scholarly journals Ensemble of Regression-Type and Interpolation-Type Metamodels

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 654 ◽  
Author(s):  
Cheng Yan ◽  
Jianfeng Zhu ◽  
Xiuli Shen ◽  
Jun Fan ◽  
Dong Mi ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Computational Cost ◽  
Energy Sources ◽  
Benchmark Problems ◽  
Physical Systems ◽  
Functional Fitting ◽  
Cost Of Time ◽  
The Individual

Metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks. Lacking the prior knowledge of actual physical systems, it may be difficult to find an appropriate metamodel in advance for a new task. A favorite way of overcoming this difficulty is to construct an ensemble metamodel by assembling two or more individual metamodels. Motivated by the existing works, a novel metamodeling approach for building the ensemble metamodels is proposed in this paper. By thoroughly exploring the characteristics of regression-type and interpolation-type metamodels, some useful information is extracted from the feedback of the regression-type metamodels to further improve the functional fitting capability of the ensemble metamodels. Four types of ensemble metamodels were constructed by choosing four individual metamodels. Common benchmark problems are chosen to compare the performance of the individual and ensemble metamodels. The results show that the proposed metamodeling approach reduces the risk of selecting the worst individual metamodel and improves the accuracy of the used individual metamodels.

scholarly journals Scalable Multiport Converter Structure for Easy Grid Integration of Alternate Energy Sources for Generation of Isolated Voltage Sources for MMC

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1779
Author(s):  
Syed Rahman ◽  
Irfan Khan ◽  
Khaliqur Rahman ◽  
Sattam Al Otaibi ◽  
Hend I. Alkhammash ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Energy Resources ◽  
Energy Sources ◽  
Renewable Energy Resources ◽  
Component Level ◽  
Storage Devices ◽  
Multiport Converter ◽  
Detailed Simulation ◽  
The Individual

This paper presents a novel, scalable, and modular multiport power electronic topology for the integration of multiple resources. This converter is not only scalable in terms of the integration of multiple renewable energy resources (RES) and storage devices (SDs) but is also scalable in terms of output ports. Multiple dc outputs of a converter are designed to serve as input to the stacking modules (SMs) of the modular multilevel converter (MMC). The proposed multiport converter is bidirectional in nature and superior in terms of functionality in a way that a modular universal converter is responsible for the integration of multiple RES/SDs and regulates multiple dc output ports for SMs of MMC. All input ports can be easily integrated (and controlled), and output ports also can be controlled independently in response to any load variations. An isolated active half-bridge converter with multiple secondaries acts as a central hub for power processing with multiple renewable energy resources that are integrated at the primary side. To verify the proposed converter, a detailed design of the converter-based system is presented along with the proposed control algorithm for managing power on the individual component level. Additionally, different modes of power management (emulating the availability/variability of renewable energy sources (RES)) are exhibited and analyzed here. Finally, detailed simulation results are presented in detail for the validation of the proposed concepts and design process.

scholarly journals Variable-order fracture mechanics and its application to dynamic fracture

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sansit Patnaik ◽  
Fabio Semperlotti
Keyword(s):  
Dynamic Fracture ◽  
Crack Surface ◽  
A Priori ◽  
Computational Cost ◽  
Dynamic Crack Propagation ◽  
Benchmark Problems ◽  
Variable Order ◽  
Dynamic Crack ◽  
Brittle Solids ◽  
Complex Crack

AbstractThis study presents the formulation, the numerical solution, and the validation of a theoretical framework based on the concept of variable-order mechanics and capable of modeling dynamic fracture in brittle and quasi-brittle solids. More specifically, the reformulation of the elastodynamic problem via variable and fractional-order operators enables a unique and extremely powerful approach to model nucleation and propagation of cracks in solids under dynamic loading. The resulting dynamic fracture formulation is fully evolutionary, hence enabling the analysis of complex crack patterns without requiring any a priori assumption on the damage location and the growth path, and without using any algorithm to numerically track the evolving crack surface. The evolutionary nature of the variable-order formalism also prevents the need for additional partial differential equations to predict the evolution of the damage field, hence suggesting a conspicuous reduction in complexity and computational cost. Remarkably, the variable-order formulation is naturally capable of capturing extremely detailed features characteristic of dynamic crack propagation such as crack surface roughening as well as single and multiple branching. The accuracy and robustness of the proposed variable-order formulation are validated by comparing the results of direct numerical simulations with experimental data of typical benchmark problems available in the literature.

scholarly journals A consensus-based global optimization method for high dimensional machine learning problems

2020 ◽  
Author(s):  
Jose Carrillo ◽  
Shi Jin ◽  
Lei Li ◽  
Yuhua Zhu
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Weighted Average ◽  
Planck Equation ◽  
Optimization Method ◽  
Learning Problems ◽  
High Dimensional ◽  
Nonconvex Functions ◽  
The Individual ◽  
Parameter Constraints

