公开(公告)号：US12164270B2

公开(公告)日：2024-12-10

申请号：US17505343

申请日：2021-10-19

Applicant: International Business Machines Corporation

Inventor： Nianjun Zhou ,  Dharmashankar Subramanian

IPC: G05B13/04 ,  G05B19/418 ,  G05B23/02

Abstract: 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.

公开(公告)号：US20240403726A1

公开(公告)日：2024-12-05

申请号：US18327156

申请日：2023-06-01

Applicant: International Business Machines Corporation

Inventor： Long Vu ,  Alexander Zadorojniy ,  Dharmashankar Subramanian

IPC: G06N20/20

Abstract: Disclosed embodiments may include a system for identifying Markov Decision Process (MDP) solutions. The system may receive input data including one or more first states and one or more first actions. The system may identify, via a machine learning model (MLM), a subset of the input data. The system may formulate, via the MLM, a search space based on the subset of the input data, the search space including one or more second states and one or more second actions. The system may conduct, via the MLM, hyperparameter tuning of the search space. The system may generate, via the MLM, an MDP instance based on the hyperparameter tuning. The system may determine, via the MLM, whether the generated MDP instance includes a first MDP solution.

公开(公告)号：US20240303536A1

公开(公告)日：2024-09-12

申请号：US18179856

申请日：2023-03-07

Applicant: International Business Machines Corporation

Inventor： Dharmashankar Subramanian ,  Nianjun Zhou

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A computer implemented method for data driven optimization. A number of processor units creates a regression model using historical data in a current neighborhood. The historical data is for a system over time. The number of processor units generates an optimization solution using the regression model created from the current neighborhood and an objective function. The number of processor units determines whether the optimization solution is within the current neighborhood. The number of processor units selects a new neighborhood containing the historical data in response to the optimization solution not being within the current neighborhood. The new neighborhood is based on the optimization solution and becomes the current neighborhood. The number of processor units repeats the creating, generating, determining, and selecting steps in response to the optimization solution not being within the current neighborhood.

公开(公告)号：US20230259830A1

公开(公告)日：2023-08-17

申请号：US17651293

申请日：2022-02-16

Applicant: International Business Machines Corporation

Inventor： Dharmashankar Subramanian ,  Nianjun Zhou ,  Pavankumar Murali

IPC: G06N20/20 ,  G06K9/62 ,  G05B17/02

CPC classification number: G06N20/20 ,  G06K9/6262 ,  G06K9/6235 ,  G05B17/02 ,  G06K2009/6237

Abstract: A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

公开(公告)号：US11422545B2

公开(公告)日：2022-08-23

申请号：US16895651

申请日：2020-06-08

Applicant: International Business Machines Corporation

Inventor： Nianjun Zhou ,  Dharmashankar Subramanian ,  Wesley M Gifford

IPC: G05B23/02 ,  G05B13/02 ,  G06Q50/04

Abstract: 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.

公开(公告)号：US20220245440A1

公开(公告)日：2022-08-04

申请号：US17162626

申请日：2021-01-29

Applicant: International Business Machines Corporation

Inventor： Dharmashankar Subramanian ,  Venkata Nagaraju Pavuluri ,  Yuan-Chi Chang ,  Long Vu ,  Timothy Rea Dinger

IPC: G06N3/08 ,  G06K9/62 ,  G06F9/38

Abstract: A method of using a computing device to train a neural network to recognize features in variate time series data that includes receiving, by a computing device, variate time series data. The computing device further receives results associated with the variate time series data. The computing device determines an anchor of the variate time series data. The computing device additionally determines one or more portions of the variate time series data which lead to a positive result. The computing device further determines one or more portions of the variate time series data which lead to a negative result. The computing device trains a neural network to interpret results of future variate time series data based upon the anchor, the one or more portions of the variate time series data which lead to the positive result, and the one or more portions of the variate time series data which lead to the negative result.

公开(公告)号：US20220245409A1

公开(公告)日：2022-08-04

申请号：US17162649

申请日：2021-01-29

Applicant: International Business Machines Corporation

Inventor： Venkata Nagaraju Pavuluri ,  Dharmashankar Subramanian ,  Yuan-Chi Chang ,  Long Vu ,  Timothy Rea Dinger

IPC: G06K9/62 ,  G06N20/00 ,  G06F17/18 ,  G06F7/24

Abstract: A method of using a computing device to determine a window size in variate time series data that includes receiving, by a computing device, variate time series data associated with a machine learning model. The computing device sets a moving window size and a standard deviation for the variate time series data. The computing device further calculates a moving window average for the variate time series data. The computing device additionally calculates a standard deviation across all variate time series data. The computing device sorts the standard deviations calculated in descending order. The computing device further iterates indices for the standard deviations until the indices have been visited by at least one anchor. The computing device iteratively expands each anchor to cover neighbors' anchors which have been visited by previous anchors. The computing device determines a window size based upon the expanded anchors.

公开(公告)号：US11263172B1

公开(公告)日：2022-03-01

申请号：US17141116

申请日：2021-01-04

Applicant: International Business Machines Corporation

Inventor： Yuan-Chi Chang ,  Venkata Nagaraju Pavuluri ,  Dharmashankar Subramanian ,  Long Vu ,  Debarun Bhattacharjya ,  Timothy Rea Dinger

IPC: G06F16/11

Abstract: A method, computer program product, and/or computer system improves a future efficiency of a specific system. One or more processors receive multiple historical data snapshots that describe past operational states of a specific system. The processor(s) identify a time series pattern for the time series of data in the multiple historical snapshots and calculate their variability. The processor(s) then determine that the variability in a first sub-set of the time series pattern is larger than a predefined value, and determine that future values of the first set of the time series pattern are a set of non-forecastable future values. The processor(s) also determine that the variability in a second sub-set of the time series pattern for the data is smaller than the predefined value, and utilizes this second sub-set to modify the specific system at a current time.

公开(公告)号：US20210110487A1

公开(公告)日：2021-04-15

申请号：US16599505

申请日：2019-10-11

Applicant: International Business Machines Corporation

Inventor： Nianjun Zhou ,  Dharmashankar Subramanian ,  Patrick Watson ,  Pavankumar Murali ,  Wesley M. Gifford ,  Jayant R. Kalagnanam

IPC: G06Q50/04 ,  G06F17/17 ,  G06Q10/06 ,  G06N3/08 ,  G05B19/418

Abstract: A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.

公开(公告)号：US20200169469A1

公开(公告)日：2020-05-28

申请号：US16199090

申请日：2018-11-23

Applicant: International Business Machines Corporation

Inventor： Debarun Bhattacharjya ,  Tian Gao ,  Dharmashankar Subramanian

IPC: H04L12/24 ,  H04L12/26 ,  H04L29/08

Abstract: A graphical event model method, system, and computer program product, include learning statistical and causal co-occurrence relationships among multiple event-types of data, requiring no complex input, and generating a representation that explains a mutual dynamic of the multiple event-types in a form of a graphical event model.