Embedded nanometer position tracking based on enhanced phasor analysis: erratum

2021 ◽  
Author(s):  
Hongqiang Ma ◽  
Yang Liu
Keyword(s):  
Position Tracking ◽  
Phasor Analysis

Sliding Mode Control of an Actuated Knee Joint Orthosis

2021 ◽  
Vol 15 ◽  
Author(s):  
Imen Saidi ◽  
Asma Hammami
Keyword(s):  
Sliding Mode Control ◽  
Lower Limb ◽  
Sliding Mode ◽  
Position Tracking ◽  
Sliding Mode Controller ◽  
Mechanical Device ◽  
Tracking Errors ◽  
Mode Control ◽  
Speed Tracking ◽  
Human Morphology

Introduction: In this paper, a robust sliding mode controller is developed to control an orthosis used for rehabilitation of lower limb. Materials and Methods: The orthosis is defined as a mechanical device intended to physically assist a human subject for the realization of his movements. It should be adapted to the human morphology, interacting in harmony with its movements, and providing the necessary efforts along the limbs to which it is attached. Results: The application of the sliding mode control to the Shank-orthosis system shows satisfactory dynamic response and tracking performances. Conclusion: In fact, position tracking and speed tracking errors are very small. The sliding mode controller effectively absorbs disturbance and parametric variations, hence the efficiency and robustness of our applied control.

scholarly journals Performance of displayed-marker-based position tracking

2018 ◽  
Vol 12 (7) ◽  
pp. JAMDSM0125-JAMDSM0125
Author(s):  
Akio YAMAMOTO ◽  
Taku NAKAMURA
Keyword(s):  
Position Tracking

scholarly journals Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christian Crouzet ◽  
Gwangjin Jeong ◽  
Rachel H. Chae ◽  
Krystal T. LoPresti ◽  
Cody E. Dunn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prussian Blue ◽  
Processing Speed ◽  
Digital Pathology ◽  
Ground Truth ◽  
Individual Variability ◽  
Rgb Images ◽  
Cerebral Microhemorrhages ◽  
Phasor Analysis ◽  
Better Than

AbstractCerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

scholarly journals Active Disturbance Rejection Control for Position Tracking of Electro-Hydraulic Servo Systems under Modeling Uncertainty and External Load

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 20
Author(s):  
Manh Hung Nguyen ◽  
Hoang Vu Dao ◽  
Kyoung Kwan Ahn
Keyword(s):  
Angular Velocity ◽  
Disturbance Rejection ◽  
Lyapunov Theory ◽  
Tracking Performance ◽  
System Stability ◽  
Position Tracking ◽  
Parametric Uncertainties ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, an active disturbance rejection control is designed to improve the position tracking performance of an electro-hydraulic actuation system in the presence of parametric uncertainties, non-parametric uncertainties, and external disturbances as well. The disturbance observers (Dos) are proposed to estimate not only the matched lumped uncertainties but also mismatched disturbance. Without the velocity measurement, the unmeasurable angular velocity is robustly calculated based on the high-order Levant’s exact differentiator. These disturbances and angular velocity are integrated into the control design system based on the backstepping framework which guarantees high-accuracy tracking performance. The system stability analysis is analyzed by using the Lyapunov theory. Simulations based on an electro-hydraulic rotary actuator are conducted to verify the effectiveness of the proposed control method.

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