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This document is an official template for The International Conference on Mechatronics.

El análisis estadístico de alta dimensión y tamaño de muestra pequeño (HDLSS) se está aplicando cada vez más en una amplia gama de contextos. En tales situaciones, se ve que el popular método de la Máquina de Vectores Soporte (SVM) sufre de ''Acumulación de datos'' en el margen, lo que puede disminuir la capacidad de generalización del modelo. Esto conduce al desarrollo de la Distance Weighted Discrimination para encontrar un hiperplano separador . En el presente trabajo se revisa y reproduce, con detalle en la derivación y solución de la función de pérdida que se resuelve usando SOCP, del método desarrollado en e implementado en el entorno R\cite{R}. Basado en el trabajo e implementación de se aplica y comparan resultados a conjuntos de datos reales y simulados (en medida de lo posible los mismos conjuntos de datos utilizados que en) Palabras clave: SVM, kernel, R (el ambiente de cómputo estadístico) y datos de alta dimensión con tamaño de muestra pequeño (data High Dimension Low Sample Size).

Although the analysis of data is a task that has gained the interest of the statistical community in recent years and whose familiarity with the statistical computing environment, they encourage the current statistical community (to students and teachers of the area) to complete statistical analysis reproducible by means of the tool R. However for years there has been a gap between the calculation of matrices on a large scale and the term "big data", in this work the Normalized Cut algorithm for images is applied. Despite the expected, the R environment to do image analysis is poorly, in comparison with other computing platforms such as the Python language or with specialized software such as OpenCV. Being well known the absence of such function, in this work we share an implementation of the Normalized Cut algorithm in the R environment with extensions to programs and processes performed in C ++, to provide the user with a friendly interface in R to segment images. The article concludes by evaluating the current implementation and looking for ways to generalize the implementation for a large scale context and reuse the developed code. Key words: Normaliced Cut, image segmentation, Lanczos algorithm, eigenvalues and eigenvectors, graphs, similarity matrix, R (the statistical computing environment), open source, large scale and big data.

Submitted full papers are to be supposed to be of at least 4 pages length. The authors should submit their papers electronically, written in English, due to the given deadline, through a web upload procedure available, see www.eusflat2019.cz).

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The viscosity of a particular fluid is an interesting parameter that plays an important role in fluid dynamics of that fluid. We chose the common household cooking item canola oil. Using a ball drop, we set out to measure viscosity at various temperatures and create a model for the viscosity of canola oil as a function of temperature, as well as an accurate measurement for viscosity at room temperature. It was found that the viscosity between 0 and 40 degrees Celsius can be approximated using an exponential function and that an estimation for viscosity at room temperature was not very difficult to obtain. The precision of this measurement was limited by uncertainty in lab equipment used to measure various quantities as well as the image analysis software we used and the limited frame-rate of our camera.

Projeto lab CE2

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