International Business Machines Corporation

Dharmashankar Subramanian

Nianjun Zhou

Pu Huang

Wesley M Gifford

Debarun Bhattacharjya

Pavankumar Murali

Peter Kenneth Malkin

Ying Liu

Xin Zhou

Mark N Wegman

Jeffrey Owen Kephart

Pawan Raghunath Chowdhary

Jayant R Kalagnanam

Fan Jing Meng

Yuan-Chi Chang

Evelyn Duesterwald

Maryam Ashoori

Jie Xu

Paul Michael Matchen

Soumyadip Ghosh

Bonnie Kathryn Ray

Patrick D Watson

Clay Edwin Williams

Tamir Klinger

Thomas Robert Ervolina

Murray Robert Cantor

Peri L Tarr

Long H Vu

Dzung Tien Phan

Alan Jonathan King

Shun Xiang Yang

Venkata Nagaraju Pavuluri

Tian Gao

Timothy Rea Dinger

Lianjun An

Stanley M Sutton

Ching-Hua Chen-Ritzo

Ban Kawas

Janusz Marecki

Long Vu

Fei Fang

Paul Price

Josephine Schweiloch

WingHang Crystal Lui

Karan Manoj Samel

A computer-implemented method of discovering a composite durational event structure through temporal logic includes identifying a plurality of temporally related atomic events from temporal data trajectories of a multivariate dataset according to a definition of an atomic event predicate. At least one composite event having a durational event structure of at least some of the plurality of the temporally related atomic events is discovered by machine learning. An action is performed that is selected from a predetermined list associated with the composite event.

Composite event estimation through temporal logic

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.

Automated model predictive control using a regression-optimization framework for sequential decision making

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.

Continous monitoring, advance alerts, and control of key performance indicator variables

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.

Process optimization with joint-level inflow model

A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.

Prediction and operational efficiency for system-wide optimization of an industrial processing system

In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.

Real-time opportunity discovery for productivity enhancement

Triggering a prioritized alert and provisioning an action may include receiving historical data associated with a set of projects, the historical data spanning multiple consecutive time periods. A hierarchical data structure is generated that includes occurrences of performance factors in the historical data. Based on the hierarchical data structure, Bayesian scores associated with the performance factors are derived, the Bayesian scores representing likelihood of the performance factors occurring in a given project. The performance factors are ranked based on the Bayesian scores. Based on ranking, an alert and an action may be automatically triggered.

Microcontroller for triggering prioritized alerts and provisioned actions

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.

Generating mode change alerts with automatic detection from sensor data

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.

Generating a hybrid sensor to compensate for intrusive sampling

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.

Proximal graphical event model of statistical learning and causal discovery with event datasets

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.

Modifying a particular physical system according to future operational states

Methods and systems for determining a reallocation of resources are described. A device may determine initial allocation data that indicates a first amount of resources allocated to a plurality of areas. The device may determine a set of attacker expected rewards based on the initial allocation data. The device may determine a set of defender expected rewards based on the attacker expected rewards. The device may determine moving rewards indicating defensive scores in response to movement of the resources among the plurality of areas. The device may determine defender response rewards indicating defensive scores resulting from an optimal attack on the plurality of areas. The device may generate reallocation data indicating an allocation of a second amount of resources to the plurality of areas. The second amount of resources may maximize the moving rewards and the defender response rewards.

Resource reallocation based on expected rewards

A system, computer program product, and method is described to provide a visualization tool which portrays the certain equivalent for one or more hypothetical (i.e. synthetic) or real probability distributions p(m), and optionally allows the user to manipulate that representation, resulting in associated changes to the underlying utility function u(m). In a first example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the probability distribution p(m), for a fixed utility function u(m). In a second example, the risk preference visualization tool allows one to explore how the certain equivalent depends upon the utility function u(m), assuming one or more fixed probability distributions p(m), p(m), etc. In a third example, the decision maker can provide feedback through the visualization tool that would cause their utility function to be modified.

Dual interactive visualization system for sensitivity analysis to risk preferences for decision support

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.

Predicting likelihood of on-time product delivery, diagnosing issues that threaten delivery, and exploration of likely outcome of different solutions

Adapting an existing portfolio optimizer to support one or more valuated dependencies without modifying the existing portfolio optimizer, may include translating one or more original elements and associated dependencies in a portfolio to be optimized based on said one or more valuated dependencies; invoking the existing portfolio optimizer with the translated one or more original elements and associated dependencies; and translating optimization results, if said optimization results contain translated one or more original elements, into a solution characterized in terms of said one or more original elements.

Non-intrusively adapting existing portfolio optimizer for valuated dependencies

Growing community of inventors

Stamford, CT, United States of America

Dharmashankar Subramanian