Search query: Highly Efficient Multilevel Frequency Converters of Metallurgical Production Facilities

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abstract: A comparative analysis of traditional and multilevel low voltage frequency converters was performed. Calculation methods were proposed that make it possible to evaluate the technical and economic indices of multilevel semiconductor devices. The attractive qualities of low voltage multilevel frequency converters as compared with high voltage (with rated voltage exceeding 1 kV) multilevel semiconductor devices were distinguished. In low voltage multilevel converters, the loss in semiconductor rectifiers was reduced by about 1.5 times, the effect of overvoltage in the load was decreased, and the operating speed of the active rectifier was increased. A method of estimation of the loss in semiconductor converters with two level standalone inverters based on mathematical models and experimental studies is proposed, which made it possible to clarify the parameters of analytical equations. It is stated that using the three level bridge inverters (rectifiers) in active rectifiers makes it possible to shorten the durability of transient process of the system in the regeneration mode because of a reduced level of switching overvoltages. A method of choosing the optimal number of phases of semiconductor multilevel low voltage frequency converters for metallurgical production facilities according to the criterion of maximum probability of failure free operation was developed.
author_names: E S Chupin

M A Grigor'ev
title: Highly Efficient Multilevel Frequency Converters of Metallurgical Production Facilities
venue: Russian Electrical Engineering
year: 2019

abstract: A new method for analysing nonlinear and non stationary data has been developed. The key part of the method is the 'empirical mode decomposition' method with which any complicated data set can be decomposed into a finite and often small number of 'intrinsic mode functions' that admit well behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non stationary processes. With the Hilbert transform, the 'instrinic mode functions' yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy frequency time distribution, designated as the Hilbert spectrum. In this method, the main conceptual innovations are the introduction of 'intrinsic mode functions' based on local properties of the signal, which make the instantaneous frequency meaningful; and the introduction of the instantaneous frequencies for complicated data sets, which eliminate the need for spurious harmonics to represent nonlinear and non stationary signals. Examples from the numerical results of the classical nonlinear equation systems and data representing natural phenomena are given to demonstrate the power of this new method. Classical nonlinear system data are especially interesting, for they serve to illustrate the roles played by the nonlinear and non stationary effects in the energy frequency time distribution.
author_names: Norden E Huang

Zheng Shen

Steven R Long

Manli C Wu

Hsing H Shih

Quan Zheng

Nai-chyuan Yen

Chi Chao Tung

Henry H Liu
title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis
venue: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
year: 1998

abstract: We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6] and Hofmann's aspect model also known as probabilistic latent semantic indexing (pLSI) [3] In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.
author_names: David M Blei

A Ng

Michael I Jordan
title: Latent Dirichlet Allocation
venue: J. Mach. Learn. Res.
year: 2003

abstract: This chapter provides an overview of Analytic Hierarchy Process (AHP) which is a systematic procedure for representing the elements of any problem hierarchically. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pair wise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy. These judgments are then translated to numbers. The AHP includes procedures and principles used to synthesize the many judgments to derive priorities among criteria and subsequently for alternative solutions. It is useful to note that the numbers thus obtained are ratio scale estimates and correspond to so called hard numbers. Problem solving is a process of setting priorities in steps. One step decides on the most important elements of a problem, another on how best to repair, replace, test, and evaluate the elements, and another on how to implement the solution and measure performance.
author_names: Thomas L Saaty

Kevin P Kearns
title: The Analytic Hierarchy Process
year: 1985

abstract: 1. Introduction to Predictive Control. 2. A Standard Formulation of Predictive Control. 3. Solving Predictive Control Problems. 4. Step Response and Transfer Function Formulations. 5. Tuning. 6. Stability. 7. Robust Predictive Control. 8. Perspectives. 9. Case Studies. 10. The Model Predictive Control Toolbox. References Appendices A. Some Commercial MPC Products B. MATLAB Program basicmpc C. The MPC Toolbox D. Solutions to Problems
author_names: Jan M Maciejowski
title: Predictive control with constraints
year: 2002

abstract: Understand the need for analyses of large, complex, information rich data sets. Identify the goals and primary tasks of the data mining process. Describe the roots of data mining technology. Recognize the iterative character of a data mining process and specify its basic steps. Explain the influence of data quality on a data mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
author_names: S Gnanapriya

