Abstract: A method for training a target machine learning model for a target system in engineered processes and machines. A multitask Gaussian process implements a joint model of safety values of the target and auxiliary system. A new state is selected for the target system, wherein target safety values are predicted by the multitask Gaussian process.

Abstract: A method for providing training data for a machine learning (ML) model for predicting the behavior of a technical system. The method includes: generating a first data set containing simulation data for the technical system; generating a second data set containing prototype data of the technical system; generating a third data set by combining the first and second data sets; training the ML model based on the third data set; generating a fourth data set as first input data based on the third data set by maximizing an information function, to obtain a first feature combination as input data for the technical system; measuring the first feature combination for the prototype data of the technical system to obtain a fifth data set; and adding the fifth data set as output data and the generated fourth data set as first input data to the third data set as training data.

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 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 automated generation of a system for ascertaining a state variable characterizing a state of a technical system. In the method: a first model is provided, which is configured to ascertain an estimated value of the state variable from the measured variable. Measured pairs of measured variables and in each case assigned state variables are provided. Parameters characterizing the behavior of the first model are adjusted depending on the measured pairs. A machine learning system is provided, which, linked with the first model, produces an overall model configured to ascertain an overall estimated value of the state variable from the measured variable. The machine learning system is trained. An approximation of the machine learning system is ascertained from the machine learning system by means of symbolic regression. The link from the first model and symbolic regression is provided as a generated system.

Abstract: A computer-implemented method and device for determining trajectories from a set of trajectories for measurements on a technical system. A predictive model of the technical system includes a measure of uncertainty of the prediction of the predictive model, wherein the measure depends on trajectories from the set, wherein the trajectories from the set for which the measure indicates a greater or equal uncertainty than the measure for others of the trajectories from the set are determined.

Abstract: Systems and methods are provided that enable optical image stabilization and image sensor alignments. By enabling image sensors to be adjusted based on identified misalignments, users of the systems and methods disclosed herein can adjust imaging during the course of a surgery or surgical procedure, without needing to stop the surgery or surgical procedure to manually realign optical components. The optical image stabilization allows for adjustment of sensor components to compensate for shifts or other movement of imaging devices and to maintain focus on a target object. The optical image stabilization also includes digital masks that can be overlaid on an image to block distracting mask shifting.

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 filter assembly is configured to optically couple a camera head to an endoscope. The filter assembly includes a first end adapted to be removably coupled to the camera head and a second end opposite and spaced apart from the first end, the second end adapted to be removably coupled to the endoscope. The filter assembly includes an optic housing disposed between the first end and the second end, the optic housing includes an optical path length maximizer and a first window. The first window has a first surface that is angled with respect to a plane that is perpendicular to a longitudinal axis of the optic housing so as to minimize a narcissus reflection within the optic housing and an optical filter to reject laser irradiation.

Abstract: Systems and methods are provided that enable optical image stabilization and image sensor alignments. By enabling image sensors to be adjusted based on identified misalignments, users of the systems and methods disclosed herein can adjust imaging during the course of a surgery or surgical procedure, without needing to stop the surgery or surgical procedure to manually realign optical components. The optical image stabilization allows for adjustment of sensor components to compensate for shifts or other movement of imaging devices and to maintain focus on a target object. The optical image stabilization also includes digital masks that can be overlaid on an image to block distracting mask shifting.

Abstract: A method for ascertaining control model parameters of a control model of an axial piston pump is disclosed. The method models at least one control variable of an adjustment unit of the axial piston pump as a function of operating variables, wherein operating points of the axial piston pump each are characterized by operating values of the operating variables.

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: Systems and methods are provided that enable optical image stabilization and image sensor alignments. By enabling image sensors to be adjusted based on identified misalignments, users of the systems and methods disclosed herein can adjust imaging during the course of a surgery or surgical procedure, without needing to stop the surgery or surgical procedure to manually realign optical components. The optical image stabilization allows for adjustment of sensor components to compensate for shifts or other movement of imaging devices and to maintain focus on a target object. The optical image stabilization also includes digital masks that can be overlaid on an image to block distracting mask shifting.

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 filter assembly is configured to optically couple a camera head to an endoscope. The filter assembly includes a first end adapted to be removably coupled to the camera head and a second end opposite and spaced apart from the first end, the second end adapted to be removably coupled to the endoscope. The filter assembly includes an optic housing disposed between the first end and the second end, the optic housing includes an optical path length maximizer and a first window. The first window has a first surface that is angled with respect to a plane that is perpendicular to a longitudinal axis of the optic housing so as to minimize a narcissus reflection within the optic housing and an optical filter to reject laser irradiation.

Abstract: An electrical device, such as a camera controller includes a connector having a first magnetic element and a first cam surface. An electrical housing includes a housing panel having an outer surface configured to receive the connector. A target element is disposed within the electrical housing. The target element includes a second magnetic element. The outer surface includes a second cam surface configured to engage the first cam surface and translate a rotation of the connector into an axial displacement of the connector. In one aspect, a target positioning member is configured to position the target element into the first position when the connector is removed from the outer surface. The targeting positioning member is further configured to position the target element into the second position when the first cam surface engages the second cam surface.

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: A filter assembly is configured to optically couple a camera head to an endoscope. The filter assembly includes a first end adapted to be removably coupled to the camera head and a second end opposite and spaced apart from the first end, the second end adapted to be removably coupled to the endoscope. The filter assembly includes an optic housing disposed between the first end and the second end, the optic housing includes an optical path length maximizer and a first window. The first window has a first surface that is angled with respect to a plane that is perpendicular to a longitudinal axis of the optic housing so as to minimize a narcissus reflection within the optic housing and an optical filter to reject laser irradiation.

Abstract: An electrical device, such as a camera controller includes a connector having a first magnetic element and a first cam surface. An electrical housing includes a housing panel having an outer surface configured to receive the connector. A target element is disposed within the electrical housing. The target element includes a second magnetic element. The outer surface includes a second cam surface configured to engage the first cam surface and translate a rotation of the connector into an axial displacement of the connector. In one aspect, a target positioning member is configured to position the target element into the first position when the connector is removed from the outer surface. The targeting positioning member is further configured to position the target element into the second position when the first cam surface engages the second cam surface.