Abstract: Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.

Abstract: A computer-implemented method for generating reliability indication data of a computer vision model. The method includes: obtaining visual data including an input image or sequence representing an observed scene, the visual data being characterizable by a first set of visual parameters; analysing the observed scene in the visual data using a computer vision reliability model sensitive to a second set of visual parameters, the second set of visual parameters includes a subset of the first set of visual parameters, and is obtained from the first set of visual parameters according to a sensitivity analysis applied to a plurality of parameters in the first set of visual parameters, the sensitivity analysis is performed during an offline training phase of the computer vision reliability model; generating reliability indication data of the observed scene using the analysis of the observed scene; and outputting the reliability indication data of the computer vision model.

Abstract: A method for operating an injection valve by ascertaining an opening time and/or closing time of the injection valve on the basis of a sensor signal. The method includes: providing an analysis point time series by sampling a sensor signal of a sensor of the injection valve; using a nonlinear, data-based first submodel in order to obtain a first model output on the basis of the analysis point time series; using a linear, data-based second submodel in order to obtain a second model output on the basis of the analysis point time series; ascertaining the opening time and/or closing time as a function of the first and second model outputs.

Abstract: A method for operating an injection valve by determining an opening or closing time of the injection valve based on a sensor signal. The method includes: providing an evaluation point time series by sampling a sensor signal of a sensor of the injection valve; using a non-linear data-based first sub-model to obtain a first output vector based on the evaluation point time series, wherein each element of the first output vector is associated with a specific time; using a linear, data-based second sub-model to obtain a second output vector based on the evaluation point time series, wherein each element of the second output vector is associated with a specific time; limiting the time determined by the first output vector depending on the second output vector in order to obtain the opening or closing time.

Abstract: A method for training a data-based evaluation model to determine an opening or closing time of an injection valve based on a sensor signal. The method includes: measuring an operation of the injection valve in order to determine at least one sensor signal and an associated opening or closing time; sampling the sensor signal at a sampling rate in order to obtain a sensor signal time series with sensor signal values; determining a plurality of training data sets by assigning a plurality of evaluation point time series generated from a sensor signal time series to the opening or closing time associated with the sensor signal, wherein the evaluation point time series has a lower temporal resolution than the sensor signal time series; training the data-based evaluation model depending on the determined training data sets.

Abstract: A computer-implemented method determines a degree of robustness for a robustness of a provided, trained, data-based sensor model for evaluating an input dataset having at least one signal time series in order to determine a model output representing a change-point time. The method includes providing a plurality of unlabeled validation input datasets to the sensor model, and determining a plurality of robust validation input datasets of the plurality of unlabeled validation input datasets that satisfy a first robustness criterion and/or a second robustness criterion. The method further includes determining a proportion of the plurality of robust validation input datasets out of the plurality of unlabeled validation input datasets in order to obtain the degree of robustness.

Abstract: A method for operating an injection valve by ascertaining an opening time and/or closing time of the injection valve on the basis of a sensor signal. The method includes: providing an analysis point time series by sampling a sensor signal of a sensor of the injection valve; using a nonlinear, data-based first submodel in order to obtain a first model output on the basis of the analysis point time series; using a linear, data-based second submodel in order to obtain a second model output on the basis of the analysis point time series; ascertaining the opening time and/or closing time as a function of the first and second model outputs.

Abstract: A computer-implemented method for training a data-based time determining model for determining an opening or closing time of an injection valve based on a sensor signal. The method includes: providing an unlabeled analysis point time series by sampling the sensor signal of a sensor of the injection valve; training the data-based time determining model to assign a time specification which represents a specific opening or closing duration to an analysis point time series, the training process being carried out using a first shifting function to time-shift the analysis point time series and a second shifting function in order to time-shift the time specification. A consistency loss function is used for the training process.

Abstract: A method is for providing training data for training a data-based system model for operating a technical system by defining a data point determined from input variables for determining at least one output variable depending on which the technical system is operating. The method includes providing training data that are determined with a scenario other than a real operation of the technical system, the training data are defined for data points determined from the input variables, capturing operational data points determined from the input variables in real-world operation of the technical system, and splitting the training data into training data points and validation data points. The method further includes determining a k-Nearest Neighbor tree from the training data points, and determining a first distribution of distance values of distances between each of the validation data points and a predetermined number of next training data points of the training data points.

