Abstract: A device and a computer-implemented method for determining a variable of a technical system, using a machine learning model. A kernel for the model is selected from a set of kernels as a function of a selection criterion, and a first data set which includes mutually assigned input variables and output variables of the technical system. The selection criterion is determined for a kernel that is selected from the set of kernels as a function of an acquisition function, the acquisition function being determined as a function of a second data set that includes pairs of kernels from the set of kernels and a selection criterion. The pairs of kernels are determined over respectively one pair of kernels from the set of kernels and as a function of the second data set. Representations of a first and second kernel are provided.

Abstract: A method for initially preparing an at least partially data-based aging state model for an electrical energy storage means is disclosed. The method includes providing a number of energy storage means on a test bench for measurement based on a respective load profile, wherein the load profiles are different and characterize a chronological trend of at least one load-imposing operational variable for the energy storage means. The method also includes operating the number of energy storage means having the respective associated load profile and recording chronological operational variable trends. Further, the method includes at a predetermined evaluation timepoint, determining an aging state of a subset of the energy storage means as a label based on an input vector, and generating a training data set, which includes the operational variable trends and the determined label, for each energy storage means of the subset of the energy storage means.

Abstract: A device and computer-implemented method for machine learning. A probabilistic model is provided, in particular a model that includes a probability distribution, preferably a Gaussian process or a Bayesian neural network, the model being defined as a function of at least one hyperparameter, in particular of the Gaussian process or of the Bayesian neural network. In one iteration, an instruction for a first measurement is determined and output as a function of the model. For the at least one hyperparameter an a posteriori distribution over values for the at least one hyperparameter being determined as a function of the first measurement. In another iteration, an instruction for a second measurement is determined and output as a function of the model. At least one value of the at least one hyperparameter is determined as a function of the second measurement.

Abstract: Active learning for operating a physical system. The method includes: providing a data set that comprises data points each comprising an input for operating the physical system, and a first and second observation of the physical system; training a multi-output Gaussian process for predicting the first observation for a given input with the data set; training a Gaussian process for predicting the second observation for a given input with the data set; determining with the data set an input for operating the physical system; determining the first and second observations that result from operating the physical system with the determined input; and adding a data point to the data set that comprises the determined input and the determined first and second observations.

Abstract: Apparatus, system and computer-implemented method of operating a technical system. A first variable of the technical system is mapped onto a prediction for a second variable of the technical system using a Gaussian process, and the technical system is operated depending on the prediction. The Gaussian process, depending on a sum of a first Gaussian process, weighted with a first weight function, is determined with a second Gaussian process. The first Gaussian process maps the first variable based onto a first prediction of the second variable, and the second Gaussian process maps the first variable onto a second prediction for the second variable. Sub-domains of a domain and/or a value range of the Gaussian process are determined depending on a numerical representation of a binary tree with leaves and nodes.

Abstract: A method for training a machine learning algorithm including uncertainties. The method includes: pre-training the algorithm based on initially collected data by a control unit in order to obtain an initial model, determining a set of channels, the data originating from channels contained in the set of channels being intended to be used for retraining the initial model, based on an established data level and on the respective influence, which the data originating from one of the channels have on uncertainties instantaneously contained in the initial model, transferring detected data originating from the individual channels of the set of channels to the control unit, and retraining of the initial model by the control unit based on the data transferred to the control unit.

Abstract: A method for training a machine learning algorithm including uncertainties. The method includes the following steps: for each point in time of the plurality of points in time, determining in each case an influence, which the data detected at the corresponding point in time have on uncertainties instantaneously contained in the initial model, for each point in time of the plurality of points in time, determining a resolution of the corresponding detected data based on an established data level and on the respective influence, which the corresponding data have on uncertainties instantaneously contained in the initial model, for each point in time of the plurality of points in time, transferring the detected data to the control unit based on the corresponding determined resolution, and retraining of the initial model by the control unit based on the data transferred to the control unit.

Abstract: A computer-implemented method for the safe, active training of a computer-aided model for modeling time series of a physical system using Gaussian processes, including the steps of establishing a safety threshold value ?; initializing by implementing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section; implementing the determined new curve section on the physical system and measuring output variables; incorporating the new output values in the regression model and the safety model until N passes have been carried out; and outputting the regression model and the safety model.

Abstract: A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.

Abstract: Training a Gaussian process state space model, which describes a correlation between selected control parameters of a plurality of control parameters for controlling a robotic device and output variables of the robotic device assigned in each case.

