Robert Boschgmbh

Max-planck-gesellschaft Zur Forderung Der Wissenschaften E.v.

Martin Schiegg

Heiner Markert

Andre Guntoro

Muhammad Bilal Zafar

Stefan Angermaier

Ernst Kloppenburg

Christian Daniel

Sebastian Gerwinn

Xiahan Shi

Max Welling

Volker Imhof

Michael Pfeiffer

The Duy Nguyen-Tuong

Edgar Klenske

Andreas Doerr

Laura Beggel

Sebastian Trimpe

Zeynep Akata

Bernhard Kausler

Leonard Salewski

Roman Dominik Kilgus

A computer-implemented method of training a function for use in controlling or monitoring a physical system operating in an environment. The function maps an input instance comprising sensor measurements to an output signal. The function is parameterized by a set of parameters including representations of multiple reference instances. Given a training input instance, a number of reference instances are identified as being similar to the training input instance, and their representations and/or output signals are aggregated into an aggregate latent representation for the training input instance. Based on this aggregate latent representation, an output signal for the training input instance is determined, which is compared to a training output signal to derive a training signal. At least a representation of a reference instance is adjusted according to the training signal, obtaining a reference instance not comprised in the training dataset.

Determining an output signal by aggregating parent instances

A machine learning system and method of operating a machine learning system for determining a time series, comprising providing an input for a first in particular generative model depending on a probabilistic variable, determining an output of the first model in response to the input for the first model, the output of the first model characterizing the time series. The first model comprises a first layer that is trained to map input for the first model determined depending on the probabilistic variable to output characterizing intermediate data, and a second layer that is trained to map the intermediate data to the time series depending on an output of a third layer of the first model. The output of the third layer characterizes a physical constraint to a machine state. Values of the time series or of the intermediate data are constrained by the output of the third layer.

Computer implemented machine learning system and a method for operating the machine learning system for determining a time series

A method for determining a state of a transmission for a vehicle includes providing an input for a first generative model depending on a route information, a vehicle speed, a probabilistic variable, and an output of a second physical model, and determining an output of the first model characterizing the state of the transmission in response to the input for the first model. The first model includes a first layer trained to map input to an intermediate state and a second layer trained to map the intermediate state to the state of the transmission depending on the output of the second model. The method includes providing an input for the second physical model depending on at least one vehicle state and/or the route information, and determining an output of the second model in response to the input for the second model. The output of the second model characterizes limit(s) for the intermediate state.

Device, method and machine learning system for determining a state of a transmission for a vehicle

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

Method for ascertaining driving profiles

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

A model calculating unit for calculating a neural layer of a multilayer perceptron model having a hardwired processor core developed in hardware for calculating a definitely specified computing algorithm in coupled functional blocks. The processor core is designed to calculate, as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron, an output variable for each neuron for a neural layer of a multilayer perceptron model having a number of neurons, a sum of the values of the input variables weighted by the weighting factor, determined by the neuron and the input variable, and the offset value specified for the neuron being calculated for each neuron and the result being transformed using an activation function in order to obtain the output variable for the neuron.

Model calculating unit and control unit for calculating a neural layer of a multilayer perceptron model

A device and a method for operating a test stand. A set of measurements of input variables of a system model of a component of a machine is provided. An optimization problem is defined using a set of measurements of input variables. A gradient for solving the optimization problem is determined as a function of the set of measurements. A solution to the optimization problem, which defines a design for input data for the test stand for a measurement on the component, is determined as a function of the gradient. A measurement of output data is acquired on the component on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. The system model for the component is trained as a function of the pairs.

Device and method for operating a test stand

A model calculating unit for the selective calculation of an RBF model or of a neural layer of a multilayer perceptron model having a hardwired processor core designed in hardware for calculating a fixedly specified computing algorithm in coupled function blocks. The processor core is designed to calculate an output variable for an RBF model as a function of one or multiple input variables of an input variable vector, of supporting points, of length scales, of parameters specified for each supporting point, the processor core furthermore being designed to calculate an output variable for each neuron for the neural layer of the multilayer perceptron model having a number of neurons as a function of the one or the multiple input variables of the input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron.