Abstract: A method trains a data-based sensor model for determining a change-point timing in at least one sensor signal of a cyclic technical process executed by a technical system. The method includes providing sensor signal time series of a sampled measured value and an associated change-point timing, and determining an evaluation signal time series by defining a regular evaluation time window over the sensor signal time series. The method further includes determining training datasets having the evaluation signal time series and the assigned change-point timing, and training the data-based sensor model with the training datasets. The change-point timing is provided as a classification vector. The classification vector encodes a time point within the evaluation time window by a position of an element of the classification vector and encodes a time point before and/or after the evaluation time window by a further position of a further element of the classification vector.

Abstract: A method determines a distance metric for determining a distance to a data point having heterogeneous classes of variables. The method includes providing training records each assigning a label to a data point, the training records partitioned into training data points of a training amount and validation data points of a validation amount, and training a data-based system model with the training amount, such that the data-based system model associates data points with a model output, respectively. The method further includes for each validation data point of the validation amount, determining a quality level of the data-based system model and a distance value to a nearest training data point for each of the heterogeneous classes of variables. The distance value to the nearest training data point is determined separately with respect to a respective class of variables.

Abstract: A method evaluates a data-based sensor model for determining a change-point time in a sensor signal time series. The method includes providing an evaluation signal time series within an evaluation time window of a sensor signal time series, and determining sensor signal extracts from the evaluation signal time series. The sensor signal extracts are (i) time-shifted with respect to one another, or (ii) respectively offset from one another by a number of sensing steps. The sensor signal extracts are shorter in length than the evaluation signal time series. The method further includes determining one or more frequency contributions from the sensor signal extracts using a fast Fourier transform (“FFT”) or a Goertzel algorithm, and evaluating the one or more frequency contributions in a trained data-based sensor model in order to determine a change-point time within the evaluation time window.

Abstract: A method evaluates a trained data-based evaluation model for determining a model output for controlling, regulating, operating, or monitoring a technical system with periodically determined input data sets. The method includes recording input data sets for a predetermined number of time-sequential scanning steps, and aggregating the input data sets into an input data package of validated input data sets. The method further includes determining an evaluation result for each of the input data sets in the input data package using the trained data-based evaluation model. Upon each evaluation, one or more model parameters of the trained data-based evaluation model are reduced by an amount or set to 0. The method is further configured to aggregate the evaluation results to obtain the model output.

Abstract: A method is for training a data-based evaluation model for determining an evaluation result. The method includes providing training data sets that assign input data sets to one or more labels, and determining a distribution interval of values of all the input data sets. The method further includes performing an initial determination of model parameters for the data-based evaluation model as a function of the distribution interval, and training the data-based evaluation model with the training data sets by further adaptation of the model parameters.

Abstract: A method is for generating training data for training a machine learning algorithm. The training data respectively include a data point and a data value associated with the data point. The method includes providing first training data for training the machine learning algorithm and approximating a manifold in which at least one part of the data points of the first training data is located. The method further includes determining a structure of the at least one part of the data points of the first training data in the manifold, and generating additional training data based on the determined structure of the at least one part of the data points of the first training data in the manifold.

Abstract: A method is for generating training data for training a machine learning algorithm. The training data respectively include a data point and a data value associated with the data point. The method includes providing first training data for training the machine learning algorithm, providing an additional data point, and approximating nearest neighbors of the additional data point based on the data points of the first training data. The method further includes determining a data value associated with the additional data point from data values associated with the nearest neighbors of the additional data point. A data pair, including the additional data point and the data value associated with the additional data point, forms additional training data.

Abstract: A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

Abstract: A computer-implemented method for ascertaining a closure point in time of an injector of an internal combustion engine using a classifier. The method includes: ascertaining a time series of input signals, each corresponding to a point in time within the time series, and each characterizing a deformation of the injector; ascertaining a plurality of first values using the classifier based on the time series, in each case a first value corresponding to a point in time of the time series, and the first value characterizing a probability that the closure point in time of the injector matches the point in time; ascertaining a plurality of second values, each being a sum of neighboring first values, of a first value and the first value, the second value corresponding to the point in time to which the first value corresponds; ascertaining the closure point in time based on the largest second value.

Abstract: A computer-implemented method for testing conformance between images generated by a synthetic image generator and images obtained from authentic visual data. A conformance test result results from comparing results of global sensitivity analyses used to rank the effect of visual parameters on the computer vision model both for synthetic and authentic visual data.

Abstract: A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.