Reliable Convergence Research Articles

RG-MADS를 적용한 매입형 영구자석 동기전동기의 최적설계

Newly proposed RG-MADS (Randomly Guided Mesh Adaptive Direct Search) has been applied to the optimal design of Interior Permanent Magnet Synchronous Motor (IPMSM) which has the distinctive features of magnetic saturation. RG-MADS, advanced from classical MADS algorithm, has the superiority in computational time and reliable convergence accuracy to the optimal solution, thus it is appropriate to the optimal design of IPMSM coupled with time-consuming Finite Element Analysis (FEA), necessary to the nonlinear magnetic application for better accuracy. Effectiveness of RG-MADS has been verified through the well-known benchmark-functions beforehand. In addition, the proposed RG-MADS has been applied to the optimal design of IPMSM aiming at maximizing the Maximum Torque Per Ampere (MTPA), which is regarded as representative design goal of IPMSM.

The Study on the Shuffled Frog Leap Algorithm and Its Application

In this paper a SFLA has been developed for optimal placement and size of DER considering loss minimization and voltage profile improvement as objectives. Different sizes of DERs are considered for different cases studied. In all cases the SFLA algorithm shows reliable convergence, exhibits improved voltage profiles, and reduced real and reactive power losses. Further, in comparison with the Particle Swarm Optimization (PSO) the SFLA algorithm suggests better optimal locations of DERs leading to further reduction in losses.

Nonsymmetric Black Box multigrid with coarsening by three

SUMMARYThe classical Petrov–Galerkin approach to Black Box multigrid for nonsymmetric problems due to Dendy is combined with the recent factor‐three‐coarsening Black Box algorithm due to Dendy and Moulton, along with a powerful symmetric line Gauss–Seidel smoother, resulting in an efficient and robust multigrid solver. Focusing on the convection–diffusion operator, the algorithm is tested and shown to achieve fast and reliable convergence with both first‐order and second‐order accurate upstream discretizations of the convection operator. The solver also exhibits robust behavior with respect to discontinuous jumps in the diffusion coefficient and performs well for recirculating flows over a wide range of diffusion coefficients. The efficiency of the solver is supported by results of an analysis for the case of constant coefficients. Copyright © 2012 John Wiley & Sons, Ltd.

P-1060 - The study of factorial structure, validity and reliability of the penn state worry questionnaire (PSWQ)

Background Penn State Worry Questionnaire (PSWQ) is an appropriate instrument for assessing of worry. Worry is the cognitive component of anxiety. It has a positive correlation with severity of the anxiety disorders. Preparing PWSQ and its psychometric index was necessary for Iranian students. Aim The aim of this study was to assess factorial structures, validity and reliability of Pennsylvania State Worry Questionnaire. Method The English version of the questionnaire was translated into Persian with the standard procedures done for translating Psychological tests. 272 second grade high school students were selected by cluster sampling from different educational regions of City of Tehran. They completed PSWQ and principal components procedure by the Varimax rotation used for data analysis. Results Exploratory factor analysis showed that PSWQ is a one-dimensional instrument for worry. Validity according to internal consistency coefficients was 0.83. Correlation with metacognition 30 question questionnaire was 0.63 indicating convergence reliability. Correlation with the Spiel Berger Anxiety scale was 0.67, with trait anxiety scale 0.70, with state anxiety scale 0.58. This finding shows that the predictive reliability of the Questionnaire is acceptable. Conclusion According to the findings of this study PSWQ have good Psychometrics properties in Iranian population and can be used as a valid measurement in Psychological research.

Performance index and meta-optimization of a direct search optimization method

Design optimization is becoming an increasingly important tool for design, often using simulation as part of the evaluation of the objective function. A measure of the efficiency of an optimization algorithm is of great importance when comparing methods. The main contribution of this article is the introduction of a singular performance criterion, the entropy rate index based on Shannon's information theory, taking both reliability and rate of convergence into account. It can also be used to characterize the difficulty of different optimization problems. Such a performance criterion can also be used for optimization of the optimization algorithms itself. In this article the Complex-RF optimization method is described and its performance evaluated and optimized using the established performance criterion. Finally, in order to be able to predict the resources needed for optimization an objective function temperament factor is defined that indicates the degree of difficulty of the objective function.