R Suganya

Gagan Devi

M Suresh Kumar
title: Data Mining Concepts and Techniques
year: 2010

abstract: Direct fabrication of large micropatterned single crystals. p1205 21 Feb 2003. (news) Academy plucks best biophysicists from a sea of mediocrity. p994 14 Feb 2003.
author_names: Aaas News

Eric Lu

Minmin Zhou

Rong Mocsai

Attila Myers

E Huang

B Jackson

Davide Ferrari

V Tybulewicz

Victor Lowell

Clifford A Lepore

J Koretzky

Gary Kahn

Mark L

Frederic Achard

Hugh Douglas Eva

Ernst-Detlef See Also Schulze

Jairaj K Acharya

Usha Acharya

Shetal Patel

E Koundakjian

Kunio Nagashima

Xianlin Han

Daniel L Adams

Jonathan C And Horton

Melissa D Adams

Mitch McVey

Jeff Sekelsky

John W Adamson

Gerd G Kochendoerfer

A W Adeleke

A See Kamdem-Toham

Alan Aderem

C Picard

Gerald H Haug

Girish S Agarwal

Marlan O Scully

Hugo Aguilaniu

Lena Gustafsson

Michel Rigoulet

Thomas Nystrom

Ferhaan Ahmad

Joachim P Schmitt

Misako Aida

Salai C Ammal

Joanna Aizenberg

David A Muller

John L Grazul

Don R Hamann

James W Ajioka

C J Su

Aravind B Akella

M S Alam

F Gao

Ahmet Alatas

Harald Sinn

Titus V Albu

Peter S Zuev

Maher Al-Dayeh

Joseph R Dwyer

Abdulaziz Al-ghonaium

Sami Al-Hajjar

Sulaiman Al-Jumaah

Andrei Allakhverdov

V A Pokrovsky

Andrew P See Brown

James H Allen

James H Gillooly
title: "A and B"
venue: Sophonisba Breckinridge
year: 2019

abstract: We investigate the dependence of the center of mass tomogram of a system with many degrees of freedom $N$ on the Planck constant \hbar It is shown that to use the central limit theorem under taking the limit $N\to \infty one should keep the energy of the system to be constant. In the case, the resulting distribution is Gaussian if the initial distribution is a product of independent excited states of a quantum oscillator or even and odd coherent states either. Then, if one turns the Planck constant \hbar \to 0$ we get \delta function associated with the distribution concentrated in zero with the probability equal to one.
author_names: Grigori G Amosov

Vladimir I Man'ko
title: A use of the central limit theorem to obtain a classical limit for the center of mass tomogram
year: 2009

abstract: Abstract Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently those with short range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed memory parallel machine which allows for message passing of data between independently executing processors. The algorithms are tested on a standard Lennard Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840 node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
author_names: Steven J Plimpton
title: Fast parallel algorithms for short range molecular dynamics
year: 1993

