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公开(公告)号：US20220058590A1
公开(公告)日：2022-02-24
申请号：US16998486
申请日：2020-08-20
摘要： A computer-implemented method for maintaining equipment in a geo-distributed system includes receiving, by a processor, a selection of quantities to optimize when adjusting a maintenance schedule of the geo-distributed system that includes multiple pieces of equipment that are spread over a geographical region, and wherein the maintenance schedule identifies when a set of maintenance tasks are executed at a first equipment from the geo-distributed system over a predetermined duration. The method further includes generating, by the processor, a mixed-integer linear program for optimizing the maintenance schedule using a set of predetermined constraints. The method further includes executing, by the processor, the mixed-integer linear program via a mixed-integer linear program solver. The method further includes adjusting, by the processor, the maintenance schedule by selecting only a subset of the maintenance tasks.
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公开(公告)号：US20210264290A1
公开(公告)日：2021-08-26
申请号：US16797401
申请日：2020-02-21
发明人： Pavankumar Murali , Haoran Zhu , Dung Tien Phan , Lam Nguyen
摘要： Aspects of the invention include an optimal interpretable decision tree using integer linear programming techniques. A non-limiting example computer-implemented method includes receiving, using a processor, a plurality of data inputs from a process and selecting, using the processor, a data subset from the plurality of data inputs by solving linear programming to obtain a solution. The method builds and optimizes, using the processor, an optimal decision tree based on the data subset and alerts, using the processor, a user when a prediction of the optimal decision tree is greater than a threshold value.
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公开(公告)号：US11823076B2
公开(公告)日：2023-11-21
申请号：US16939577
申请日：2020-07-27
发明人： Dung Tien Phan , Hongsheng Liu , Lam Nguyen
CPC分类号： G06N5/04 , G06F16/285 , G06N20/00
摘要： In an approach to hyperparameter optimization, one or more computer processors express a hyperparameter tuning process of a model based on a type of model, one or more dimensions of a training dataset, associated loss function of the model, and associated computational constraints of the model, comprising: identifying a set of optimal hyper-rectangles based a calculated local variability and a calculated best function value; calculating a point as a representative for each identified potentially optimal hyper-rectangle by locally searching over the identified set of potentially optimal hyper-rectangles; dividing one or more hyper-rectangles in the identified set of optimal hyper-rectangles into a plurality of smaller hyper-rectangles based on each calculated point; and calculating one or more optimal hyperparameters utilizing a globally converged hyper-rectangle from the plurality of smaller hyper-rectangles. The one or more computer processors classify one or more unknown datapoints utilizing the model associated with tuned hyperparameters.
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公开(公告)号：US11676039B2
公开(公告)日：2023-06-13
申请号：US16797401
申请日：2020-02-21
发明人： Pavankumar Murali , Haoran Zhu , Dung Tien Phan , Lam Nguyen
摘要： Aspects of the invention include an optimal interpretable decision tree using integer linear programming techniques. A non-limiting example computer-implemented method includes receiving, using a processor, a plurality of data inputs from a process and selecting, using the processor, a data subset from the plurality of data inputs by solving linear programming to obtain a solution. The method builds and optimizes, using the processor, an optimal decision tree based on the data subset and alerts, using the processor, a user when a prediction of the optimal decision tree is greater than a threshold value.
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公开(公告)号：US12099941B2
公开(公告)日：2024-09-24
申请号：US16925013
申请日：2020-07-09
发明人： Arun Kwangil Iyengar , Jeffrey Owen Kephart , Dhavalkumar C. Patel , Dung Tien Phan , Chandrasekhara K. Reddy
IPC分类号： G06Q10/00 , G06F18/214 , G06F18/22 , G06N20/20 , G06Q10/04
CPC分类号： G06Q10/04 , G06F18/214 , G06F18/22 , G06N20/20
摘要： Techniques for generating model ensembles are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received. A respective prediction accuracy of each respective model of the plurality of models is determined for a first interval of the plurality of intervals by processing labeled evaluation data using the respective model. Additionally, a model ensemble specifying one or more of the plurality of models for each of the plurality of intervals is generated, comprising selecting, for the first interval, a first model of the plurality of models based on (i) the respective prediction accuracies and (ii) at least one non-error metric.
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公开(公告)号：US20220171996A1
公开(公告)日：2022-06-02
申请号：US17109112
申请日：2020-12-01
发明人： Lam Minh Nguyen , Dung Tien Phan
摘要： A computer-implemented method for a shuffling-type gradient for training a machine learning model using a stochastic gradient descent (SGD) includes the operations of uniformly randomly distributing data samples or coordinate updates of a training data, and calculating the learning rates for a no-shuffling scheme and a shuffling scheme. A combined operation of the no-shuffling scheme and the shuffling scheme of the training data is performed using a stochastic gradient descent (SGD) algorithm. The combined operation is switched to performing only the shuffling scheme from the no-shuffling scheme based on one or more predetermined criterion; and training the machine learning models with the training data based on the combined no-shuffling scheme and shuffling scheme.
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公开(公告)号：US20210103221A1
公开(公告)日：2021-04-08
申请号：US16596732
申请日：2019-10-08
摘要： A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.
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公开(公告)号：US11656606B2
公开(公告)日：2023-05-23
申请号：US16998642
申请日：2020-08-20
发明人： Dung Tien Phan , Lam Nguyen , Pavankumar Murali , Hongsheng Liu
IPC分类号： G05B15/00 , G05B19/418 , G06N5/046 , G06N3/08 , G06N7/01
CPC分类号： G05B19/41835 , G06N3/08 , G06N5/046 , G06N7/01
摘要： Aspects of the invention include implemented method includes selecting an optimization algorithm for the control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model, in which a gradient is available. Calculating set-point variables using the optimization algorithm. Predicting an output based on the calculated set-point variables. Comparing an actual output at the processing plant to the predicted output. Suspending a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output.
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公开(公告)号：US20220057786A1
公开(公告)日：2022-02-24
申请号：US16998642
申请日：2020-08-20
发明人： Dung Tien Phan , Lam Nguyen , Pavankumar Murali , Hongsheng Liu
IPC分类号： G05B19/418 , G06N3/08 , G06N5/04 , G06N7/00
摘要： Aspects of the invention include implemented method includes selecting an optimization algorithm for the control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model, in which a gradient is available. Calculating set-point variables using the optimization algorithm. Predicting an output based on the calculated set-point variables. Comparing an actual output at the processing plant to the predicted output. Suspending a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output.
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公开(公告)号：US20220027757A1
公开(公告)日：2022-01-27
申请号：US16939577
申请日：2020-07-27
发明人： Dung Tien Phan , Hongsheng Liu , Lam Nguyen
摘要： In an approach to hyperparameter optimization, one or more computer processors express a hyperparameter tuning process of a model based on a type of model, one or more dimensions of a training dataset, associated loss function of the model, and associated computational constraints of the model, comprising: identifying a set of optimal hyper-rectangles based a calculated local variability and a calculated best function value; calculating a point as a representative for each identified potentially optimal hyper-rectangle by locally searching over the identified set of potentially optimal hyper-rectangles; dividing one or more hyper-rectangles in the identified set of optimal hyper-rectangles into a plurality of smaller hyper-rectangles based on each calculated point; and calculating one or more optimal hyperparameters utilizing a globally converged hyper-rectangle from the plurality of smaller hyper-rectangles. The one or more computer processors classify one or more unknown datapoints utilizing the model associated with tuned hyperparameters.
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