On the distributed Lagrange multiplier/fictitious domain method for rigid‐particle‐laden flows: a proposition for an alternative formulation of the Lagrange multipliers

SUMMARYThe distributed Lagrange multiplier/fictitious domain method proposed for the direct numerical simulation of particle‐laden flows is considered in this work. First, it is demonstrated that improved accuracy is obtained with a coupled numerical scheme, whereby the pressure and the Lagrange multiplier fields enforcing incompressibility and rigid body motion, respectively, are calculated and applied together. However, the convergence characteristics of the iterative solution of the coupled scheme are poor because symmetric but indefinite and poorly conditioned matrices are produced. An analysis is then presented, which suggests that the cause for the matrix pathologies lies in the interaction of the respective matrix operators enforcing incompressibility and rigid body motion. On the basis of this analysis, an alternative formulation is developed for the Lagrange multipliers, being now composed of a set of forces distributed only on the particle boundary together with a set of couples distributed within the particle core. The new formulation is tested with several types of flows with stationary or moving particles under creeping or finite Reynolds number conditions and it is demonstrated that it produces correct results and better conditioned matrices, thus enabling faster and more reliable convergence of the conjugate gradient method. The analysis and tests, therefore, support the expectation that the proposed formulation is promising and worthy of further study and improvement. Copyright © 2011 John Wiley & Sons, Ltd.

Variable Mesh Adaptive Direct Search Algorithm Applied for Optimal Design of Electric Machines Based on FEA

Optimal design of electric machine based on finite element analysis (FEA) is characterized with the nonlinear magnetic saturation, which causes the much longer computation time for maintaining the high accuracy. In order to compensate the excessive computation time due to FEA and guarantee the reliable convergence to global optimum, variable mesh adaptive direct search (VMADS) is newly implemented with multi-start in this paper. Effectiveness of VMADS has been verified through a benchmark function. In particular, the proposed algorithm has been employed to optimal design of interior permanent magnet synchronous machine (IPMSM) for maximum torque per ampere (MTPA), of which design objective should be evaluated by FEA for better accuracy.

Optimal Segmentation of Brain Tumors using DRLSE Levelset

segmentation plays a vital role in medical image processing and computer vision. In case of medical scan images geometric level set functions perform accurate segmentation in good no of cases but develops irregularities during concave region evolution. These irregularities cause numerical errors and eventually destroy the stability of the evolution. In this paper, a new variational formulation known as distance regularization has a unique forwardandbackward (FAB) diffusion e ffect is used for the analysis of medical brain image scans which perform accurate segmentation in case of concavities. This method also eliminates the need of the costly rein itialization procedure. This method shows reliable and good convergence to the object boundaries with speed in case of concavities.

Novel Fourier-domain constraint for fast phase retrieval in coherent diffraction imaging

Coherent diffraction imaging (CDI) for visualizing objects at atomic resolution has been realized as a promising tool for imaging single molecules. Drawbacks of CDI are associated with the difficulty of the numerical phase retrieval from experimental diffraction patterns; a fact which stimulated search for better numerical methods and alternative experimental techniques. Common phase retrieval methods are based on iterative procedures which propagate the complex-valued wave between object and detector plane. Constraints in both, the object and the detector plane are applied. While the constraint in the detector plane employed in most phase retrieval methods requires the amplitude of the complex wave to be equal to the squared root of the measured intensity, we propose a novel Fourier-domain constraint, based on an analogy to holography. Our method allows achieving a low-resolution reconstruction already in the first step followed by a high-resolution reconstruction after further steps. In comparison to conventional schemes this Fourier-domain constraint results in a fast and reliable convergence of the iterative reconstruction process.

Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints

In the current research chaotic search is used with the optimization technique for solving non-linear complicated power system problems because Chaos can overcome the local optima problem of optimization technique. Power system problem, more specifically voltage stability, is one of the practical examples of non-linear, complex, convex problems. Smart grid, restructured energy system and socio-economic development fetch various uncertain events in power systems and the level of uncertainty increases to a great extent day by day. In this context, analysis of voltage stability is essential. The efficient method to assess the voltage stability is maximum loadability limit (MLL). MLL problem is formulated as a maximization problem considering practical security constraints (SCs). Detection of weak buses is also important for the analysis of power system stability. Both MLL and weak buses are identified by PSO methods and FACTS devices can be applied to the detected weak buses for the improvement of stability. Three particle swarm optimization (PSO) techniques namely General PSO (GPSO), Adaptive PSO (APSO) and Chaotic PSO (CPSO) are presented for the comparative study with obtaining MLL and weak buses under different SCs. In APSO method, PSO-parameters are made adaptive with the problem and chaos is incorporated in CPSO method to obtain reliable convergence and better performances. All three methods are applied on standard IEEE 14 bus, 30 bus, 57 bus and 118 bus test systems to show their comparative computing effectiveness and optimization efficiencies.