abstract: Preface. 1 Introduction to Optimization. 1.1 Introduction. 1.2 Historical Development. 1.3 Engineering Applications of Optimization. 1.4 Statement of an Optimization Problem. 1.5 Classification of Optimization Problems. 1.6 Optimization Techniques. 1.7 Engineering Optimization Literature. 1.8 Solution of Optimization Problems Using MATLAB. References and Bibliography. Review Questions. Problems. 2 Classical Optimization Techniques. 2.1 Introduction. 2.2 Single Variable Optimization. 2.3 Multivariable Optimization with No Constraints. 2.4 Multivariable Optimization with Equality Constraints. 2.5 Multivariable Optimization with Inequality Constraints. 2.6 Convex Programming Problem. References and Bibliography. Review Questions. Problems. 3 Linear Programming I: Simplex Method. 3.1 Introduction. 3.2 Applications of Linear Programming. 3.3 Standard Form of a Linear Programming Problem. 3.4 Geometry of Linear Programming Problems. 3.5 Definitions and Theorems. 3.6 Solution of a System of Linear Simultaneous Equations. 3.7 Pivotal Reduction of a General System of Equations. 3.8 Motivation of the Simplex Method. 3.9 Simplex Algorithm. 3.10 Two Phases of the Simplex Method. 3.11 MATLAB Solution of LP Problems. References and Bibliography. Review Questions. Problems. 4 Linear Programming II: Additional Topics and Extensions. 4.1 Introduction. 4.2 Revised Simplex Method. 4.3 Duality in Linear Programming. 4.4 Decomposition Principle. 4.5 Sensitivity or Postoptimality Analysis. 4.6 Transportation Problem. 4.7 Karmarkar's Interior Method. 4.8 Quadratic Programming. 4.9 MATLAB Solutions. References and Bibliography. Review Questions. Problems. 5 Nonlinear Programming I: One Dimensional Minimization Methods. 5.1 Introduction. 5.2 Unimodal Function. ELIMINATION METHODS. 5.3 Unrestricted Search. 5.4 Exhaustive Search. 5.5 Dichotomous Search. 5.6 Interval Halving Method. 5.7 Fibonacci Method. 5.8 Golden Section Method. 5.9 Comparison of Elimination Methods. INTERPOLATION METHODS. 5.10 Quadratic Interpolation Method. 5.11 Cubic Interpolation Method. 5.12 Direct Root Methods. 5.13 Practical Considerations. 5.14 MATLAB Solution of One Dimensional Minimization Problems. References and Bibliography. Review Questions. Problems. 6 Nonlinear Programming II: Unconstrained Optimization Techniques. 6.1 Introduction. DIRECT SEARCH METHODS. 6.2 Random Search Methods. 6.3 Grid Search Method. 6.4 Univariate Method. 6.5 Pattern Directions. 6.6 Powell's Method. 6.7 Simplex Method. INDIRECT SEARCH (DESCENT) METHODS. 6.8 Gradient of a Function. 6.9 Steepest Descent (Cauchy) Method. 6.10 Conjugate Gradient (Fletcher Reeves) Method. 6.11 Newton's Method. 6.12 Marquardt Method. 6.13 Quasi Newton Methods. 6.14 Davidon Fletcher Powell Method. 6.15 Broyden Fletcher Goldfarb Shanno Method. 6.16 Test Functions. 6.17 MATLAB Solution of Unconstrained Optimization Problems. References and Bibliography. Review Questions. Problems. 7 Nonlinear Programming III: Constrained Optimization Techniques. 7.1 Introduction. 7.2 Characteristics of a Constrained Problem. DIRECT METHODS. 7.3 Random Search Methods. 7.4 Complex Method. 7.5 Sequential Linear Programming. 7.6 Basic Approach in the Methods of Feasible Directions. 7.7 Zoutendijk's Method of Feasible Directions. 7.8 Rosen's Gradient Projection Method. 7.9 Generalized Reduced Gradient Method. 7.10 Sequential Quadratic Programming. INDIRECT METHODS. 7.11 Transformation Techniques. 7.12 Basic Approach of the Penalty Function Method. 7.13 Interior Penalty Function Method. 7.14 Convex Programming Problem. 7.15 Exterior Penalty Function Method. 7.16 Extrapolation Techniques in the Interior Penalty Function Method. 7.17 Extended Interior Penalty Function Methods. 7.18 Penalty Function Method for Problems with Mixed Equality and Inequality Constraints. 7.19 Penalty Function Method for Parametric Constraints. 7.20 Augmented Lagrange Multiplier Method. 