Abstract: A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.

Abstract: A device and a method for training a model including a first sub-model and a second sub-model. Digital data are down-scaled to generate first input data. The digital data are divided into multiple data areas to generate second input data. A first sub-model generates first sub-model data relating to first input data fed to it. The first sub-model data are up-scaled to form first output data. A second sub-model for the data areas generates corresponding output data areas relating to second input data fed to it. The output data areas are assembled to form second output data. The first and second output data are combined to form third output data. The first sub-model is trained on the digital data by comparing provided target data and the first output data. The second sub-model is trained on the digital data by comparing the target data and the third output data.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A method for monitored training of a neural network. In the method, training examples including training measured data and associated training output variables are provided; a spatial region, which contains at least a part of the locations indicated by the training measured data of a training example, is subdivided into a grid made up of adjoining cells; for each cell, values of the measured variables contained in the training measured data for all locations in this cell are aggregated to form values of the measured variables which relate to this cell; these aggregated values of the measured variables are mapped by the neural network on one or multiple output variables; deviations of these output variables from the training output variables are assessed using a predefined cost function; parameters of the neural network are optimized.

Abstract: A method for synthetically generating a point cloud of radar or LIDAR reflections, a reflection indicating at least one location at which radar or LIDAR interrogating radiation has been reflected. In the method, distribution functions which according to a random distribution provide samples in each case for at least one of the variables contained in the radar or LIDAR reflections are provided; synthetic reflections are generated by drawing samples in each case from the distribution functions for variables contained in the radar or LIDAR reflections, one of multiple distribution functions being selected according to at least one selection random distribution in order to draw each sample; the synthetic reflections are combined to form the sought point cloud.

Abstract: A method for ascertaining a transformation, which converts source measured data recorded using a source configuration of a measuring system at a scenery, into target measured data, which a target configuration of the measuring system would record at the same scenery. In the method: training source measured data recorded using the source configuration at training sceneries are provided; an approach is predefined, according to which the target measured data result from the source measured data by application of predefined filter operation(s) to the source measured data; the training source measured data are mapped by application of the filter operation on target measured data; the trainable model is trained with the goal of bringing the resulting filter operation, and/or the target measured data generated thereby into harmony with a predefined piece of additional information and/or condition; the approach completed by the trained model is provided as the sought-after transformation.

Abstract: A method is disclosed for converting source radar data of a source configuration of a radar system target radar data of a target configuration. The method comprises: providing a source array of grid cells for source reflex locations; determining, for each respective grid cell in the source array, a probability or frequency that source reflex locations are located in the respective grid cell; forming a source tensor including the source array populated with the probability or frequency for each grid cell; transforming the source tensor into a target tensor including a target array of grid cells for the target reflex locations and indicating the probabilities or frequencies of the target reflex locations for each respective grid cell; and generating the target radar data by sampling the location coordinates of the target reflex locations.

Abstract: A method for converting measured data of at least one source measurement modality into realistic measured data of at least one target measurement modality. The method includes: the measured data of the source measurement modality are mapped onto representations in a latent space using an encoder of a trained encoder-decoder arrangement, and the representations are mapped onto the realistic measured data of the target measurement modality using the decoder of the encoder-decoder arrangement, the amount of information of the representations of measured data in the latent space being smaller than the amount of information of the measured data.

Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Abstract: A device and a method for training a model including a first sub-model and a second sub-model. Digital data are down-scaled to generate first input data. The digital data are divided into multiple data areas to generate second input data. A first sub-model generates first sub-model data relating to first input data fed to it. The first sub-model data are up-scaled to form first output data. A second sub-model for the data areas generates corresponding output data areas relating to second input data fed to it. The output data areas are assembled to form second output data. The first and second output data are combined to form third output data. The first sub-model is trained on the digital data by comparing provided target data and the first output data. The second sub-model is trained on the digital data by comparing the target data and the third output data.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.