Analyzing Periodic Motion Classification

2003 ◽  
pp. 673-680
Author(s):  
Xavier Orriols ◽  
Xavier Binefa
Keyword(s):  
Periodic Motion ◽  
Motion Classification

scholarly journals Feature Engineering for Motion Classification in Machine Vision

2021 ◽  
Vol 2115 (1) ◽  
pp. 012043
Author(s):  
Soumya Shaw ◽  
Susan Elias ◽  
Sudha Velusamy
Keyword(s):  
Periodic Motion ◽  
Motion Path ◽  
Power Requirement ◽  
Motion Classification ◽  
Average Accuracy ◽  
Starting Point ◽  
Mathematical Equations ◽  
Technological Platform ◽  
Execution Order ◽  
Motion Types

Abstract With the most advanced classification algorithms in the technological platform, the computational power requirement is on the surge. The paper hereby presents computationally trivial algorithms to simplify the process of computational intensive classifications techniques, especially in the Motion Classification arena. The proposed methods prove crucial in acting as a lightweight and computationally fast stepping stone to a fundamentally more significant application of Motion indexing and classification, Action recognition, and predictive analysis of motion energy. The algorithms classify the motions into linear, circular, or periodic motion types by following an appropriate execution order. They consider the tracked motion path of the object of interest as a sequence and use it as a starting point to perform all operations, resulting in a feature that can be classified into separate classes. Using a single parameter for classifying the motion engenders a faster and relatively more straightforward route to motion identification and elicits the algorithm’s uniqueness. Two algorithms are proposed, namely, Angle Derivative Technique and Determinant Method for classifying the motion into two classes (linear & circular). On the other hand, a different algorithm identifies periodic motion using the principle of correlation on the motion sequences. All the algorithms show an average accuracy of over 95%. It also elicited an average processing time of 15.6 ms and 19.86 ms for Angle Derivative Method and Determinant Method, respectively, and 31.2 ms for periodic motion on Intel(R) Core(TM) i3-5005U CPU @ 2.00 GHz and 8GB RAM. A dataset of camera-captured videos consisting of three motion types is used for testing while the proposed methods are trained on a dataset of motion described by mathematical equations with added 3σ noise levels.

scholarly journals Noise Reduction of Learning Control for Periodic Motion of Galvanometer Scanner

2020 ◽  
Vol 53 (2) ◽  
pp. 8401-8406
Author(s):  
Shingo Ito ◽  
Han Woong Yoo ◽  
Georg Schitter
Keyword(s):  
Noise Reduction ◽  
Periodic Motion ◽  
Learning Control ◽  
Galvanometer Scanner

scholarly journals Viscous Flow Around a Rigid Body Performing a Time-periodic Motion

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Thomas Eiter ◽  
Mads Kyed
Keyword(s):  
Rigid Body ◽  
Sobolev Spaces ◽  
Periodic Motion ◽  
Viscous Incompressible Fluid ◽  
Body Motion ◽  
Translational Velocity ◽  
Ill Posed ◽  
Homogeneous Sobolev Spaces ◽  
The Mean ◽  
Time Periodic

AbstractThe equations governing the flow of a viscous incompressible fluid around a rigid body that performs a prescribed time-periodic motion with constant axes of translation and rotation are investigated. Under the assumption that the period and the angular velocity of the prescribed rigid-body motion are compatible, and that the mean translational velocity is non-zero, existence of a time-periodic solution is established. The proof is based on an appropriate linearization, which is examined within a setting of absolutely convergent Fourier series. Since the corresponding resolvent problem is ill-posed in classical Sobolev spaces, a linear theory is developed in a framework of homogeneous Sobolev spaces.

scholarly journals Posture Dynamic Modeling and Stability Analysis of a Magnetic Driven Dual-Spin Spherical Capsule Robot

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 238
Author(s):  
Huiyuan Yang ◽  
Yongshun Zhang ◽  
Zhenhu Liu ◽  
Xu Liu ◽  
Guanxi Liu
Keyword(s):  
Angular Velocity ◽  
Magnetic Induction ◽  
Periodic Motion ◽  
Lyapunov Theory ◽  
Asymptotically Stable ◽  
Magnetic Torque ◽  
Stable Motion ◽  
Capsule Robot ◽  
Magnetic Induction Intensity ◽  
Spherical Capsule

In order to realize the intervention operation in the unstructured and ample environments such as stomach and colon, a dual-spin spherical capsule robot (DSCR) driven by pure magnetic torque generated by the universal rotating magnetic field (URMF) is proposed. The coupled magnetic torque, the viscoelastic friction torque, and the gravity torque were analyzed. Furthermore, the posture dynamic model describing the electric-magnetic-mechanical-liquid coupling dynamic behavior of the DSCR in the gastrointestinal (GI) tract was established. This model is a second-order periodic variable coefficient dynamics equation, which should be regarded as an extension of the Lagrange case for the dual-spin body system under the fixed-point motion, since the external torques were applied. Based on the Floquet–Lyapunov theory, the stability domain of the DSCR for the asymptotically stable motion and periodic motion were obtained by investigating the influence of the angular velocity of the URMF, the magnetic induction intensity, and the centroid deviation. Research results show that the DSCR can realize three kinds of motion, which are asymptotically stable motion, periodic motion, and chaotic motion, according to the distribution of the system characteristic multipliers. Moreover, the posture stability of the DSCR can be improved by increasing the angular velocity of the URMF and reducing the magnetic induction intensity.

Periodic motion planning for virtually constrained Euler–Lagrange systems

2006 ◽  
Vol 55 (11) ◽  
pp. 900-907 ◽  
Author(s):  
A. Shiriaev ◽  
A. Robertsson ◽  
J. Perram ◽  
A. Sandberg
Keyword(s):  
Motion Planning ◽  
Periodic Motion
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