scholarly journals A Nonlinear Circuit Analysis Technique for Time-Variant Inductor Systems

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2321 ◽  
Author(s):  
Xinning Wang ◽  
Chong Li ◽  
Dalei Song ◽  
Robert Dean
Keyword(s):  
Closed Form ◽  
Eddy Currents ◽  
High Efficiency ◽  
Nonlinear Differential Equations ◽  
Closed Form Solution ◽  
Form Solution ◽  
Circuit Analysis ◽  
Nonlinear Method ◽  
Sensors And Actuators ◽  
Computation Efficiency

Time-variant inductors exist in many industrial applications, including sensors and actuators. In some applications, this characteristic can be deleterious, for example, resulting in inductive loss through eddy currents in motors designed for high efficiency operation. Therefore, it is important to investigate the electrical dynamics of systems with time-variant inductors. However, circuit analysis with time-variant inductors is nonlinear, resulting in difficulties in obtaining a closed form solution. Typical numerical algorithms used to solve the nonlinear differential equations are time consuming and require powerful processors. This investigation proposes a nonlinear method to analyze a system model consisting of the time-variant inductor with a constraint that the circuit is powered by DC sources and the derivative of the inductor is known. In this method, the Norton equivalent circuit with the time-variant inductor is realized first. Then, an iterative solution using a small signal theorem is employed to obtain an approximate closed form solution. As a case study, a variable inductor, with a time-variant part stimulated by a sinusoidal mechanical excitation, is analyzed using this approach. Compared to conventional nonlinear differential equation solvers, this proposed solution shows both improved computation efficiency and numerical robustness. The results demonstrate that the proposed analysis method can achieve high accuracy.

Improved Parker-Sochacki Approach for Closed Form Solution of Enzyme Catalyzed Reaction Model

2017 ◽  
Vol 8 (1-2) ◽  
pp. 90
Author(s):  
Babatunde Sunday Ogundare ◽  
Saheed O Akindeinde ◽  
Adebayo O Adewumi ◽  
Adebayo A Aderogba
Keyword(s):  
Closed Form ◽  
Series Solution ◽  
Nonlinear Differential Equations ◽  
Closed Form Solution ◽  
Processing Technique ◽  
Reaction Model ◽  
Form Solution ◽  
Post Processing ◽  
Analytical Technique ◽  
Comparison Of The Results

In this article, a new analytical technique called Improved Parker-Sochacki Method (IPSM) for solving nonlinear Michaelis-Menten enzyme catalyzed reaction model is proposed. The global form of the solution for the concentrations of the substrate, enzyme and the enyzme-free product are obtained. Employing the Laplace-Pade resummation as a post processing technique on the computed series solution, the domain of convergence of the solution is greatly extended. The solution is therefore devoid of limited convergence interval that is typical of series solution of nonlinear differential equations.  The proposed method showed a significant improvement  over the conventional Parker-Sochacki Method (PSM). Furthermore, comparison of the results with numerically computed solutions elucidated the simplicity and accuracy of the proposed method.

Registration of a hybrid robot using the Degradation-Kronecker method and a purely nonlinear method

Robotica ◽  
2015 ◽  
Vol 34 (12) ◽  
pp. 2729-2740 ◽  
Author(s):  
S. J. Yan ◽  
S. K. Ong ◽  
A. Y. C. Nee
Keyword(s):  
Closed Form ◽  
Real Time ◽  
Good Accuracy ◽  
Tracking System ◽  
Closed Form Solution ◽  
Form Solution ◽  
Fast Computation ◽  
Nonlinear Method ◽  
Simulation And Experiment ◽  
Limited Accuracy

SUMMARYAlthough the registration of a robot is crucial in order to identify its pose with respect to a tracking system, there is no reported solution to address this issue for a hybrid robot. Different from classical registration, the registration of a hybrid robot requires the need to solve an equation with three unknowns where two of these unknowns are coupled together. This property makes it difficult to obtain a closed-form solution. This paper is a first attempt to solve the registration of a hybrid robot. The Degradation-Kronecker (D-K) method is proposed as an optimal closed-form solution for the registration of a hybrid robot in this paper. Since closed-form methods generally suffer from limited accuracy, a purely nonlinear (PN) method is proposed to complement the D-K method. With simulation and experiment results, it has been found that both methods are robust. The PN method is more accurate but slower as compared to the D-K method. The fast computation property of the D-K method makes it appropriate to be applied in real-time circumstances, while the PN method is suitable to be applied where good accuracy is preferred.

scholarly journals An Efficient Algorithm for Overcomplete Sparsifying Transform Learning with Signal Denoising

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Beiping Hou ◽  
Zhihui Zhu ◽  
Gang Li ◽  
Aihua Yu
Keyword(s):  
Closed Form ◽  
Efficient Algorithm ◽  
Synthetic Data ◽  
Closed Form Solution ◽  
Form Solution ◽  
Signal Denoising ◽  
Alternating Minimization ◽  
Computation Complexity ◽  
Learning Problem ◽  
Computation Efficiency

This paper deals with the problem of overcomplete transform learning. An alternating minimization based procedure is proposed for solving the formulated sparsifying transform learning problem. A closed-form solution is derived for the minimization involved in transform update stage. Compared with existing ones, our proposed algorithm significantly reduces the computation complexity. Experiments and simulations are carried out with synthetic data and real images to demonstrate the superiority of the proposed approach in terms of the averaged representation and denoising errors, the percentage of successful and meaningful recovery of the analysis dictionary, and, more significantly, the computation efficiency.

On a Closed-Form Solution of Prandtl's System of Equations

2013 ◽  
Vol 40 (2) ◽  
pp. 106-114
Author(s):  
J. Venetis ◽  
Aimilios (Preferred name Emilios) Sideridis
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Form Solution ◽  
System Of Equations

scholarly journals A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 10 (7) ◽  
pp. 435
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Simulated Data ◽  
Real Data ◽  
Point Clouds ◽  
Form Solution ◽  
Spatial Transformation ◽  
Dual Quaternions ◽  
Feature Based ◽  
Planar Feature

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

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