Model calculating unit and control unit for selectively calculating an RBF model, a gaussian process model and an MLP model

A system for adapting a base classifier to one or more novel classes. The base classifier classifies an instance into a base class by extracting a feature representation from the instance using a feature extractor and matching it to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel and base classes are then adapted, wherein at least one class representation of a novel class is adapted based on a class representation of a base class and at least one class representation of a base class is adapted based on a class representation of a novel class. The adapted class representations of the base and novel classes are associated with the base classifier.

Adapting a base classifier to novel classes

A model calculation unit for calculating a multilayer perceptron model, the model calculation unit being designed in hardware and being hardwired, including: a process or core; a memory; a DMA unit, which is designed to successively instruct the processor core to calculate a neuron layer, in each case based on input variables of an assigned input variable vector and to store the respectively resulting output variables of an output variable vector in an assigned data memory section, the data memory section for the input variable vector assigned to at least one of the neuron layers at least partially including in each case the data memory sections of at least two of the output variable vectors of two different neuron layers.

Model calculation unit and control unit for calculating a multilayer perceptron model with feedforward and feedback

A model calculation unit for calculating a gradient with respect to a certain input variable of input variables of a predefined input variable vector for an RBF model with the aid of a hard-wired processor core designed as hardware for calculating a fixedly predefined processing algorithm in coupled functional blocks, the processor core being designed to calculate the gradient with respect to the certain input variable for an RBF model as a function of one or multiple input variable(s) of the input variable vector of an input dimension, of a number of nodes, of length scales predefined for each node and each input dimension, and of parameters of the RBF function predefined for each node.

Model calculation unit and control unit for calculating a partial derivative of an RBF model

A method is provided for ascertaining a NOconcentration and an NHslip downstream from an SCR catalytic converter of an internal combustion engine of a vehicle. State variables of an internal combustion engine as first input variables and an updated NHfill level of the SCR catalytic converter as a second input variable cooperate with at least one machine learning algorithm or at least one stochastic model. The at least one machine learning algorithm or at least one stochastic model calculates the NOconcentration and the NHslip downstream from the SCR catalytic converter as a function of the first input variables and the second input variables and output the same as output variables.

Method for ascertaining a NOconcentration and a NHslip downstream from an SCR catalytic converter

A method for ascertaining whether a series of sensor values contains an anomaly, including the following steps: providing a shapelet and at least one training data series; measuring in each case a distance between the shapelet and the training data series at a plurality of different predefinable positions of the training data series; ascertaining at least one minimal distance from the measured distances and ascertaining at least one change variable for at least one predefinable data point of the shapelet the change variable being ascertained as a function of at least one of the measured distances. A computer program, a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored are also provided.

Method, device and computer program for ascertaining an anomaly

A method for ascertaining a time characteristic of a measured variable adjustable by an actuator, wherein a time characteristic of a control variable is applied to the actuator, wherein the ascertaining is effected by means of a Gaussian process state model of the behavior of the actuator, wherein the time characteristic of the measured variable of the actuator is ascertained on the basis of a parameterizable family of functions, wherein in the parameterizable family of functions a time dependency of a later latent state, in particular ascertained using a transfer function, of the actuator on an earlier latent state of the actuator and an earlier control variable of the actuator is the same as the applicable dependency of the Gaussian process state model.

Method for ascertaining a time characteristic of a measured variable, prediction system, actuator control system, method for training the actuator control system, training system, computer program, and machine-readable storage medium

A method for ascertaining emissions of a motor vehicle driven with the aid of an internal combustion engine in a practical driving operation. A machine learning system is trained to generate time curves of the operating variables with the aid of measured time curves of operating variables of the motor vehicle and/or of the internal combustion engine, and to then ascertain the emissions as a function of these generated time curves.

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Korntal-Muenchingen, Germany

Martin Schiegg