A Shape Parameterization Method Using Principal Component Analysis in Applications to Parametric Shape Optimization

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
Kazuo Yonekura ◽  
Osamu Watanabe
Keyword(s):  
Principal Component Analysis ◽  
Shape Optimization ◽  
Parametric Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Principal Component ◽  
Component Analysis ◽  
Parameterization Method ◽  
Shape Parameterization ◽  
Set Of Points

This paper proposes a shape parameterization method using a principal component analysis (PCA) for shape optimization. The proposed method is used as a preprocessing tool of parametric optimization algorithms, such as genetic algorithms (GAs) or response surface methods (RSMs). When these parametric optimization algorithms are used, the number of parameters should be small while the design space represented by the parameters should be able to represent a variety of shapes. In order to define the parameters, PCA is applied to shapes. In many industrial fields, a large amount of data of shapes and their performance is accumulated. By applying PCA to these shapes included in a database, important features of the shapes are extracted. A design space is defined by basis vectors which are generated from the extracted features. The number of dimensions of the design space is decreased without omitting important features. In this paper, each shape is discretized by a set of points and PCA is applied to it. A shape discretization method is also proposed and numerical examples are provided.

Curve Representation Using Principal Component Analysis for Shape Optimization of Path Generating Mechanisms

2005 ◽  
Author(s):  
Nassim Khaled ◽  
Ahmad Smaili
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Principal Component ◽  
Component Analysis ◽  
Path Generation ◽  
Starting Point ◽  
Curve Representation ◽  
Generation Mechanisms

The focus of this paper is on the synthesis of path generation mechanisms based on shape optimization. The principle component analysis (PCA) technique used in image processing is employed to represent the desired coupler curve of the mechanism and simulated annealing is used as the optimization tool. PCA representation is invariant under rotation, translation, scaling, and starting point. Once a shape-optimized mechanism is found, it is translated, rotated, and scaled to its final form. An illustrative example is introduced to demonstrate the proposed method.

Structural Design Space Exploration Using Principal Component Analysis

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
Spencer Bunnell ◽  
Steven Gorrell ◽  
John Salmon ◽  
Christopher Thelin ◽  
Christopher Ruoti
Keyword(s):  
Principal Component Analysis ◽  
Structural Design ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Principal Component ◽  
Component Analysis ◽  
Compressor Blades ◽  
Design Variables ◽  
Stress Changes

Abstract Design space exploration (DSE) is the process whereby a designer seeks to understand some results across a set of design variations. Structural DSE of turbomachinery compressor blades is often challenging because the large number of design variables make it difficult to learn the effect that each variable has upon the stress contours. Principal component analysis (PCA) of the stress contours is used as a way to understand how the stress contours change over the design space. Two methods are introduced to address the challenge of understanding how the stress changes over a large number of variables. First, a two-point correlation is applied to relate the design variables to the scores of each principal component. Second, a coupling of the stress and coordinate location of each node in PCA is developed which also indicates how the stress variations relate to geometric variations. These provide insight to how design variables influence the stress. It is shown how these methods use PCA as DSE tools to better explore the structural design space of compressor blades. Better DSE can improve compressor blades and the computational cost needed for their design.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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