Abstract: A method for evaluating signals of a sensor unit including at least two sensors. The method includes reading in a first sensor value of a first of the sensors and a second sensor value of a second sensor, the first and second sensor value each representing one parameter of a substance to be measured by the sensors or a linking of the parameters. A threshold value range is read in, which maps a range of combinations of at least the first and second sensor values, which represents the presence or a value of the substance to be measured in surroundings of the first and second sensors. A combination of the read-in first and second sensor values is recognized as being outside the threshold value range. The threshold value range is changed into a changed threshold value range so that the combination is situated within the changed threshold value range.

Abstract: A device and method are disclosed for operating a fuel cell system having a fuel cell stack. Data are provided that map input variables of the fuel cell system and a position of a cell of the fuel cell stack on a voltage of the cell. A model is trained to map the input variables of the fuel cell system and the position of a cell of the fuel cell stack on a probability distribution for a prediction of a voltage of the cell. Instantaneous input variables of the fuel cell system are determined. A probability for the voltage of the cell or for a total voltage of the fuel cell stack is determined for a cell of the fuel cell stack based on the input variables using the model based on the probability distribution. A state of the fuel cell system is determined based on the probability.

Abstract: A method for assessing a state of a radio channel. The method includes: ascertaining or providing a piece of state information, which characterizes a simulated state of a spatial arrangement of components of the surroundings of the wireless communication network; ascertaining at least one prediction based on a machine-trained model, the state information being provided as an input parameter in an input section of a machine-trained model, the state information being propagated by the machine-trained model, and the at least one prediction of a piece of channel state information based on the machine-trained model, which characterizes a state of at least one radio channel between two communication modules, being provided in an output section of the machine-trained model.

Abstract: An on-board unit (OBU1; OBU2) for cooperative driving of a road user is provided. The on-board unit (OBU1; OBU2) comprises: an environment determination unit (102; 112) configured to determine traffic situation data (tsD) representing a traffic situation in which the road user participates; a communication scheme determination unit (104; 114) configured to determine at least one communication parameter (cP) in dependence on the determined traffic situation data (tsD) using a machine-learning communication model (110; 120); and a coordination unit (106; 116) configured to communicate in dependence on the at least one communication parameter (cP) with at least one further on-board unit (OBU2; OBU1) of another road user via at least one coordination message (cM) which is transmitted via a radio channel (RCH).

Abstract: A system for processing a classifier. The classifier is a Naïve Bayes-type classifier classifying an input instance into multiple classes based on multiple continuous probability distributions of respective features of the input instance and based on prior probabilities of the multiple classes. Upon receiving a removal request message identifying one or more undesired training instances, the classifier is made independent from one or more undesired training instances. To this end, for a continuous probability distribution of a feature, adapted parameters of the probability distribution are computed based on current parameters of the probability distribution and the one or more undesired training instances. Further, an adapted prior probability of a class is computed based on a current prior probability of the class and the one or more undesired training instances.

Abstract: A system for processing a model. The model provides a model output given an input instance. The model has been trained on a training dataset by iteratively optimizing an objective function including losses according to a loss function for training instances of the training dataset. Upon receiving a removal request message identifying one or more undesired training instances of the training dataset, the model is made independent from the one or more undesired training instances. To this end, the one or more undesired training instances are removed from the training dataset to obtain a remainder dataset, and an adapted model is determined for the remainder dataset. The parameters of the adapted model are first initialized based on the set of parameters of the trained model, and then iteratively adapted by optimizing the objective function with respect to the remainder dataset.

Abstract: A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.

Abstract: A computer-implemented method for the safe, active training of a computer-aided model for modeling time series of a physical system using Gaussian processes, including the steps of establishing a safety threshold value ?; initializing by implementing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section; implementing the determined new curve section on the physical system and measuring output variables; incorporating the new output values in the regression model and the safety model until N passes have been carried out; and outputting the regression model and the safety model.

Abstract: The invention relates to a method, which can be used to determine the local itinerary of a user of a variety of public means of transportation. Based on said determined itinerary then the travel cost can be equitably distributed among the individual operators of the means of transportation. The basis of the method is a comparison of the coordinates of stops of the public means of transportation with the coordinates of base stations for sending and receiving operations in mobile phone communication (GSM). Said coordinates must be determined only once and can be stored in a memory. If the coordinates of a base station into which a mobile phone is logged are close to the coordinates of a stop, it is assumed that the user of the mobile phone is located at said stop. During the travel of the user of the public means of transportation, it is detected at regular intervals—for example in intervals of 30 seconds—into what base station the mobile phone is logged.