Wyniki 1-1 spośród 1 dla zapytania: authorDesc:"André L. MAITELLI"

Development of automatic classifier for sensor measurements of an industrial process DOI:10.15199/48.2019.03.22

Czytaj za darmo! »

Clustering processes, in general, are dependent on the problem to be presented. If we know in advance the number of cores representing the data or the tolerance needed to consider the creation of a new core, then we will have a good ranking. However, these conditions are not always available. In an industrial process, for example, the need to identify patterns through clustering is hampered by the fact that known methods are performed offline [1]. The K-Means is an example of a clustering algorithm that needs in advance information on the number of core. In addition, the procedure is computationally costly because all data is labeled at each new location of the cores. This makes it unfeasible to use the algorithm in real-time processes. Due to the limitations of existing clustering methods, a new clustering method has been proposed, known as TEDACloud [2]. The method aims to solve all these limitations by applying the concepts of eccentricity and typicity (TEDA) that allows recursive methods of statistical calculations to be determined on the data [3]. Even with the improvement from TEDA-Cloud, clustering still needs to store which points are about the classification of each pattern and also the number of points that are under the effect of two cores. The TEDA-Cloud classifies the data using statistical concepts that indicate a probability of the point being associated with each cores. If the eccentricity is high enough to decide that the points presented do not fit any pattern, then a new core is formed. However, if the data represented by a core are very scattered, they can affect the abagence of another core, allowing them to be joined together. The determination of whether two cores should be joined is calculated by the number of points that are together in the two. This makes TEDA-CLoud a high computational cost. The TEDA-Cloud modified brings the improvement of the way the fusion is defined. The method avoids the lab[...]

 Strona 1