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You searched for subject:(transformation in order AND hyperplane). Showing records 1 – 2 of 2 total matches.

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1. Francisco Josà Calixto de Sousa. CombinaÃÃes afins.

Degree: Master, 2013, Universidade Federal do Ceará

Neste trabalho, consideramos combinaÃÃes afins de vetores de um espaÃo vetorial com especiais aplicaÃÃes no ensino mÃdio atravÃs da mÃdia aritmÃtica ponderada e da desigualdade de Jensen. Verificamos caracterÃsticas de transformaÃÃes lineares de conjuntos especÃficos nos espaÃos vetoriais como conjuntos convexos e variedades afins, atravÃs do nÃcleo e da imagem das transformaÃÃes. Estabelecemos relaÃÃes entre transformaÃÃes afins, combinaÃÃes afins e transformaÃÃes lineares. Discutimos a dimensÃo do hiperplano relacionando-o como variedade afim. Vemos que todo subespaÃo vetorial de Rn com dimensÃo n - 1 Ã um hiperplano, assim como o nÃcleo de um funcional linear.

In this paper, we consider combinations of related vectors of a vector space with special applications in high school through the weighted arithmetic mean and the Jensen inequality. We observed characteristics of specific sets of linear transformations in the vector spaces as convex sets and related varieties through the core and image transformations. Established relations between affine transformations, combinations thereof and linear transformations. We discuss the size of the hyperplane relating it as affine variety. We see that all of Rn vector subspace with dimension n - 1 is a hyperplane, as the core of a linear functional.

Advisors/Committee Members: JoÃo Montenegro de Miranda, Marcelo Ferreira de Melo, Marcos Ferreira de Melo.

Subjects/Keywords: MATEMATICA; variedade afim; transformaÃÃo linear; transformaÃÃo afim e hiperplano; combination order; variety in order; linear transformation; transformation in order and hyperplane; Ãlgebra linear; Ensino mÃdio; Numeros reais; Real numbers

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sousa, F. J. C. d. (2013). CombinaÃÃes afins. (Masters Thesis). Universidade Federal do Ceará. Retrieved from http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10039 ;

Chicago Manual of Style (16th Edition):

Sousa, Francisco Josà Calixto de. “CombinaÃÃes afins.” 2013. Masters Thesis, Universidade Federal do Ceará. Accessed October 23, 2020. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10039 ;.

MLA Handbook (7th Edition):

Sousa, Francisco Josà Calixto de. “CombinaÃÃes afins.” 2013. Web. 23 Oct 2020.

Vancouver:

Sousa FJCd. CombinaÃÃes afins. [Internet] [Masters thesis]. Universidade Federal do Ceará 2013. [cited 2020 Oct 23]. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10039 ;.

Council of Science Editors:

Sousa FJCd. CombinaÃÃes afins. [Masters Thesis]. Universidade Federal do Ceará 2013. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10039 ;


Arizona State University

2. Mosley, James Holton. The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes.

Degree: PhD, Electrical Engineering, 2011, Arizona State University

Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.

Subjects/Keywords: Electrical Engineering; Industrial Engineering; Applied Mathematics; Hyperplane Classifier; L-moment Kernel; Order Statistics; Statistical Process Control; Support Vector Machines; Western Electric Rules

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mosley, J. H. (2011). The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/9289

Chicago Manual of Style (16th Edition):

Mosley, James Holton. “The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes.” 2011. Doctoral Dissertation, Arizona State University. Accessed October 23, 2020. http://repository.asu.edu/items/9289.

MLA Handbook (7th Edition):

Mosley, James Holton. “The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes.” 2011. Web. 23 Oct 2020.

Vancouver:

Mosley JH. The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes. [Internet] [Doctoral dissertation]. Arizona State University; 2011. [cited 2020 Oct 23]. Available from: http://repository.asu.edu/items/9289.

Council of Science Editors:

Mosley JH. The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes. [Doctoral Dissertation]. Arizona State University; 2011. Available from: http://repository.asu.edu/items/9289

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