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公开(公告)号：US20230046653A1
公开(公告)日：2023-02-16
申请号：US17879178
申请日：2022-08-02
摘要： An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.
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公开(公告)号：US20220343143A1
公开(公告)日：2022-10-27
申请号：US17641899
申请日：2020-09-10
摘要： A computer-implemented method for generating an adapted task graph, including the steps of providing a first input data set with at least one task graph and at least one task context and/or a second input data set with at least one constraint and at least one task context, generating an adapted task graph using a trained neural network based on the first input data set and/or the second input data set, and providing the adapted task graph.
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公开(公告)号：US20240143623A1
公开(公告)日：2024-05-02
申请号：US18494627
申请日：2023-10-25
IPC分类号： G06F16/27
CPC分类号： G06F16/273
摘要： To restore consistency of a digital twin database, identifiers with metadata imported from various data sources are processed by an encoder, which computes latent representations of the identifiers that are compared by an efficient similarity metric. If the respective similarity score exceeds a threshold, a match is detected between the identifiers. In that case, the digital twin database is updated by aligning the first identifier and the second identifier. This matching algorithm for equipment identifiers updates the digital twin data automatically and continuously by aligning identifiers which refer to the same piece of equipment. The updates flow directly into the digital twin database, thereby removing the manual effort. Using approximate nearest neighbor methods is highly efficient, especially for large plants. The encoder is implemented as an autoencoder which relies only on unlabeled training data. This unsupervised approach is more suitable for industrial scenarios where labeled data is expensive to create.
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公开(公告)号：US10545967B2
公开(公告)日：2020-01-28
申请号：US15513272
申请日：2014-09-25
IPC分类号： G06F16/00 , G06F16/2457 , G06F16/28 , G05B19/05
摘要： A control apparatus of an automation system, the control apparatus includes a database adapted to store time series data in a historian data source and adapted to store events derived from the time series data based on event detection rules in an event data source, wherein a semantic data or event query received by the control apparatus is mapped to a corresponding data source of the database to retrieve the queried data or event which are contextualized using an ontological context model of the automation system stored in the database and output by control apparatus in a semantic format is provided.
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公开(公告)号：US20230418802A1
公开(公告)日：2023-12-28
申请号：US18338302
申请日：2023-06-20
CPC分类号： G06F16/2282 , G06F16/211
摘要： A solution for automated column type annotation maps each column contained in a table to a column annotation class. A pre-processor transforms the table into a numerical tensor representation by outputting a sequence of cell tokens for each cell in the table. A table encoder encodes the sequences of cell tokens and a column annotation label for each column into body cell embeddings. A body pooling component processes the body cell embeddings to provide column representations. A classifier classifies the column representations to provide for each column, confidence scores for each column annotation class. The method concludes with comparing the highest confidence score for each column with a threshold, and, if the highest confidence score for each column is above the threshold, annotating each column with the respective column annotation class.
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公开(公告)号：US20220284296A1
公开(公告)日：2022-09-08
申请号：US17683757
申请日：2022-03-01
摘要： Provided is a computer implemented method for providing an agent for creating a graph neural network architecture, which is suitable for providing a prediction of at least one indicator of a complex system and to a computer implemented method for providing such a graph neural network architecture by an agent. Also provide is an agent and a unit for providing an agent a computer program product and computer readable storage media.
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公开(公告)号：US20220027849A1
公开(公告)日：2022-01-27
申请号：US17312725
申请日：2019-12-11
发明人： Dagmar Beyer , Mitchell Joblin , Lutz Lukas , Benjamin Paul , Martin Ringsquandl , Nataliia Rümmele , Amit Vaidya
IPC分类号： G06Q10/08 , G06K9/62 , G06F16/901
摘要： A computer implemented method for providing a service for a complex industrial system, the method including the steps of providing Bill of Materials, BoM, trees of system component instances of said complex industrial system; generating automatically a unified BoM data model by clustering matching nodes within the provided BoM trees; and performing the service for the complex industrial system based on the generated unified BoM data model is provided.
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公开(公告)号：US20220170976A1
公开(公告)日：2022-06-02
申请号：US17538778
申请日：2021-11-30
发明人： Martin Ringsquandl , Mitchell Joblin , Dagmar Beyer , Sebastian Weber , Sylwia Henselmeyer , Marcel Hildebrandt
IPC分类号： G01R31/08 , G01R31/333 , G06N3/04
摘要： Provided is an assistance apparatus for localizing errors in a monitored technical system consisting of devices and/or transmission lines, including at least one processor configured to obtain values of actual attributes of the devices and/or of the transmission lines, determine an error probability for each device and/or transmission line by processing a graph neural network with the obtained actual values of attributes as input, wherein the graph neural network is trained by training attributes assigned to an attributed graph representation of the technical system, and output an indication for such devices and/or transmission lines, whose error probability is higher than a predefined threshold.
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公开(公告)号：US20240281422A1
公开(公告)日：2024-08-22
申请号：US18681129
申请日：2022-08-09
发明人： Martin Ringsquandl , Mitchell Joblin , Aneta Koleva , Georgia Olympia Brikis , Rakebul Hasan , Marcel Hildebrandt , Markus Zechel
IPC分类号： G06F16/215 , G06N3/0455
CPC分类号： G06F16/215 , G06N3/0455
摘要： 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.
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公开(公告)号：US20230273573A1
公开(公告)日：2023-08-31
申请号：US18113224
申请日：2023-02-23
发明人： Marcel Hildebrandt , Serghei Mogoreanu , Mitchell Joblin , Martin Ringsquandl , Chandra Sekhar Akella
IPC分类号： G05B13/02
CPC分类号： G05B13/027
摘要： A database stores a set of items, with each item having technical attributes, and with each item representing a module that can be used in an engineering project of a first user, u1. A feature encoder embeds each item based on its technical attributes into a low-dimensional vector space. Then, in a second step, a graph neural network pools over these item embeddings to compute an updated user embedding for the first user A decoder mapping then addresses the recommendation task by outputting recommendation scores for each item. That means, heuristically speaking, that the method and system lift the recommendation task to the level of technical attributes to overcome the sparsity problem caused by item sets that are not overlapping between user groups. Thus, when matching similar users, the method does not rely on users configuring exactly the same modules but rather on configured modules that are similar from a technical point of view.
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