Siemens Aktiengesellschaft

Oracle International Corporation

Siemens Energy Global Gmbh & Co. Kg

Thomas A Runkler

Manfred Baldauf

Marc Christian Weber

Clemens Otte

Arno Arzberger

Frank Steinbacher

Per Egedal

Steffen Udluft

Volkmar Sterzing

Siegmund Düll

Mikhail Roshchin

Markus Michael Geipel

Pankaj Gupta

Alexander Hentschel

Sebastian Mittelstädt

Ralph Grothmann

Daniel Hein

Bernt Andrassy

Shounak Roychowdhury

Subburam Rajaram

Sebastian-Philipp Brandt

Gulnar Mehdi

Klaus Arthur Schmid

Klaus-Peter Hitzel

Michael Schnurbusch

Juergen Hollatz

James C Bezdek

Jonas Witt

Jürgen Baus

Michael Rock

Method and apparatus for industrial scale production of a suspension for a battery, wherein an input material is processed via ball milling in a rotating chamber of a device that is effected as a continuous process with a continuously controlled addition of the input material and with a continuously controlled delivery of the processed output material, where state parameters of the input material and process parameters of the manufacturing installation are acquired as first parameters during production of the suspension, results of laboratory analyses about the state or quality of the manufactured suspension are acquired as second parameters in a learning phase during production, the first and the second parameters are used in the learning phase for training a model for predicting the state or quality via machine learning, and where the device is open-loop or closed-loop controlled outside the learning phase via the first parameters and the trained model.

Method and arrangement for industrial scale production of a suspension for a battery

Various embodiments include a method for producing a quality test system executing a quality test model with a filter mask and a quality model to determine a quality feature of a battery cell. The system has an electrochemical impedance spectroscopic unit for capturing test data relating to the battery within a frequency range. The method includes: creating the model; and producing the system. Creating the model includes: capturing spectroscopic learning data; creating the filter mask using a first machine learning method with analysis data from part of the frequency range by consulting the filter mask and creating the model using a second machine learning method. The first and the second learning method are coupled based on the learning data. The first machine learning method creates a filter mask determining the analysis data such that the second machine learning method creates a quality model optimized with respect to maximizing the quality.

Production of a quality test system

An assistance system for assisting the planning of automation systems includes a configuration database that has configuration datasets of automation systems, where a respective configuration dataset in each case has the configuration data of a predefined automation system, includes a vectorization component for structuring and adjusting configuration datasets, where the vectorization component is configured to convert the configuration datasets of the configuration database into vectorized configuration datasets, and includes an AI component for processing the vectorized configuration datasets using artificial intelligence, where the processing of the vectorized configuration datasets by the artificial intelligence (AI) component entails utilization of a neural network that has a deep learning architecture.

Method and assistance system for assisting the planning of automation systems

An apparatus for monitoring an actuator system, a method for providing an apparatus for monitoring an actuator system, and a method for monitoring an actuator system where the has at least one actuator and at least one data output signal. An anomaly detector detects anomalies. A suppressing engine determines time periods in which a control intervention has been performed. In a resulting monitoring signal, only anomalies are indicated which do not overlap with time periods in which the control intervention has been performed resulting in less irrelevant alerts and false positives output to a human supervisor monitoring the actuator system. The apparatus for monitoring a system may be provided with a plurality of actuators that may affect one another over time. The apparatus may be applied to a system of submersible pumps, or a system of conveyor belts.

Apparatus for monitoring an actuator system, method for providing an apparatus for monitoring an actuator system and method for monitoring an actuator system

Provided is a method for the computer-assisted creation of digital rules for monitoring a technical system. In the method, an ontology is used, which contains a plurality of classes including classes of components of the technical system and classes of operating state characteristics of the technical system and contains semantic relations between the classes. By means of a user interface, a user can formulate abstract rules by means of the classes and the semantic relations from the ontology. The abstract rules are converted into concrete rules valid for the specific technical system in an automated manner. The method has the advantage that corresponding rules no longer have to be formulated individually for individual technical systems by the user. Instead, abstract rules only have to be created one time for identical or similar technical systems.

Method for the computer aided creation of digital rules for monitoring the technical system

Provided is a computer-implemented method for inter-sententially determining a semantic relationship between a first entity and a second entity in a natural language document, comprising at least the steps of:

Neural relation extraction within and across sentence boundaries

A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.

Method of computer-assisted determination of the usage of electrical energy produced by a power generation plant, particularly a renewable power generation plant

The embodiments relate to a method for the computer-aided control and/or regulation of a technical system, particularly a power generation installation. The actions to be performed in the course of regulation or control are ascertained using a numerical optimization method (e.g., a particle swarm optimization). In this case, the numerical optimization method uses a predetermined simulation model that is used to predict states of the technical system and, on the basis thereof, to ascertain a measure of quality that reflects an optimization criterion for the operation of the technical system.

Computer-aided control and/or regulation of a technical system

A decision tree clustering procedure is provided which employs a unified approach to extracting both the decision tree and (preferably fuzzy) clusters. The decision tree is built by subsequent clustering of single dimensions or features, and the choice of the winning separation is based on cluster validity. In one embodiment, the clustering employs a fuzzy c-means (FCM) model and the partition coefficient (PC) to determine the selected separations.

System and method for generating decision trees

A method and arrangement for computer-assisted determination of clusters recognizes foaming in a washing machine. The following quantities are measured during a washing process in the washing machine; a pressure in the washing machine, a temperature in the washing machine, and an amount of water in the washing machine. Application vectors are formed from the measured quantities, and fuzzy affiliation values for predetermined clusters are identified for the application vectors. A foam formation is recognized based on the fuzzy affiliation values.

Method and arrangement for computer-assisted determination of clusters for the recognition of foaming in a washing machine as well as method and arrangement for recognizing foaming in a washing machine

In an apparatus and method for computerized design of fuzzy rules from training data vectors, a membership function type is selected from a set of predetermined membership function types, and membership values for the training data vectors and center of gravity values for the membership functions are determined in alternation in an iterative method for all training data vectors and for each membership function. When an abort criterion is met, then the most recently determined membership functions, described by the center of gravity values, are employed as rules.

Method and apparatus for computerized design of fuzzy logic rules from

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Munich, Germany

Thomas A Runkler