Performance Analysis of Optimization Based FOC and DTC Methods for Three Phase Induction Motor

Three-phase induction motors are becoming increasingly utilized in industrialfield due to their better efficiency and simple manufacture.The speed control of an induction motor is essential in a variety of applications,but it is dif-ficult to control.This research analyses the three-phase induction motor’s perfor-mance usingfield-oriented control(FOC)and direct torque control(DTC)techniques.The major aim of this work is to provide a critical evaluation of devel-oping a simple speed controller for induction motors with improving the perfor-mance of Induction Motor(IM).For controlling a motor,different optimization approaches are accessible;in this research,a Fuzzy Logic Controller(FLC)with Fractional Order Darwinian Particle Swarm Optimization(FODPSO)algorithm is presented to control the induction motor.The FOC and DTC are controlled using FODPSO,and their performance is compared to the traditional FOC and DTC technique.Each scheme had its own simulation model,and the results were com-pared using hardware experimental and MATLAB-Simulink.In terms of time domain specifications and torque improvement,the proposed technique surpasses the existing method.

SAR target detection based on the optimal fractional Gabor spectrum feature

In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)into two dimensions,the fractional time-frequency spectrum feature of an image can be obtained.In the achievement process,we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT.Finally,the energy attenuation gradient(EAG)feature of the optimal time-frequency spectrum is extracted for high-frequency detection.The simulation results show the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

Local Discontinuous Galerkin Methods with Novel Basis for Fractional Diffusion Equations with Non-smooth Solutions

In this paper,we develop novel local discontinuous Galerkin(LDG)methods for fractional diffusion equations with non-smooth solutions.We consider such problems,for which the solutions are not smooth at boundary,and therefore the traditional LDG methods with piecewise polynomial solutions suffer accuracy degeneracy.The novel LDG methods utilize a solution information enriched basis,simulate the problem on a paired special mesh,and achieve optimal order of accuracy.We analyze the L2 stability and optimal error estimate in L2-norm.Finally,numerical examples are presented for validating the theoretical conclusions.

Fractional Order Darwinian Pigeon-Inspired Optimization for Multi-UAV Swarm Controller

This paper presents a novel multiple unmanned aerial vehicde(UAV)swarm cotoller based on the fractional alculus theory.This controller i designed baed on fractional order Darwinian pigeon-inepired optimization(F 0DPI0)and PID algorithm.Several comparative simulations are conducted in the paper.The simulation results reveal that FODPIObased muli-UAV formation controller is superior to the basic PIO and dilTerential evolution(DE)method.The fractional oelfcdent in F ODPIO algorithm makes it eflective optimbation with fast convergence rate,small oversboot,and better stability.Therefore,the contnoller propoeed in this paper is fessible and robust.

A LOW ORDER NONCONFORMING MIXED FINITE ELEMENT METHOD FOR NON-STATIONARY INCOMPRESSIBLE MAGNETOHYDRODYNAMICS SYSTEM

A low order nonconforming mixed finite element method(FEM)is established for the fully coupled non-stationary incompressible magnetohydrodynamics(MHD)problem in a bounded domain in 3D.The lowest order finite elements on tetrahedra or hexahedra are chosen to approximate the pressure,the velocity field and the magnetic field,in which the hydrodynamic unknowns are approximated by inf-sup stable finite element pairs and the magnetic field by H^(1)(Ω)-conforming finite elements,respectively.The existence and uniqueness of the approximate solutions are shown.Optimal order error estimates of L^(2)(H^(1))-norm for the velocity field,L^(2)(L^(2))-norm for the pressure and the broken L^(2)(H^(1))-norm for the magnetic field are derived.

Centralized Decisions for a Two-Stage Supply Chain with Price-Discount Dependent Demand

In this paper,we study a centralized supply chain for a two-stage with selling price discount.This supply chain consists of a supplier and a retailer. Based on the feature that the product’s selling season is short and the supply chain faces great demand uncertainty. We consider a two-stage scenario where,at the beginning of stage 1,the supplier reserves production capacity based on historic data in advance,stage 2 comes to us after some leadtime,both the supplier and the retailer update the demand information,the retailer then places an order not exceeding the reserved capacity based on the selling-pricing discount dependent demand. We make optimal decisions on the reserved capacity in stage 1,selling price discount and order quantity in stage 2. In this supply chain,the pattern in stage2 is figured out first,and then stage 1 is cleared as well. Then we present a numerical example to give some insights. Finally we get some conclusions.

