Abstract: A method for teaching an electronic computing device includes at least a machine learning algorithm for predicting a position-based propagation of radio waves in an environment, including the steps of: providing a mathematical model for the position-based propagation, wherein the mathematical model includes at least a physical model for the position-based propagation in the environment generating training data for the machine learning algorithm including a propagation field and/or a propagation domain; training the machine learning algorithm by fitting the training data to a partial derivative of the machine learning algorithm; and obtaining a prediction of a propagation loss by a weighted sum of multiple evaluations of the trained machine learning algorithm. Furthermore, provided is a computer program product, a computer-readable storage medium as well as an electronic computing device.

Abstract: A training data generator includes an interface to read symbols extracted from digital system plans for technical systems, wherein each of the system plans represents the design and/or the functionality of a technical system by of symbols, and the system plans are identical. The training data generator also includes a storage module which is designed to store the extracted symbols, a selection module which is designed to randomly select a symbol sub-quantity of the stored symbols using a random generator, a generator which is designed to generate at least one synthetic system plan on the basis of the selected symbol sub-quantity, and an output module to output the at least one synthetic system plan as training data in order to train a trainable image detection module, wherein the trainable image detection module is designed to generate a digital system plan using an analog system plan of a technical system.

Abstract: The present invention relates to a method for scheduling charging of electric vehicles by an electric vehicle charging system, wherein charging objectives to be considered in generating a charging schedule for one or more charging stations of the charging system are obtained, a hierarchy of the charging objectives is determined and the charging schedule is generated by performing a lexicographic optimization based on the hierarchy of the charging objectives.

Abstract: A control device of an automation system, which is configured to control a plant, such as a production plant, including using an AI system, is provided. In an application of the control device, the device monitors the production with regard to the quality of the objects produced, for example, with regard to the presence of fault cases. The AI system is trained in advance based on a plurality of known states of the objects, so that the AI system may be trained for the occurrence of new, previously unknown states, where only a small number of example cases are required.