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11.发明申请公开(公告)号：US20230123421A1
公开(公告)日：2023-04-20
申请号：US17503557
申请日：2021-10-18
摘要： A computer system, computer program product, and computer-implemented method are provided that includes learning a tree ordered graphical event model from an event dataset. Temporal relationships between one or more events in received temporal event data is modeled, and an ordered graphical event model (OGEM) graph is learned. The learned OGEM graph is configured to capture ordinal historical dependence. Leveraging the learned OGEM graph, a parameter sharing architecture is learned, including order dependent statistical and causal co-occurrence relationships among event types. A control signal to an operatively coupled event device that is associated with at least one event type reflected in the learned parameter sharing environment is dynamically issued. The control signal is configured to selectively control an event injection.
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公开(公告)号：US20230119440A1
公开(公告)日：2023-04-20
申请号：US17505343
申请日：2021-10-19
IPC分类号： G05B13/04 , G05B19/418 , G05B23/02
摘要： One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.
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公开(公告)号：US20220011760A1
公开(公告)日：2022-01-13
申请号：US16923148
申请日：2020-07-08
摘要： Techniques for model fidelity monitoring and regeneration for manufacturing process decision support are described herein. Aspects of the invention include determining that an output of a regression model corresponding to a current time period of decision support for a manufacturing process is not within a predefined range of a historical process dataset, wherein the regression model was constructed based on the historical process dataset, and performing an accuracy and fidelity analysis on the regression model based on process data from the manufacturing process corresponding to a previous time period. Based on a result of the accuracy and fidelity analysis being below a threshold, a mismatch of the regression model as compared to the manufacturing process is determined. Based on determining the mismatch, a temporary regression model corresponding to the manufacturing process is generated, and decision support for the manufacturing process is performed based on the temporary regression model.
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公开(公告)号：US20210383194A1
公开(公告)日：2021-12-09
申请号：US16894970
申请日：2020-06-08
摘要： A computer-implemented method is presented for learning relationships between multiple event types by employing a multi-channel neural graphical event model (MCN-GEM). The method includes receiving, by a computing device, time-stamped, asynchronous, irregularly spaced event epochs, generating, by the computing device, at least one fake epoch between each inter-event interval, wherein fake epochs represent negative evidence, feeding the event epochs and the at least one fake epoch into long short term memory (LSTM) cells, computing hidden states for each of the event epochs and the at least one fake epoch, feeding the hidden states into spatial and temporal attention models, and employing an average attention across all event epochs to generate causal graphs representing causal relationships between all the event epochs.
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公开(公告)号：US20190340548A1
公开(公告)日：2019-11-07
申请号：US16401146
申请日：2019-05-02
发明人： Debarun Bhattacharjya , Jeffrey O. Kephart , Jesus M. Rios Aliaga , Danny Soroker , Dharmashankar Subramanian , Ruben Rodriguez Torrado
摘要： A system, method and program product for analyzing long term risk. A system is disclosed that includes a risk system for analyzing long-term risks, including: a risk knowledgebase that includes risk information associated with at least one domain; a risk model builder that builds a representation of a risk model based on inputs from a user interface and the risk knowledgebase, wherein the risk model includes risk factor nodes, risk event nodes and impact nodes; and a risk simulation engine that processes the representation and computes predicted outcomes.
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公开(公告)号：US20230316150A1
公开(公告)日：2023-10-05
申请号：US17708834
申请日：2022-03-30
CPC分类号： G06N20/20 , G06K9/6227
摘要： A method includes training, by one or more processing devices, a plurality of machine learning predictive models, thereby generating a plurality of trained machine learning predictive models. The method further includes generating, by the one or more processing devices, a solved machine learning optimization model, based at least in part on the plurality of trained machine learning predictive models. The method further includes outputting, by the one or more processing devices, one or more control input and predicted outputs based at least in part on the solved machine learning optimization model.
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17.发明申请公开(公告)号：US20220260977A1
公开(公告)日：2022-08-18
申请号：US17177434
申请日：2021-02-17
IPC分类号： G05B19/418 , G01F15/075 , G01F15/06
摘要： Embodiments of the invention are directed to collecting, by a computer system, sensor data of a manufacturing system, the sensor data being measured at intervals smaller than a time interval of a target measurement of the manufacturing system. The sensor data is determined to have a relationship to the target measurement. A synthetic target measurement is generated at an interval smaller than the time interval based on the relationship. An advance warning is automatically generated for the target measurement based on the synthetic target measurement within the interval smaller than the time interval.
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公开(公告)号：US20220037020A1
公开(公告)日：2022-02-03
申请号：US16942842
申请日：2020-07-30
发明人： Debarun Bhattacharjya , Tian Gao , Nicholas Scott Mattei , Karthikeyan Shanmugam , Dharmashankar Subramanian , Kush Raj Varshney
摘要： Analyzing complex systems by receiving labeled event data describing events occurring in association with a complex system, generating a first machine learning model according to the distribution of labeled event data, receiving state variable transition data describing state variable transitions occurring in association with a complex system, training a second machine learning model according to a combination of a distribution of state variable transitions and the first machine learning model, and using the second machine learning model to predict the effects of events upon state variables within the complex system according to new state variable transition and new labeled event data.
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公开(公告)号：US20210382469A1
公开(公告)日：2021-12-09
申请号：US16895651
申请日：2020-06-08
摘要： A hybrid sensor can be generated by training a machine learning model, such as a neural network, based on a training data set. The training data set can include a first time series of upstream sensor data having forward dependence to a target variable, a second time series of downstream sensor data having backward dependence to the target variable and a time series of measured target variable data associated with the target variable. The target variable has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data and the downstream sensor data. The hybrid sensor can estimate a value of the target variable at a given time, for example, during which no actual measured target variable value is available.
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20.发明申请公开(公告)号：US20130325763A1
公开(公告)日：2013-12-05
申请号：US13906490
申请日：2013-05-31
发明人： Murray R. Cantor , Evelyn Duesterwald , Tamir Klinger , Peter K. Malkin , Paul M. Matchen , Dharmashankar Subramanian , Stanley M. Sutton , Peri L. Tarr , Mark N. Wegman
IPC分类号： G06Q10/06
CPC分类号： G06Q10/063114 , G06Q10/06 , G06Q10/06312 , G06Q10/06313 , G06Q10/0633 , G06Q10/0635
摘要： A task effort estimator may determine a probability distribution of an estimated effort needed to complete unfinished tasks in a project based on one or more of a set of completed tasks belonging to a project and attributes associated with the completed tasks belonging to the project, a set of completed tasks not belonging to the project and attributes associated with the completed tasks not belonging to the project, or the combination of both. A project completion predictor may determine a probability distribution of completion time for the project based on the probability distribution of an estimated effort needed to complete the unfinished tasks in the project, and one or more resource and scheduling constraints associated with the project.
摘要翻译： 任务工作估计器可以基于属于项目的一组完成的任务中的一个或多个项目以及与属于项目的完成任务相关联的属性来确定在项目中完成未完成任务所需的估计努力的概率分布，集合 不属于项目的已完成任务和与完成的任务不属于项目相关联的属性，或两者的组合。 项目完成预测器可以基于完成项目中未完成任务所需的估计努力的概率分布以及与项目相关联的一个或多个资源和调度约束来确定项目完成时间的概率分布。
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