Abstract: Control method, control system, computer-implemented method for determining a predicted weight value of a product produced by an injection molding device and a computer-implemented method for training a machine learning (ML) via an ML method, wherein the trained ML model is configured to determine the predicted weight value of the product produced via the injection molding device, where the method comprises recording and/or determining first production parameters of the injection molding device during production of a first product, recording and/or determining predecessor production parameters of the injection molding device during production of at least one predecessor product and each predecessor weight value of the at least one predecessor product, recording and/or determining a first weight value for the first product, and training the ML model, via a supervised learning method, with the first product parameters, further product parameters, at least one predecessor weight value, and the first weight value

Abstract: A process for controlling a conveyor line for general cargo is provided, the conveyor line including a plurality of consecutive conveyor line portions, each of which is driven by a drive. The drives are controlled by a computing unit using a machine learning model. The machine learning model accomplishes this by getting first input data on the basis of current operating information from at least one further conveyor line that it does not control. The machine learning model has previously been trained using second input data on the basis of operating information of the at least one further conveyor line. The operating information of the at least one further conveyor line in this instance relates to measured values from sensors for detecting general cargo and speeds of conveyor line portions.

Abstract: A process for controlling a conveyor line for general cargo, the conveyor line including a plurality of consecutive conveyor line portions , each of which is driven by a drive. One or more sensors for detecting general cargo are located on at least some of the conveyor line portions. The drives are controlled by means of a computing unit using a machine learning model. The machine learning model accomplishes this by repeatedly receiving input data including a vector of a fixed length, each vector element being associated with a section of the conveyor line and indicating a current proportional occupancy of the respective section by an item of general cargo. Each conveyor line portion is split into a plurality of the sections of identical size. An apparatus or a system for data processing, a computer program, a computer-readable data carrier and a data carrier signal is also provided.

Abstract: Using the example of a logistics system including a plurality of parallel conveyor lines for piece goods, which each lead to a combining unit in the conveying direction, it is provided how the temporally and spatially extremely complex control of such an industrial installation can be simulated with the aid of neural networks such that the temporal and spatial dependences are also reliably identified by the neural network. This is effected by digital stopwatches which are applied to the neural network in addition to sensor data from the logistics system and are reset to an initial value whenever motion detectors indicate the passage of a package.

Abstract: In order to configure a control device, a predefined default configuration data set is read in. Furthermore, a deviation from the default configuration data set as well as a control performance are determined for each of a large number of generated test configuration data sets. In addition, a Pareto optimization is performed for the large number of test configuration data sets, wherein the deviation as well as the control performance are used as Pareto objective criteria. A configuration data set resulting from the Pareto optimization is then selected to configure the control device.

Abstract: To protect against the theft of a machine learning module predicting sensor signals, the machine learning module is trained, on the basis of a timeseries of a sensor signal, to predict a later signal value of the sensor signal as first output signal and to output a scatter width of the predicted later signal value as second output signal. The machine learning module is expanded, and the expanded machine learning module is transferred to a user. When an input signal is supplied, a first and a second output signal are derived from the input signal. The checking module then checks whether a later signal value of the input signal lies outside a scatter width indicated by the second output signal by a signal value indicated by the first output signal. An alarm signal is output depending on the check result, if later signal values lie outside the scatter width.

Abstract: A computer-implemented method for configuring a system model and a computer-implemented method for configuring a sensor model. There is also described a computer-implemented method for determining future switching behavior of a system unit, with the following steps: a) receiving the configured system model; b) receiving the configured sensor model, c) the configured sensor model being a probability distribution regarding how the sensor unit will behave in the specific time period; d) establishing at least one random sample of behavior of a sensor unit by sampling from the probability distribution; and e) determining the future switching behavior of the system unit and/or at least one associated statistical value on the basis of the established random sample by means of the trained system model. There is also described a corresponding computer program product.

Abstract: A computer-implemented method for determining at least one remaining time value, to be determined, for a system is provided, having the following steps: a. providing at least one known input data record containing a multiplicity of input elements for at least one determined time; b. providing at least one associated known remaining time value for the at least one input data record; c. determining the at least one remaining time value to be determined by applying an error function to the at least one input data record and the at least one associated remaining time value; and d. providing an output data record containing the at least one determined remaining time value and an associated reliability value. The invention furthermore targets a corresponding determination unit and computer program product.

Abstract: Real state information, which influences the switching times of a light signal system, is supplied as input signals to a first neural network in a fixed time cycle. The first neural network calculates estimated state information as a replacement for real state information or parts of the real state information which are not received in good time or are received incorrectly in the fixed time cycle. This estimated state information is output to artificial intelligence which predicts the switching times. The first neural network allows the artificial intelligence to also make good predictions for the switching times of signal groups when one of the many communication paths involved fails or is overloaded. It is therefore possible to predict signal group states in the fixed time cycle in real time with a high degree of robustness and tolerance with respect to gaps in the time cycle of the real state information provided.

Abstract: A method predicts a switch state and/or a switch time point of a signaling system. The method includes collecting first and second state data, the first and second state data influencing the switch state and/or switch time point. The collection of the first state data includes reading of state data from a signaling system control device of the signaling system by a signaling system interface. The collection of the second state data includes reading in of the state data. A prediction model is provided and configured to make a prediction of the switch time point and/or the switch state of the signaling system based on first and second state data. The switch state and/or the switch time point of the signaling system is predicted via the prediction model using the first and second state data. The predicted switch state and/or switch time point of the signaling system is outputted.

Abstract: A method for configuration of a controlled drive application of a logistics system. The logistics system includes parallel conveying paths for piece goods. Each conveying path includes sub-conveying paths which are each accelerated or delayed to merge the piece goods on a single output conveying path with defined spacing. A system model of the logistics system is firstly determined by operating data of the logistics system which include sensor values of the logistics system and changes to control variables. A control function is determined, which includes configuration data for the drives, with at least one control action being performed on the precondition of one or more performance features that are to be achieved in the system model, during which control action the operating data is simulated for a plurality of time steps.

Abstract: A method and a device for ascertaining control parameters in a computer-assisted manner for handling a technical system is provided. A starting state and the surroundings of the technical system are detected using at least one sensor, and a physical simulation model of the technical system is generated using same. On the basis of the starting state, different combinations of handling steps of the technical system are simulated with respect to a specified target state using the simulation model, wherein control parameters of the technical system for carrying out the handling steps are varied. The simulation data is used to train a machine learning routine by an evaluation of each handling step, and the trained machine learning routine is used to ascertain an optimized combination of handling steps. The control parameters of the optimized combination of handling steps are output to control the technical system is also provided.