Abstract: A computer-implemented system and method for synthesizing a controller for an under actuated robotic manipulator includes a machine learning based model having a plurality of neural network modules. Each module is configured to approximate a function related to an underactuated controller for a robotic manipulator. Parameters of each function are learned during training of the model using a loss function that satisfies one or more conditions including structure preservation, integrability and equilibrium assignment.

Abstract: A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

Abstract: Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Abstract: An auto-encoder model processes a datasets describing a physical part from a part catalogue in the form of a property co-occurrence graph is provided, and performs entity resolution and auto-completion on the co-occurrence graph in order to compute a corrected and/or completed dataset. The encoder includes a recurrent neural network and a graph attention network. The decoder contains a linear decoder for numeric values and a recurrent neural network decoder for strings. The auto-encoder model provides an automated end-to-end solution that can auto-complete missing information as well as correct data errors such as misspellings or wrong values. The auto-encoder model is capable of auto-completion for highly unaligned part specification data with missing values.

Abstract: Blocks of spatially structured information such as log files or images are processed in a training loop for an attention-based classifier, using an active learning approach. First, the classifier provides a predicted label and an attention map for each classified block. Blocks are selected from the classified blocks if the output of the classifier for the respective block meets a selection criterion. The selected blocks are then displayed to a user together with the predicted label and a visual representation of the attention map. Based on these changes, the classifier is retrained. The method allows for an automatic, intelligent selection of a small number of data points that need to be labeled by a domain expert. The domain expert does not need to collect the training data a priori, but systematically and iteratively gets asked for training examples that are then directly used by the machine learning algorithm for learning.

Abstract: System and method enable development of an industrial metaverse used in engineering by computer aided design. A metaverse pipeline includes a plurality of metaverse entities including at least one design application and one simulation application configured to develop a virtual environment metaverse representation of an industrial facility. A metaverse knowledge graph describes semantics and relationships between the metaverse pipeline entities and is configured to exchange information across the software applications of the metaverse pipeline entities of various platforms while developing the virtual environment metaverse. The metaverse knowledge graph enables synchronization across metaverse development platforms, such that updates to one platform are transferrable, whereby the virtual entities and environment can be assimilated according to new edits.

Abstract: Device, Computing Platform and Method of Analyzing Log Files of an Industrial Plant are disclosed. The method including: determining at least one block in log entries of the log files, wherein the log entries includes one or more log messages and wherein the block represents co-occurring log messages; annotating the co-occurring log messages of the block using semantic metadata, wherein the semantic metadata defines one or more message types for the co-occurring log messages, wherein the semantic metadata is indicative of at least one of a start action, an end action, a source, an anomaly, a cause and an inspect action; generating a coherent representation for the block by representing the co-occurring log messages in a graph based on the semantic metadata; and enabling detection of at least one event in the block based on a comparison the coherent representation with template representations of predefined events associated with the industrial plant.

Abstract: To create or update a digital twin modeling a physical system, data from several conjoint data sources are repeatedly extracted and semantically annotated, wherein semantic annotations of data that have been extracted from a first conjoint data source are influenced by previously extracted and semantically annotated data from at least one other conjoint data source. The semantically annotated data are used to create or update the digital twin. This provides a modular and easily expandable solution for enriching a digital twin from a plurality of conjoint data sources with heterogenous data. Processing of different data sources does not happen isolated from one another while the system remains modularly expandable to new data sources. Compared to previous approaches, instead of creating yet another stand-alone digital representation of the physical system that is not compatible with already existing solutions, these solutions are augmented with a modular data management pipeline.

Abstract: A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

Abstract: Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Abstract: A method for generating graph-structured representations of a brownfield system including collecting training data of training systems. Training data includes training pairs, with each training pair including training sensor observations and a training digital twin model. The method includes transforming the training digital twin models into training graph-structured representations. The training graph-structured representations include nodes and links. The nodes represent components of the training system and the links represent relations between the components of the training system. A graph generative model is trained to generate graph-structured representations of the brownfield system using the training sensor observations and the training graph-structured representations of the training digital twin models. Graph-structured representations of the brownfield system are generated using the trained graph generative model and sensor observations of the brownfield system.

Abstract: Provided is a Computer-implemented method for Receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; Extracting at least one additional information of each log file of the at least two log files; and Combining each log file of the at least two log files with the extracted additional information into at least two processed log tiles; wherein the at least two processed log files comply with a coherent representation. Further, the invention relates to a corresponding computer program product and generating unit.