scholarly journals Estimation of the Dynamic Moduli of Viscoelastic Asphalt Mixtures Using the Extended Kalman Filter Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Yu-Seok Gong ◽  
Dowan Kim ◽  
Sungho Mun
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Master Curves ◽  
Dynamic Moduli ◽  
Frequency Sweep ◽  
Sigmoidal Curve ◽  
Linear Viscoelastic ◽  
Kalman Filter Algorithm ◽  
Impact Resonance ◽  
Mean Square Errors

Here, we develop a model predicting the dynamic moduli of hot-mix asphalt/concrete using the extended Kalman filter (EKF) algorithm and draw frequency-domain master curves. Discrete dynamic moduli were obtained via impact resonance tests (IRTs) on linear viscoelastic (LVE) asphalt at 20, 30, 35, 40, and 50°C. Typically, viscoelastic characteristics have been used to derive asphalt dynamic moduli; compressive frequency sweep tests at different frequencies (Hz) and temperatures are employed to this end. We compared IRT-derived viscoelastic master curves obtained via compressive frequency sweep testing to those derived using the EKF algorithm, which employs a nonlinear sigmoidal curve and a Taylor series to explore the viscoelastic function. The model reduced errors at both low and high frequencies by correcting the coefficients of the master curve. Furthermore, the predictive model effectively estimated dynamic moduli at various frequencies, and also root-mean-square errors (RMSEs) which, together with the mean percentage errors (MPEs), were used to compare predictions.

The p53–Mdm2 regulation relationship under different radiation doses based on the continuous–discrete extended Kalman filter algorithm

2018 ◽  
Vol 273 ◽  
pp. 230-236 ◽  
Author(s):  
Yurong Li ◽  
Jun Chen ◽  
Li Jiang ◽  
Nianyin Zeng ◽  
Haiyan Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Radiation Doses ◽  
Kalman Filter Algorithm

scholarly journals Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6829
Author(s):  
Luke Wicent F. Sy ◽  
Nigel H. Lovell ◽  
Stephen J. Redmond
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lie Group ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Ultra Wideband ◽  
Distance Measurements ◽  
Limb Kinematics ◽  
Mean Square Errors ◽  
Lower Limb Kinematics

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

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