We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl. Sci., 27(01):183{204, 2017], which is a gradient-free optimization method for general nonconvex functions. We rst replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted average which the individual particles tend to relax toward. For its mean- eld limit{a nonlinear Fokker-Planck equation{we prove, in both time continuous and semi-discrete settings, that the convergence of the method, which is exponential in time, is guaranteed with parameter constraints independent of the dimensionality. We also conduct numerical tests to high dimensional problems to check the success rate of the method.

scholarly journals COSMO: A Conic Operator Splitting Method for Convex Conic Problems

2021 ◽  
Vol 190 (3) ◽  
pp. 779-810
Author(s):  
Michael Garstka ◽  
Mark Cannon ◽  
Paul Goulart
Keyword(s):  
Convex Sets ◽  
Operator Splitting ◽  
Splitting Method ◽  
Computational Cost ◽  
Coefficient Matrix ◽  
Benchmark Problems ◽  
Portfolio Optimisation ◽  
Splitting Algorithm ◽  
Semidefinite Programs ◽  
Operator Splitting Method

AbstractThis paper describes the conic operator splitting method (COSMO) solver, an operator splitting algorithm and associated software package for convex optimisation problems with quadratic objective function and conic constraints. At each step, the algorithm alternates between solving a quasi-definite linear system with a constant coefficient matrix and a projection onto convex sets. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefinite programs that arise in portfolio optimisation, graph theory, and robust control. Moreover, the solver uses chordal decomposition techniques and a new clique merging algorithm to effectively exploit sparsity in large, structured semidefinite programs. Numerical comparisons with other state-of-the-art solvers for a variety of benchmark problems show the effectiveness of our approach. Our Julia implementation is open source, designed to be extended and customised by the user, and is integrated into the Julia optimisation ecosystem.

Taguchi-Particle Swarm Optimization for Numerical Optimization

2012 ◽  
pp. 44-59
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

Cluster-Based Input Selection for Transparant Fuzzy Modeling

2008 ◽  
pp. 1138-1156
Author(s):  
Can Yang ◽  
Jun Meng ◽  
Shanan Zhu
Keyword(s):  
Computational Cost ◽  
Fuzzy Model ◽  
Selection Procedure ◽  
Specific Model ◽  
Benchmark Problems ◽  
Input Selection ◽  
Model Free ◽  
Interpretable Model ◽  
Data Collections ◽  
Accuracy Of Prediction

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this paper, an effective model-free method is proposed for the input selection. This method is based on sensitivity analysis using Minimum Cluster Volume (MCV) algorithm. The advantage of our proposed method is that with no specific model needed to be built in advance for checking possible input combinations, the computational cost is reduced and changes of data patterns can be captured automatically. The effectiveness of the proposed method is evaluated by using three well-known benchmark problems, which show that the proposed method works effectively with small and medium sized data collections. With an input selection procedure, a concise fuzzy model is constructed with high accuracy of prediction and better interpretation of data, which serves the purpose of patterns discovery in data mining well.

Das Spiel des Übersetzens. Grundzüge einer komparativen Studie

2021 ◽  
pp. 453-486
Author(s):  
Eliane Laverdure
Keyword(s):  
Prior Knowledge ◽  
Theoretical Framework ◽  
Point Of View ◽  
Translation Theory ◽  
Self Awareness ◽  
Per Se ◽  
Linguistic Transfer ◽  
The Individual ◽  
Hans Georg Gadamer

This article seeks to shed some light on the experience of interpretation and translation from a hermeneutical point of view, more precisely on how the subjectivity of the translator plays an essential mediation role in the process of rendering the meaning of a text in a new language, a role that certainly goes beyond the sole linguistic transfer, without being per se subjective. This idea can best be understood through the concept of “game” as developed by the philosopher Hans-Georg Gadamer and introduced in translation theory by Fritz Paepcke, because it offers a model in which the participation of the individual is necessary without being arbitrary, since the players have to abide by the rules of the specific game they are playing – in this case, the text itself. However, the aim of this article is not to offer a method of translation, but is rather an attempt to consider the adequacy of translation on the basis of subjective and intersubjective factors – including the translator’s own prior knowledge and experience, his openness towards the text and his critical self-awareness – and therefore to propose an alternative to the overly normative and restrictive theoretical framework strictly focused on linguistic equivalence between ST and TT.

scholarly journals Support Vector Regression for the Modeling and Synthesis of Near-Field Focused Antenna Arrays

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1352 ◽  
Author(s):  
Rafael González Ayestarán
Keyword(s):  
Support Vector Regression ◽  
Antenna Arrays ◽  
Near Field ◽  
Computational Cost ◽  
Support Vector ◽  
Analysis Tool ◽  
Training Samples ◽  
Learning Capabilities ◽  
Novel Method ◽  
The Individual

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented.

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