A Mixed Regularization Method for Ill-Posed Problems

In this paper we propose a mixed regularization method for ill-posed prob-lems.This method combines iterative regularization methods and continuous regular-ization methods effectively.First it applies iterative regularization methods in which there is no continuous regularization parameter to solve the normal equation of the ill-posed problem.Then continuous regularization methods are applied to solve its residual problem.The presented mixed regularization algorithm is a general framework.Any iterative regularization method and continuous regularization method can be combined together to construct a mixed regularization method.Our theoretical analysis shows that the new mixed regularization method is with optimal order of error estimation and can reach the optimal order under a much wider range of the regularization parameter than the continuous regularization method such as Tikhobov regularization.Moreover,the new mixed regularization method can reduce the sensitivity of the regularization parameter and improve the solution of continuous regularization methods or iterative regularization methods.This advantage is helpful when the optimal regularization pa-rameter is hard to choose.The numerical computations illustrate the effectiveness of our new mixed regularization method.

Monitoring and Limiting Deceptive Counterfeiting:a Two-Stage Model

Over past decades,deceptive counterfeits which cannot be recognized by ordinary consumers when purchasing,such as counterfeit cosmetics,have posed serious threats on consumers’health and safety,and resulted in huge economic loss and inestimable brand damages to the genuine goods at the same time.Thus,how to effectively control and eliminate deceptive counterfeits in the market has become a critical problem to the local government.One of the principal challenges in combating the cheating action for the government is how to enhance the enforcement of relative quality inspection agencies like industrial administration office(IAO).In this paper,we formulate a two-stage counterfeit product model with a fixed checking rate from IAO and a penalty for holding counterfeits.Tominimize the total expected cost over two stages,the retailer adopts optimal ordering policies which are correlated with the checking rate and penalty.Under certain circumstances,we find that the optimal expected cost function for the retailer is first-order continuous and convex.The optimal ordering policy in stage two depends closely on the inventory level after the first sales period.When the checking rate in stage one falls into a certain range,the optimal ordering policy for the retailer at each stage is to order both kinds of products.Knowing the retailer’s optimal ordering policy at each stage,IAO can modify the checking rate accordingly to keep the ratio of deceptive counterfeits on the market under a certain level.

Numerical analysis of history-dependent variational-hemivariational inequalities

In this paper,numerical analysis is carried out for a class of history-dependent variationalhemivariational inequalities by arising in contact problems.Three different numerical treatments for temporal discretization are proposed to approximate the continuous model.Fixed-point iteration algorithms are employed to implement the implicit scheme and the convergence is proved with a convergence rate independent of the time step-size and mesh grid-size.A special temporal discretization is introduced for the history-dependent operator,leading to numerical schemes for which the unique solvability and error bounds for the temporally discrete systems can be proved without any restriction on the time step-size.As for spatial approximation,the finite element method is applied and an optimal order error estimate for the linear element solutions is provided under appropriate regularity assumptions.Numerical examples are presented to illustrate the theoretical results.

Optimized Pump Power Ratio on 2nd Order Pumping Discrete Raman Amplifier

By optimizing pump power ratio between 1st order backward pump and 2nd order forward pump on discrete Raman amplifier, we demonstrated over 2dB noise figure improvement without excessive non-linearity degradation.

MIXED DISCONTINUOUS GALERKIN METHOD FOR QUASI-NEWTONIAN STOKES FLOWS

In this paper,we introduce and analyze an augmented mixed discontinuous Galerkin(MDG)method for a class of quasi-Newtonian Stokes flows.In the mixed formulation,the unknowns are strain rate,stress and velocity,which are approximated by a discontinuous piecewise polynomial triplet ■for k≥0.Here,the discontinuous piecewise polynomial function spaces for the field of strain rate and the stress field are designed to be symmetric.In addition,the pressure is easily recovered through simple postprocessing.For the benefit of the analysis,we enrich the MDG scheme with the constitutive equation relating the stress and the strain rate,so that the well-posedness of the augmented formulation is obtained by a nonlinear functional analysis.For k≥0,we get the optimal convergence order for the stress in broken ■(div)-norm and velocity in L^(2)-norm.Furthermore,the error estimates of the strain rate and the stress in-norm,and the pressure in L^(2)-norm are optimal under certain conditions.Finally,several numerical examples are given to show the performance of the augmented MDG method and verify the theoretical results.Numerical evidence is provided to show that the orders of convergence are sharp.