scholarly journals Design optimization methods for genomic DNA tiling arrays

2005 ◽  
Vol 16 (2) ◽  
pp. 271-281 ◽  
Author(s):  
P. Bertone
Keyword(s):  
Design Optimization ◽  
Genomic Dna ◽  
Optimization Methods ◽  
Tiling Arrays ◽  
Dna Tiling

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

Applications of DNA tiling arrays for whole-genome analysis

Genomics ◽  
2005 ◽  
Vol 85 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Todd C. Mockler ◽  
Joseph R. Ecker
Keyword(s):  
Genome Analysis ◽  
Whole Genome ◽  
Whole Genome Analysis ◽  
Tiling Arrays ◽  
Dna Tiling

scholarly journals Recent Design Optimization Methods for Energy-Efficient Electric Motors and Derived Requirements for a New Improved Method—Part 1

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1400
Author(s):  
Johannes Schmelcher ◽  
Max Kleine Büning ◽  
Kai Kreisköther ◽  
Dieter Gerling ◽  
Achim Kampker
Keyword(s):  
Rare Earth ◽  
Design Optimization ◽  
Energy Efficient ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Electric Motors ◽  
Electric Cars

Energy-efficient electric motors are gathering an increased attention since they are used in electric cars or to reduce operational costs, for instance. Due to their high efficiency, permanent-magnet synchronous motors are used progressively more. However, the need to use rare-earth magnets for such high-efficiency motors is problematic not only in regard to the cost but also in socio-political and environmental aspects. Therefore, an increasing effort has to be put in finding the best design possible. The goals to achieve are, among others, to reduce the amount of rare-earth magnet material but also to increase the efficiency. In the first part of this multipart paper, characteristics of optimization problems in engineering and general methods to solve them are presented. In part two, different approaches to the design optimization problem of electric motors are highlighted. The last part will evaluate the different categories of optimization methods with respect to the criteria: degrees of freedom, computing time and the required user experience. As will be seen, there is a conflict of objectives regarding the criteria mentioned above. Requirements, which a new optimization method has to fulfil in order to solve the conflict of objectives will be presented in this last paper.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

scholarly journals Μέθοδοι σχεδιασμού-βελτιστοποίησης στις στροβιλομηχανές βασισμένες στους εξελικτικούς αλγορίθμους

2013 ◽  
Author(s):  
Στυλιανός Κυριάκου
Keyword(s):  
Design Optimization ◽  
Real World ◽  
Large Scale ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Turn Around Time ◽  
Phd Thesis

The scope of this PhD thesis is to pΙopose a set of improvements to existingshape design-optimization methods in fluid dynamiοs based on EvolutionaryΑlgorithms (EAs) and demonstrate their effiοienοy in real-world applications.Though the proposed method and the developed EA-based software are bothgeneriο, this thesis foοuses on applicatiοns in the fields of hydrau1ic andthermal turbomaοhines. With the proposed a1gorithmic variants, theoptimization turn-around time is notiοeably reduοed with respeοt to that ofοonventional (reference, background) methods. Though the latter areοomputationally expensive, with the proposed add-ons, they becomeaffordable even for large-scale industrial applications.

Reduced Order Design Optimization of Liquid Cooled Heat Sinks

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Aniket Ajay Lad ◽  
Kai A. James ◽  
William P. King ◽  
Nenad Miljkovic
Keyword(s):  
Design Optimization ◽  
Power Density ◽  
Building Blocks ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Heat Sinks ◽  
Geometrical Parameters ◽  
Power Module ◽  
Reduced Order

Abstract The recent growth in electronics power density has created a significant need for effective thermal management solutions. Liquid-cooled heat sinks or cold plates are typically used to achieve high volumetric power density cooling. A natural tradeoff exists between the thermal and hydraulic performance of a cold plate, creating an opportunity for design optimization. Current design optimization methods rely on computationally expensive and time consuming computational fluid dynamics (CFD) simulations. Here, we develop a rapid design optimization tool for liquid cooled heat sinks based on reduced-order models for the thermal-hydraulic behavior. Flow layout is expressed as a combination of simple building blocks on a divided coarse grid. The flow layout and geometrical parameters are incorporated to optimize designs that can effectively address heterogeneous cooling requirements within electronics packages. We demonstrate that the use of population-based searches for optimal layout selection, while not ensuring a global optimum solution, can provide optimal or near-optimal results for most of the test cases studied. The approach is shown to generate optimal designs within a timescale of 60–120 s. A case study based on cooling of a commercial silicon carbide (SiC) electronics power module is used to demonstrate the application of the developed tool and is shown to improve the performance as compared to an aggressive state-of-the-art single-phase liquid cooling solution by reducing the SiC junction-to-coolant thermal resistance by 25% for the same pressure drop.