Identification of 5-Axis Machine Tools Feed Drive Systems for Contouring Simulation

An identification technique is introduced for identifying closed loop transfer function of machine tool’s feed drive systems to be used in simulation of the tracking and contouring performance of Computer Numerical Controlled (CNC) machine tools. The identification is performed from air-cutting tests utilizing only standard G-codes containing linear motion commands. A general transfer function model is derived for representing the closed loop tracking response of the feed drive system. The model considers the drive to be controlled by commonly used controller schemes such as P-PI Cascade, PID or the Sliding Mode Controller (SMC) with feed-forward dynamic and friction compensation. The parameters of the model transfer function are fitted tominimize the discrepancy between the actual and predicted axis position on the axis. In order to guarantee the stability of the identified model transfer function, bounds on the pole locations are imposed. The resultant constrained non-linear optimization problem is solved efficiently using the Particle Swarm Optimization (PSO) method. For achieving reliable convergence of the stochastic PSO algorithm, a parameter tuning strategy is presented. Simulation and experimental studies show that the identified feed drive model captures the fundamental dynamics of the drives system accurately for simulating their closed loop response. Combined with the kinematics of the machine, contouring errors of 5-axis CNC machine tools during simultaneous multi-axis motion are predicted.

Intelligent Memetic Algorithm Using GA and Guided MADS for the Optimal Design of Interior PM Synchronous Machine

Optimal design of an electric machine based on finite element analysis (FEA) calls for much longer computation time for maintaining high accuracy. In order to compensate for the excessive computation time and guarantee the reliable convergence to a global optimum, an intelligent memetic algorithm is newly implemented by combining a genetic algorithm (GA) and the guided mesh adaptive direct search (MADS) that employs an extension search step after the poll step. The effectiveness of guided MADS (GMADS) alone has been verified through the function optimization, and the proposed memetic algorithm is applied to an optimal design of an interior permanent magnet synchronous machine (IPMSM), of which the cost function has many local minima. Optimization results confirm that the proposed method locates an acceptable solution more effectively maintaining the reliable accuracy.

Robust acceleration of self consistent field calculations for density functional theory

We show that the type 2 Broyden secant method is a robust general purpose mixer for self consistent field problems in density functional theory. The Broyden method gives reliable convergence for a large class of problems and parameter choices. We directly mix the approximation of the electronic density to provide a basis independent mixing scheme. In particular, we show that a single set of parameters can be chosen that give good results for a large range of problems. We also introduce a spin transformation to simplify treatment of spin polarized problems. The spin transformation allows us to treat these systems with the same formalism as regular fixed point iterations.

AMGA2: improving the performance of the archive-based micro-genetic algorithm for multi-objective optimization

In this article, an improved Archive-based Micro Genetic Algorithm (referred to as AMGA2) for constrained multi-objective optimization is proposed. AMGA2 is designed to obtain fast and reliable convergence on a wide variety of optimization problems. AMGA2 benefits from the existing literature in that it borrows and improves upon several concepts from existing multi-objective optimization algorithms. Improvements and modifications to the existing diversity assessment techniques and genetic variation operators are also proposed. AMGA2 employs a new kind of selection strategy that attempts to reduce the probability of exploring less desirable search regions. The proposed AMGA2 is a steady-state genetic algorithm that maintains an external archive of best and diverse solutions and a very small working population. AMGA2 has been designed to facilitate the decoupling of the working population, the external archive, and the number of solutions desired as the outcome of the optimization process. Comprehensive benchmarking and comparison of AMGA2 with the current state-of-the-art multi-objective optimization algorithms demonstrate its improved search capability.

Classification-based self-adaptive differential evolution with fast and reliable convergence performance

To avoid the problems of slow and premature convergence of the differential evolution (DE) algorithm, this paper presents a new DE variant named p-ADE. It improves the convergence performance by implementing a new mutation strategy “DE/rand-to-best/pbest”, together with a classification mechanism, and controlling the parameters in a dynamic adaptive manner, where the “DE/rand-to-best/pbest” utilizes the current best solution together with the best previous solution of each individual to guide the search direction. The classification mechanism helps to balance the exploration and exploitation of individuals with different fitness characteristics, thus improving the convergence rate. Dynamic self-adaptation is beneficial for controlling the extent of variation for each individual. Also, it avoids the requirement for prior knowledge about parameter settings. Experimental results confirm the superiority of p-ADE over several existing DE variants as well as other significant evolutionary optimizers.

