control

Hydraulic Control Systems ◽

10.1002/9781119418528.ch3 ◽

2019 ◽

pp. 81-154

Keyword(s):

Dynamic Systems ◽

Systems And Control ◽

And Control

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Dynamic state estimation of power system with stochastic delay based on neural network

Energy Reports ◽

10.1016/j.egyr.2021.02.009 ◽

2021 ◽

Vol 7 ◽

pp. 159-166

Author(s):

Guangdou Zhang ◽

Jian Li ◽

Dongsheng Cai ◽

Qi Huang ◽

Weihao Hu

Keyword(s):

Neural Network ◽

Power System ◽

State Estimation ◽

Dynamic State ◽

Stochastic Delay ◽

Dynamic State Estimation

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Stacked Convolutional Bidirectional LSTM Recurrent Neural Network for Bearing Anomaly Detection in Rotating Machinery Diagnostics

2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII) ◽

10.1109/ickii.2018.8569065 ◽

2018 ◽

Cited By ~ 2

Author(s):

Kwangsuk Lee ◽

Jae-Kyeong Kim ◽

Jaehyong Kim ◽

Kyeon Hur ◽

Hagbae Kim

Keyword(s):

Neural Network ◽

Anomaly Detection ◽

Recurrent Neural Network ◽

Rotating Machinery ◽

Bidirectional Lstm ◽

Machinery Diagnostics

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Dynamic Systems and Control

10.1007/978-3-030-76124-0_8 ◽

2021 ◽

pp. 403-475

Author(s):

Kostas Triantafyllopoulos

Keyword(s):

Dynamic Systems ◽

Systems And Control ◽

And Control

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Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor-Based Dynamic State Estimation

ASME Letters in Dynamic Systems and Control ◽

10.1115/1.4046685 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Ritika Sibal ◽

Ding Zhang ◽

Julie Rocho-Levine ◽

K. Alex Shorter ◽

Kira Barton

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

State Estimation ◽

Short Term Memory ◽

Hidden Markov ◽

Training Data ◽

Dynamic State ◽

Support Vector ◽

Similar Amount ◽

Dynamic State Estimation

Abstract Behavior of animals living in the wild is often studied using visual observations made by trained experts. However, these observations tend to be used to classify behavior during discrete time periods and become more difficult when used to monitor multiple individuals for days or weeks. In this work, we present automatic tools to enable efficient behavior and dynamic state estimation/classification from data collected with animal borne bio-logging tags, without the need for statistical feature engineering. A combined framework of an long short-term memory (LSTM) network and a hidden Markov model (HMM) was developed to exploit sequential temporal information in raw motion data at two levels: within and between windows. Taking a moving window data segmentation approach, LSTM estimates the dynamic state corresponding to each window by parsing the contiguous raw data points within the window. HMM then links all of the individual window estimations and further improves the overall estimation. A case study with bottlenose dolphins was conducted to demonstrate the approach. The combined LSTM–HMM method achieved a 6% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily adaptable to new tasks.

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Hierarchical Nonlinear Moving Horizon Estimation of Vehicle Lateral Speed and Road Friction Coefficient

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-9069 ◽

2019 ◽

Author(s):

Chi Jin ◽

Anson Maitland ◽

John McPhee

Keyword(s):

Friction Coefficient ◽

Dynamic Systems ◽

Moving Horizon Estimation ◽

Systems And Control ◽

Road Friction ◽

And Control ◽

Road Friction Coefficient

Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046395/1074688/Hierarchical-Nonlinear-Moving-Horizon-Estimation

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Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-9201 ◽

2019 ◽

Author(s):

Michael T. Benson ◽

Harish Sathishchandra ◽

Garrett M. Clayton ◽

Sean B. Andersson

Keyword(s):

Compressive Sensing ◽

Dynamic Systems ◽

Lidar Data ◽

Systems And Control ◽

And Control

Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046574/1075674/Compressive-Sensing-Based-Reconstruction-of

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Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-9178 ◽

2019 ◽

Author(s):

Jihun Han ◽

Dominik Karbowski ◽

Namdoo Kim ◽

Aymeric Rousseau

Keyword(s):

Dynamic Systems ◽

Optimal Solutions ◽

Human Driver ◽

Systems And Control ◽

And Control

Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046575/1075675/Human-Driver-Modeling-Based-on-Analytical-Optimal

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