Optimized System-Level Design Methods for NoC-Based Many Core Embedded Systems

2014 ◽  
pp. 150-179
Author(s):  
Haoyuan Ying ◽  
Klaus Hofmann ◽  
Thomas Hollstein
Keyword(s):  
Embedded Systems ◽  
Design Optimization ◽  
Optimization Methods ◽  
Design Methods ◽  
System Level ◽  
System Level Design ◽  
Level Design ◽  
On Chip ◽  
Many Core

Due to the growing demand on high performance and low power in embedded systems, many core architectures are proposed the most suitable solutions. While the design concentration of many core embedded systems is switching from computation-centric to communication-centric, Network-on-Chip (NoC) is one of the best interconnect techniques for such architectures because of the scalability and high communication bandwidth. Formalized and optimized system-level design methods for NoC-based many core embedded systems are desired to improve the system performance and to reduce the power consumption. In order to understand the design optimization methods in depth, a case study of optimizing many core embedded systems based on 3-Dimensional (3D) NoC with irregular vertical link distribution topology through task mapping, core placement, routing, and topology generation is demonstrated in this chapter. Results of cycle-accurate simulation experiments prove the validity and efficiency of the design methods. Specific to the case study configuration, in maximum 60% vertical links can be saved while maintaining the system efficiency in comparison to full vertical link connection 3D NoCs by applying the design optimization methods.

Development of a Common Platform for Testing Metamodel Based Design Optimization Methods

2013 ◽  
Author(s):  
Adel A. Younis ◽  
George H. Cheng ◽  
G. Gary Wang ◽  
Zuomin Dong
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Computation Time ◽  
Optimization Methods ◽  
Global Optimum ◽  
Test Problems ◽  
Test Bed ◽  
Test Procedures ◽  
Local Optima

Metamodel based design optimization (MBDO) algorithms have attracted considerable interests in recent years due to their special capability in dealing with complex optimization problems with computationally expensive objective and constraint functions and local optima. Conventional unimodal-based optimization algorithms and stochastic global optimization algorithms either miss the global optimum frequently or require unacceptable computation time. In this work, a generic testbed/platform for evaluating various MBDO algorithms has been introduced. The purpose of the platform is to facilitate quantitative comparison of different MBDO algorithms using standard test problems, test procedures, and test outputs, as well as to improve the efficiency of new algorithm testing and improvement. The platform consists of a comprehensive test function database that contains about 100 benchmark functions and engineering problems. The testbed accepts any optimization algorithm to be tested, and only requires minor modifications to meet the test-bed requirements. The testbed is useful in comparing the performance of competing algorithms through execution of same problems. It allows researchers and practitioners to test and choose the most suitable optimization tool for their specific needs. It also helps to increase confidence and reliability of the newly developed MBDO tools. Many new MBDO algorithms, including Mode Pursuing Sampling (MPS), Pareto Set Pursuing (PSP), and Space Exploration and Unimodal Region Elimination (SEUMRE), were tested in this work to demonstrate its functionality and benefits.

Sensitivity Analysis of Flexible Multibody Systems Based on the Motion Formalism and the Discrete Adjoint Method

2018 ◽  
Author(s):  
Alfonso Callejo ◽  
Valentin Sonneville ◽  
Olivier A. Bauchau
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Cross Sections ◽  
Adjoint Method ◽  
Optimization Methods ◽  
Sensitivity Analyses ◽  
Discrete Adjoint ◽  
Discrete Adjoint Method ◽  
Flexible Multibody ◽  
Forward Equations

The combination of analysis and optimization methods in mechanical engineering, also known as design optimization, has great potential in product development. Robust sensitivity analyses that provide reliable and efficient objective function gradients play a key role in design optimization. This paper presents a discrete adjoint method for the sensitivity analysis of flexible mechanical systems. The ultimate goal is to be able to relate the physical properties of beam cross-sections to the dynamic behavior of the system, which is key to design realistic flexible elements. The underlying flexible multibody formulation is one that supports large-amplitude motion, beams with sophisticated composite cross-sections, and kinematic joints. A summary of the kinematic and dynamic foundations of the forward equations is presented first. Then, a discrete adjoint method, along with meaningful examples and validation, is presented. The method has proven to provide extremely accurate and reliable sensitivities.

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