7.21 Checking the Convergence of Constrained Optimization Problems. 7.22 Test Problems. 7.23 MATLAB Solution of Constrained Optimization Problems. References and Bibliography. Review Questions. Problems. 8 Geometric Programming. 8.1 Introduction. 8.2 Posynomial. 8.3 Unconstrained Minimization Problem. 8.4 Solution of an Unconstrained Geometric Programming Program Using Differential Calculus. 8.5 Solution of an Unconstrained Geometric Programming Problem Using Arithmetic Geometric Inequality. 8.6 Primal Dual Relationship and Sufficiency Conditions in the Unconstrained Case. 8.7 Constrained Minimization. 8.8 Solution of a Constrained Geometric Programming Problem. 8.9 Primal and Dual Programs in the Case of Less Than Inequalities. 8.10 Geometric Programming with Mixed Inequality Constraints. 8.11 Complementary Geometric Programming. 8.12 Applications of Geometric Programming. References and Bibliography. Review Questions. Problems. 9 Dynamic Programming. 9.1 Introduction. 9.2 Multistage Decision Processes. 9.3 Concept of Suboptimization and Principle of Optimality. 9.4 Computational Procedure in Dynamic Programming. 9.5 Example Illustrating the Calculus Method of Solution. 9.6 Example Illustrating the Tabular Method of Solution. 9.7 Conversion of a Final Value Problem into an Initial Value Problem. 9.8 Linear Programming as a Case of Dynamic Programming. 9.9 Continuous Dynamic Programming. 9.10 Additional Applications. References and Bibliography. Review Questions. Problems. 10 Integer Programming. 10.1 Introduction 588. INTEGER LINEAR PROGRAMMING. 10.2 Graphical Representation. 10.3 Gomory's Cutting Plane Method. 10.4 Balas' Algorithm for Zero One Programming Problems. INTEGER NONLINEAR PROGRAMMING. 10.5 Integer Polynomial Programming. 10.6 Branch and Bound Method. 10.7 Sequential Linear Discrete Programming. 10.8 Generalized Penalty Function Method. 10.9 Solution of Binary Programming Problems Using MATLAB. References and Bibliography. Review Questions. Problems. 11 Stochastic Programming. 11.1 Introduction. 11.2 Basic Concepts of Probability Theory. 11.3 Stochastic Linear Programming. 11.4 Stochastic Nonlinear Programming. 11.5 Stochastic Geometric Programming. References and Bibliography. Review Questions. Problems. 12 Optimal Control and Optimality Criteria Methods. 12.1 Introduction. 12.2 Calculus of Variations. 12.3 Optimal Control Theory. 12.4 Optimality Criteria Methods. References and Bibliography. Review Questions. Problems. 13 Modern Methods of Optimization. 13.1 Introduction. 13.2 Genetic Algorithms. 13.3 Simulated Annealing. 13.4 Particle Swarm Optimization. 13.5 Ant Colony Optimization. 13.6 Optimization of Fuzzy Systems. 13.7 Neural Network Based Optimization. References and Bibliography. Review Questions. Problems. 14 Practical Aspects of Optimization. 14.1 Introduction. 14.2 Reduction of Size of an Optimization Problem. 14.3 Fast Reanalysis Techniques. 14.4 Derivatives of Static Displacements and Stresses. 14.5 Derivatives of Eigenvalues and Eigenvectors. 14.6 Derivatives of Transient Response. 14.7 Sensitivity of Optimum Solution to Problem Parameters. 14.8 Multilevel Optimization. 14.9 Parallel Processing. 14.10 Multiobjective Optimization. 14.11 Solution of Multiobjective Problems Using MATLAB. References and Bibliography. Review Questions. Problems. A Convex and Concave Functions. B Some Computational Aspects of Optimization. B.1 Choice of Method. B.2 Comparison of Unconstrained Methods. B.3 Comparison of Constrained Methods. B.4 Availability of Computer Programs. B.5 Scaling of Design Variables and Constraints. B.6 Computer Programs for Modern Methods of Optimization. References and Bibliography. C Introduction to MATLAB(R) C.1 Features and Special Characters. C.2 Defining Matrices in MATLAB. C.3 CREATING m FILES. C.4 Optimization Toolbox. Answers to Selected Problems. Index
author_names: Singiresu S Rao
title: Engineering Optimization Theory and Practice
year: 2010