Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.

Position, professional expertise and functions of an educator of child care homes in Lithuania

Speaking about professionals, working with children at child care homes in Lithuania, first of all we encounter a problem of terminology. This problem rises, because in various countries and languages we call these professionals differently. In Lithuania we call them ”aukletojai”. We also use the word ”aukletojas” when speaking about both professionals, working directly with children at kindergartens, and parents, as all parents are educators of their children. We suppose, that the word ”aukletojas” corresponds to the German “erzieher”, and “aukleti” to “erziehen”. Every “aukletojas” in Lithuania clearly realizes, that he is a pedagogue, because in this country every professional, involved in educational work with children – an ”aukletojas”, a teacher, a social pedagogue and a special pedagogue – is called a pedagogue. In this context it is essential to conceive that in Lithuania an ”educator” and a ”social pedagogue” are different pedagogical professions and that none of the ”aukletojas” identify himself as a social pedagogue.

Simulation von Förderprozessen bei Vibrationsförderanlagen

Die erzielbare Fördergeschwindigkeit bei Vibrationsförderern hängt maßgeblich von der Bewegungsfunktion des Förderorganes ab. Für die gezielte Simulation dieser Anlagen mittels der diskreten Elemente Methode (DEM) ist es notwendig die geometrisch vernetzen Förderorgannachbildungen mit praxisrelevanten Bewegungsfunktionen zu beaufschlagen. Der Artikel beschreibt die Einbindung dieser Bewegungsfunktionen in die quellenoffene DEM-Software LIGGGHTS. Während des Simulationsprozesses wird eine Bewegung vernetzter CAD-Modelle durch trigonometrische Reihen ermöglicht.