A B-Spline Based Semiparametric Nonlinear Mixed Effects Model

The Semiparametric Nonlinear Mixed Effects Model (SNMM) (Ke and Wang 2001) provides a flexible framework for longitudinal comparisons of curve shapes between groups. In this article, we develop an alternative method for fitting the SNMM by reformulating Ke and Wang’s smoothing spline based model in terms of B-splines. The existing algorithm is based on a backfitting procedure that iterates between two mixed models whose corresponding likelihoods are not equivalent to the likelihood of all model parameters. The consequence is a lack of reliable convergence and statistical inference. Using B-splines, however, overcomes these disadvantages by simplifying the likelihood computations without sacrificing model flexibility. Therefore, the algorithm can be expressed in terms of existing, accurate techniques based on Adaptive Gaussian Quadrature. The model is applied to labor curves, cervical dilation measured longitudinally, from women attempting a vaginal birth after cesarean. Only partial curves were measured on cases of uterine rupture given the need for emergency c-section while controls completed delivery naturally. The model allowed us to estimate and compare the average labor curve shape between cases and controls and also determine the earliest time at which clinicians could distinguish between the average labor curves in different groups. Supplemental materials are available online.

Modeling of ammonia conversion rate in ammonia synthesis based on a hybrid algorithm and least squares support vector regression

AbstractIn ammonia synthesis production, the ammonia conversion rate reflects how well the synthesis proceeds. In this paper, a model, which characterizes the relationship between operational variables and ammonia conversion rate, is established using least squares support vector regression (LSSVR). A hybrid algorithm of particle swarm optimization and differential evolution (HPSODE) is proposed to identify the hyperparameters of LSSVR, i.e. the regulation parameter and the width of the kernel function. HPSODE is first tested through benchmark functions and the performance is evaluated with traditional particle swarm optimization (PSO), differential evolution (DE), and a hybrid particle swarm optimization with differential evolution operator (DEPSO). It is then applied to the modeling of ammonia synthesis process. Results using other modeling methods [back propagation neural network (BPNN), LSSVR, PSO–LSSVR, and DE–LSSVR] are presented for comparison purpose. The proposed HPSODE–LSSVR modeling shows good feasibility of the algorithm and reliability of global convergence. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

Reliable acoustic path and convergence zone bottom interaction in the Philippine Sea 09 Experiment.

One of the objectives of the Philippine Sea 09 (PhilSea09) was to examine the multipath structure of RAP propagation at the seafloor. For a source near the surface, a receiver at the seafloor, and ranges approaching one-half a CZ, one expects a direct path, seafloor reflected and refracted paths plus water column multiples. These were measured using the bottom elements of the SIO deep vertical line array. Similarly, for both a source and a receiver at the surface and at CZ ranges, these same paths propagate back to the surface. These were also measured with the PSU FORA (five octave research array). The experiment was part of an NPAL Group effort. [Work supported by ONR Ocean Acoustics.]

Stochastic Error Compensation Method for RDOA Based Target Localization in Sensor Network

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models

Summary Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semiparametric generalized linear models (GLMs) use iterative REML or ML estimation of the smoothing parameters of working linear approximations to the GLM. Such indirect schemes need not converge and fail to do so in a non-negligible proportion of practical analyses. By contrast, very reliable prediction error criteria smoothing parameter selection methods are available, based on direct optimization of GCV, or related criteria, for the GLM itself. Since such methods directly optimize properly defined functions of the smoothing parameters, they have much more reliable convergence properties. The paper develops the first such method for REML or ML estimation of smoothing parameters. A Laplace approximation is used to obtain an approximate REML or ML for any GLM, which is suitable for efficient direct optimization. This REML or ML criterion requires that Newton–Raphson iteration, rather than Fisher scoring, be used for GLM fitting, and a computationally stable approach to this is proposed. The REML or ML criterion itself is optimized by a Newton method, with the derivatives required obtained by a mixture of implicit differentiation and direct methods. The method will cope with numerical rank deficiency in the fitted model and in fact provides a slight improvement in numerical robustness on the earlier method of Wood for prediction error criteria based smoothness selection. Simulation results suggest that the new REML and ML methods offer some improvement in mean-square error performance relative to GCV or Akaike’s information criterion in most cases, without the small number of severe undersmoothing failures to which Akaike’s information criterion and GCV are prone. This is achieved at the same computational cost as GCV or Akaike’s information criterion. The new approach also eliminates the convergence failures of previous REML- or ML-based approaches for penalized GLMs and usually has lower computational cost than these alternatives. Example applications are presented in adaptive smoothing, scalar on function regression and generalized additive model selection.