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    GENERATING A HYBRID SENSOR TO COMPENSATE FOR INTRUSIVE SAMPLING
    22.
    发明申请
    GENERATING A HYBRID SENSOR TO COMPENSATE FOR INTRUSIVE SAMPLING 有权

    公开(公告)号：US20210382469A1

    公开(公告)日：2021-12-09

    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 ,  G06Q50/04 ,  G05B13/02

    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.

    FEATURE REPRESENTATION BASED ON ZONE BASED DIVERSITY
    24.
    发明申请
    FEATURE REPRESENTATION BASED ON ZONE BASED DIVERSITY 有权

    公开(公告)号：US20250005442A1

    公开(公告)日：2025-01-02

    申请号：US18344836

    申请日：2023-06-29

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

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

    IPC: G06N20/00

    Abstract: A product and methodology is contemplated for monitoring a multivariate process. The product has a computer readable storage medium with program instructions embodied therewith. The program instructions are executable by a computer processor to cause the device to: segment data obtained from the multivariate process into a time series of snapshot intervals, each snapshot interval further segmented into a predetermined plurality of zone intervals; compute a contrastive metric from the segmented data for each variable during each zone interval; compare the computed contrastive metrics to one or more predetermined threshold values to define representationally relevant zone intervals for each variable; apply representation learning to derive zone-based feature vectors for each variable during the corresponding relevant zone intervals; and concatenate the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots.

    REINFORCEMENT MACHINE LEARNING WITH HYPERPARAMETER TUNING
    25.
    发明申请
    REINFORCEMENT MACHINE LEARNING WITH HYPERPARAMETER TUNING 有权

    公开(公告)号：US20240428084A1

    公开(公告)日：2024-12-26

    申请号：US18340457

    申请日：2023-06-23

    Applicant: International Business Machines Corporation

    Inventor： Elita Astrid Angelina Lobo ,  Nhan Huu Pham ,  Dharmashankar Subramanian ,  Tejaswini Pedapati

    IPC: G06N3/0985 ,  G06N3/0455

    Abstract: According to a present invention embodiment, a system for training a reinforcement learning agent comprises one or more memories and at least one processor coupled to the one or more memories. The system trains a machine learning model based on training data to generate a set of hyperparameters for training the reinforcement learning agent. The training data includes encoded information from hyperparameter tuning sessions for a plurality of different reinforcement learning environments and reinforcement learning agents. The machine learning model determines the set of hyperparameters for training the reinforcement learning agent, and the reinforcement learning agent is trained according to the set of hyperparameters. The machine learning model adjusts the set of hyperparameters based on information from testing of the reinforcement learning agent. Embodiments of the present invention further include a method and computer program product for training a reinforcement learning agent in substantially the same manner described above.

    Real-time opportunity discovery for productivity enhancement
    26.
    发明授权
    Real-time opportunity discovery for productivity enhancement 有权

    公开(公告)号：US11868932B2

    公开(公告)日：2024-01-09

    申请号：US17037850

    申请日：2020-09-30

    Applicant: International Business Machines Corporation

    Inventor： Nianjun Zhou ,  Dharmashankar Subramanian ,  WingHang Crystal Lui

    IPC: G06Q10/06 ,  G06Q10/0631 ,  G06Q10/0637 ,  G06N3/049

    CPC classification number: G06Q10/06312 ,  G06N3/049 ,  G06Q10/06375

    Abstract: 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.

    AVERAGE TREATMENT EFFECT FOR PAIRED DATA
    30.
    发明申请
    AVERAGE TREATMENT EFFECT FOR PAIRED DATA 有权

    公开(公告)号：US20230044347A1

    公开(公告)日：2023-02-09

    申请号：US17386719

    申请日：2021-07-28

    Applicant: International Business Machines Corporation

    Inventor： Debarun Bhattacharjya ,  Dharmashankar Subramanian ,  Tian GAO ,  Nicholas Scott Mattei

    IPC: G06N5/04 ,  G06K9/62

    Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.

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