-
公开(公告)号：US20240333739A1
公开(公告)日：2024-10-03
申请号：US18192812
申请日：2023-03-30
发明人： Bhavna Agrawal , Robert Jeffrey Baseman , Jeffrey Owen Kephart , Anuradha Bhamidipaty , Elham Khabiri , Yingjie Li , Srideepika Jayaraman
CPC分类号： H04L63/1425 , H04L41/16
摘要： Detecting and mitigating anomalous system behavior by providing a machine learning model comprising a knowledge graph depicting system entity relationships, and modeling behavioral correlations among system entities according to historical time-series data, receiving real-time time-series data for the system, detecting an anomalous system behavior in a system locale, according to the real-time time-series data, according to the machine learning model and multivariate sensor metrics, diagnosing the anomalous system behavior according to an upstream portion of the knowledge graph and a statistical behavior model for the system locale, and mitigating the anomalous behavior by deriving a recommended action according to the anomalous behavior and generating a work order to implement the recommended action.
-
公开(公告)号：US20230128821A1
公开(公告)日：2023-04-27
申请号：US17491494
申请日：2021-09-30
发明人： Dzung Tien Phan , Lam Minh Nguyen , Jayant R. Kalagnanam , Chandrasekhara K. Reddy , Srideepika Jayaraman
IPC分类号： G06N20/10
摘要： A computer implemented method of generating a classifier engine for machine learning includes receiving a set of data points. A semi-supervised k-means process is applied to the set of data points from each class. The set of data points in a class is clustered into multiple clusters of data points, using the semi-supervised k-means process. Multi-polytopes are constructed for one or more of the clusters from all classes. A support vector machine (SVM) process is run on every pair of clusters from all classes. Separation hyperplanes are determined for the clustered classes. Labels are determined for each cluster based on the separation by hyperplanes.
-
3.发明公开公开(公告)号：US20240330756A1
公开(公告)日：2024-10-03
申请号：US18194601
申请日：2023-03-31
发明人： Dhavalkumar C. Patel , Srideepika Jayaraman , Shuxin Lin , Anuradha Bhamidipaty , Jayant R. Kalagnanam
摘要： A computer-implemented method for developing a hierarchical machine-learning pipeline can include receiving a hierarchy specification, a set of estimators for the root node, and one or more transformer options for each of the transformer nodes. The hierarchy specification provides a configuration of the root node, transformer nodes, and edges interconnecting the root and transformer nodes. A rank can be obtained for each estimator in the root node. A hierarchy pipeline traverser can then traverse a first child layer of the transformer nodes connected to the root node via one of the edges. A first ranked list of pathways can be determined with respect to the one or more transformer options selected for the first child layer and at least one selected estimator of the root node.
-
公开(公告)号：US20240047279A1
公开(公告)日：2024-02-08
申请号：US17817688
申请日：2022-08-05
发明人： Robert Jeffrey Baseman , Elham Khabiri , Anuradha Bhamidipaty , Yingjie Li , Srideepika Jayaraman , Bhavna Agrawal , Jeffrey Owen Kephart
CPC分类号： H01L22/20 , G06K9/6232 , H01L21/67253 , H01L22/10
摘要： Embodiments of the invention are directed to a computer-implemented method. A non-limiting example of the computer-implemented method includes accessing, using a processor system, a process-step sequence that includes a plurality process-steps and a plurality of queue-times. A process-step sequence mining operation is applied to the process-step sequence, wherein the process-step sequence mining operation is operable to make a prediction of an impact of a portion of the process-step sequence on a characteristic of a product generated by the process-step sequence.
-
公开(公告)号：US11599690B2
公开(公告)日：2023-03-07
申请号：US16933972
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
IPC分类号： G06F30/20 , G06F30/27 , G06F40/40 , G01N21/95 , G06F111/10
摘要： A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.
-
公开(公告)号：US12073152B2
公开(公告)日：2024-08-27
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
-
公开(公告)号：US20240161015A1
公开(公告)日：2024-05-16
申请号：US17986001
申请日：2022-11-14
发明人： Dhavalkumar C. Patel , Srideepika Jayaraman , Shuxin Lin , Anuradha Bhamidipaty , Jayant R. Kalagnanam
CPC分类号： G06N20/20 , G06F11/3495
摘要： Systems and methods for optimizing and training machine learning (ML) models are provided. In embodiments, a computer implemented method includes: performing, by a processor set, a group execution of ML pipelines using a first subset of a training data set as input data for the ML pipelines, thereby generating a trained ML model for each of the ML pipelines, wherein data transformations that are common between the ML pipelines are implemented only once to generate an output, and the output is shared between the ML pipelines during the group execution of the ML pipelines; generating, by the processor set, performance metrics for each of the trained ML models based on validation data; ranking, by the processor set, the trained ML models based on the performance metrics, thereby generating a list of ranked ML models; and outputting, by the processor set, the list of ranked ML models to a user.
-
公开(公告)号：US20220019710A1
公开(公告)日：2022-01-20
申请号：US16933972
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.
-
公开(公告)号：US20220019708A1
公开(公告)日：2022-01-20
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
-
-
-
-
-
-
-
-
搜索结果
9 条记录 (耗时 0.017 秒)
