Abstract: In a computer-implemented method of enabling an assessment of a plurality of datasets, the datasets each includes an input datapoint and an associated output datapoint. The plurality of datasets can be part of training data or validation data of a machine-learning algorithm such as a neural network. For each dataset multiple local complexity indicators are determined for multiple neighborhoods in the input space. A data structure is constructed that keeps track of the local complexity indicators.

Abstract: A method for quality assurance of a system, which includes an example-based sub-system. In order to improve the quality assurance of the system, the method involves the example-based sub-system being created and trained on the basis of collected examples that form a set of examples. The quality assurance of the system is carried out based on a procedure model for carrying out a development process representing a plan for proceeding during the quality assurance of the system. The quality assurance of the example-based sub-system is carried out based on a quality assessment which is determined based on the set of examples.

Abstract: In a computer-implemented method of enabling an assessment of a plurality of datasets, the datasets each includes an input datapoint and an associated output datapoint. The plurality of datasets can be part of training data or validation data of a machine-learning algorithm such as a neural network. For each dataset multiple local complexity indicators are determined for multiple neighborhoods in the input space. A data structure is constructed that keeps track of the local complexity indicators.

Abstract: A method of enabling the assessment of a plurality of datasets that each include an input datapoint and an associated output datapoint. The plurality of datasets can be part of training data or validation data of a machine-learning algorithm, such as a neural network. For each dataset, the cumulative fulfillment of multiple neighborhood criteria is considered, both in the input space and in the output.

Abstract: A method and a device for processing sensor data in a vehicle, and a vehicle. A scene from the surroundings of the vehicle is detected by sensors, and corresponding sensor data are generated. Objects in the scene are recognized as a result of processing the sensor data, and corresponding object data are generated which characterize the recognized objects. In order to be able to reliably assess the reliability of the scene recognition by sensors, the processing of the sensor data, the object recognition and/or the database, the generated object data are compared with quality-assured scene data which are stored in a database and which characterize objects in the scene.

Abstract: Various embodiments include modeling a component fault tree for a circuit with an input-side and an output-side component. These include using a fault tree corresponding to a hazard for each respective component, obtaining information about the components of the circuit and a connection between components, and connecting the respective fault trees based on the circuit description. Each fault tree includes an input fault mode or a basic event and an output fault mode. The output fault mode and the input fault mode are each assigned to a component terminal. An output fault mode of the input-side component tree is connected to an input fault mode of the output-side component tree if: there is a connection between the assigned terminal of the input-side component and the output-side component and the output fault mode of the input-side component correlates to an input fault mode of the output-side component.

Abstract: A quality assurance method for an example-based system improves quality assurance by creating and training the example-based system based on collected examples forming an example set. The respective example in the example set includes an input value in an input space. A first example set including a plurality of examples and a second example set including a plurality of examples are collected. A first quality rating representing coverage of the input space by the examples in the first example set is determined based on distribution of the input values in the input space. A second quality rating representing coverage of the input space by the examples in the second example set is determined based on distribution of the input values in the input space. The first and second quality ratings are compared to one another. A computer program and a computer-readable storage medium are also provided.

Abstract: A quality assurance method for an example-based system improves quality assurance by creating and training the example-based system on the basis of collected examples that form a set of examples. A respective example in the set of examples includes an input value that is situated in an input space. A quality assessment representing a coverage of the input space by examples in the set of examples is ascertained on the basis of a distribution of the input values in the input space. A computer program and a computer-readable storage medium are also provided.

Abstract: Various embodiments include modeling a component fault tree for a circuit with an input-side and an output-side component. These include using a fault tree corresponding to a hazard for each respective component, obtaining information about the components of the circuit and a connection between components, and connecting the respective fault trees based on the circuit description. Each fault tree includes an input fault mode or a basic event and an output fault mode. The output fault mode and the input fault mode are each assigned to a component terminal. An output fault mode of the input-side component tree is connected to an input fault mode of the output-side component tree if: there is a connection between the assigned terminal of the input-side component and the output-side component and the output fault mode of the input-side component correlates to an input fault mode of the output-side component.

Abstract: A method for automatic focusing of a microscope on a predetermined object in a specimen to be examined may include a) producing a set of criteria to be satisfied for the predetermined object using at least one training image of the object, b) producing a first image of the specimen to be examined which contains the predetermined object with the microscope in a first focal position, c) ascertaining the section or sections of the first image, which in each case satisfies or satisfy the set of criteria according to step a), and defining each ascertained section as object area of the first image, d) producing further images of the specimen with the microscope in different focal positions, e) determining the optimum focal position(s) using the further images, wherein, for this purpose, in all images only the partial region or partial regions which correspond to the object area or object areas is/are evaluated, f) focusing the microscope on at least one of the optimum focal position(s) determined in step e).