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Visual Approach (visual + approach)

Distribution by Scientific Domains
Engineering100%

Selected Abstracts

Investigating static and dynamic characteristics of electromechanical actuators (EMA) with MATLAB GUIs

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 2 2010
Gursel Sefkat
Abstract This paper deals with the design of an electromechanical device considering some prescribed performance requirements, and static and dynamic analysis of this device are carried out. In studying the transient response of such a system, as part of dynamic analysis, two methods mostly used finite element method (FEM) and finite differences method (FDM). However, these methods need much CPU time. In this work, a computer simulator program is developed for an EMA. This technique is implemented in the MATLAB-Simulink environment and tested for different design tasks such as electromagnetic valves or electromechanical brakes etc. Furthermore, by using GUIDE tools within MATLAB, a simple useful and user-friendly GUI structure is developed to provide a visual approach to design and analysis process. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 383,396, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20279 [source]

Graphical user interfaces in an engineering educational environment

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 1 2005
Christopher Depcik
Abstract Graphical user interfaces (GUIs) are being increasingly used in the classroom to provide users of computer simulations with a friendly and visual approach to specifying all input parameters and increased configuration flexibility. In this study, the authors first describe a number of software and language options that are available to build GUIs. Subsequently, a comprehensive comparative assessment of possible alternatives is undertaken in the light of a benchmark educational program used in a course on computational fluid dynamics (CFD) at the University of Michigan. For the GUIs presented, their educational value with respect to flexible data entry and post-processing of results has been demonstrated. In addition, the authors offer recommendations for pros and cons of available options in terms of platform independence, ease of programming, facilitation of interaction with students, and flexibility. © 2005 Wiley Periodicals, Inc. Comput Appl Eng Educ 13: 48,59, 2005; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20029 [source]

MATLAB based GUIs for linear controller design via convex optimization

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 1 2003
Wathanyoo Khaisongkram
Abstract Owing to the current evolution of computational tools, a complicated parameter optimization problem could be effectively solved by a computer. In this paper, a CAD tool for multi-objective controller design based on MATLAB program is developed. In addition, we construct simple GUIs (using GUIDE tools within MATLAB) to provide a visual approach in specifying the constraints. The linear controller design problem can be cast as the convex optimization subjected to time domain and frequency domain constraints. This optimization problem is efficiently solved within a finite dimensional subspace by a practical ellipsoid algorithm. In the design process, we include a model reduction of the resulting controller to speed up the computational efficiency. Finally, a numerical example shows the capability of the program to design multi-objective controller for a one-link flexible robot arm. © 2003 Wiley Periodicals, Inc. Comput Appl Eng Educ 11: 13,24, 2003; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.10035 [source]

Decision-making method using a visual approach for cluster analysis problems; indicative classification algorithms and grouping scope

EXPERT SYSTEMS, Issue 3 2007
Ran M. Bittmann
Abstract: Currently, classifying samples into a fixed number of clusters (i.e. supervised cluster analysis) as well as unsupervised cluster analysis are limited in their ability to support ,cross-algorithms' analysis. It is well known that each cluster analysis algorithm yields different results (i.e. a different classification); even running the same algorithm with two different similarity measures commonly yields different results. Researchers usually choose the preferred algorithm and similarity measure according to analysis objectives and data set features, but they have neither a formal method nor tool that supports comparisons and evaluations of the different classifications that result from the diverse algorithms. Current research development and prototype decisions support a methodology based upon formal quantitative measures and a visual approach, enabling presentation, comparison and evaluation of multiple classification suggestions resulting from diverse algorithms. This methodology and tool were used in two basic scenarios: (I) a classification problem in which a ,true result' is known, using the Fisher iris data set; (II) a classification problem in which there is no ,true result' to compare with. In this case, we used a small data set from a user profile study (a study that tries to relate users to a set of stereotypes based on sociological aspects and interests). In each scenario, ten diverse algorithms were executed. The suggested methodology and decision support system produced a cross-algorithms presentation; all ten resultant classifications are presented together in a ,Tetris-like' format. Each column represents a specific classification algorithm, each line represents a specific sample, and formal quantitative measures analyse the ,Tetris blocks', arranging them according to their best structures, i.e. best classification. [source]