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    DISTRIBUTED MACHINE LEARNING IN EDGE COMPUTING
    3.
    发明申请
    DISTRIBUTED MACHINE LEARNING IN EDGE COMPUTING 有权

    公开(公告)号：US20230037308A1

    公开(公告)日：2023-02-09

    申请号：US17444674

    申请日：2021-08-09

    Applicant: International Business Machines Corporation

    Inventor： Dong Qin ,  Jing Du ,  Li Jia Lu ,  Lin Zhang ,  Miao Guo

    IPC: H04L12/24 ,  H04L12/26

    Abstract: Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

    CONTINUOUS PRODUCTION PROCESS OPTIMIZATION USING MACHINE LEARNING
    5.
    发明申请
    CONTINUOUS PRODUCTION PROCESS OPTIMIZATION USING MACHINE LEARNING 有权

    公开(公告)号：US20250053144A1

    公开(公告)日：2025-02-13

    申请号：US18231657

    申请日：2023-08-08

    Applicant: International Business Machines Corporation

    Inventor： Jing Du ,  Deng Xin Luo ,  Li Jia Lu ,  Miao Guo

    IPC: G05B13/04 ,  G05B13/02

    Abstract: One embodiment of the invention provides a computer-implemented method for optimization of a continuous production process. The method comprises receiving input data comprising a plurality of datasets each including one or more variables relating to a production equipment involved in the continuous production process. The method further comprises generating different prediction models based on the input data. Each of the prediction models is configured to output a target prediction relating to the production equipment. The method further comprises generating an objective optimization model based on each target prediction output from each of the prediction models. The objective optimization model comprises a deep neural network. The method further comprises generating a loss function corresponding to the objective optimization model, and optimizing weights for parameters of the prediction models using backpropagation of the deep neural network and the loss function, resulting in optimized weights for the parameters of the prediction models.

    COGNITIVE DATA OUTLIER PRE-CHECK BASED ON DATA LINEAGE
    7.
    发明申请
    COGNITIVE DATA OUTLIER PRE-CHECK BASED ON DATA LINEAGE 有权

    公开(公告)号：US20220350789A1

    公开(公告)日：2022-11-03

    申请号：US17245063

    申请日：2021-04-30

    Applicant: International Business Machines Corporation

    Inventor： Xiang Yu Yang ,  Deng Xin Luo ,  Ye Wang ,  Yu Pan ,  Zhong Fang Yuan ,  Miao Guo

    IPC: G06F16/23 ,  G06F16/28 ,  G06F16/901 ,  G06K9/62

    Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining pre-check data associated with specified data nodes; calculating outliers for each specified data node, wherein the outliers are calculated based on a unit of the pre-check data associated with each specified data node; backtracking the calculated outliers for each specified data node through an associated generating data link; selecting one or more data nodes associated with a set of largest outliers; selecting one or more data links associated with the set of largest outliers; and generating potential anomaly indications based on the one or more data nodes selected and the one or more data links selected.

    GENERATING PROGRAM ANALYSIS RULES BASED ON CODING STANDARD DOCUMENTS
    8.
    发明申请
    GENERATING PROGRAM ANALYSIS RULES BASED ON CODING STANDARD DOCUMENTS 审中-公开

    公开(公告)号：US20200301672A1

    公开(公告)日：2020-09-24

    申请号：US16358743

    申请日：2019-03-20

    Applicant: International Business Machines Corporation

    Inventor： Yu Li ,  Zi Lin Zhang ,  Yingyi Wang ,  Miao Guo ,  Yi Bin Wang ,  Yao Xing Li

    IPC: G06F8/10 ,  G06F11/36 ,  G06N3/08 ,  G06F8/73 ,  G06F8/71

    Abstract: In an approach to generating program analysis rules, one or more computer processors identify one or more unassociated code standard documents. The one or more computer processors feed the one or more unassociated code standard documents into a cognitive model, wherein the cognitive model utilizes one or more historical code standard documents based on the unassociated code standard documents and associated program analysis rules based on the unassociated code standard documents, wherein the historical code standard documents are natural language documents and the program analysis rules are programmatic. The one or more computer processors generate, based on one or more calculations by the cognitive model, one or more program analysis rules. The one or more computer processors correct one or more programmatic errors or one or more stylistic errors based on the generated one or more program analysis rules.

    Automated pressure level detection and correction
    9.
    发明授权
    Automated pressure level detection and correction 有权

    公开(公告)号：US12020126B2

    公开(公告)日：2024-06-25

    申请号：US17301565

    申请日：2021-04-08

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Deng Xin Luo ,  Miao Guo ,  Xiang Yu Yang ,  Yi Ming Wang ,  Yan Yu Zhang ,  Wen Wang

    IPC: G06N20/00 ,  E21F17/00 ,  G06N5/02 ,  G08B21/18

    CPC classification number: G06N20/00 ,  E21F17/00 ,  G06N5/02 ,  G08B21/182

    Abstract: A method, system, and computer program product for implementing automated pressure level detection and correction is provided. The method includes retrieving from sensors, measurement attributes associated with geological conditions occurring during a mining process. Subsequently, threshold levels configured to activate an alarm associated with measurement attributes exceeding the threshold levels are determined and code is executed with respect to the threshold levels. A combined threshold severity level associated with the safety threshold levels is determined and code is executed with respect to the safety threshold levels. A combined clustering level associated with the safety threshold levels is determined and a difference value between the combined threshold severity level and combined clustering level is generated. A relationship between the difference value and a threshold value is determined and automated software and hardware control systems are enabled for controlling machinery associated with the mining process resulting in operation of the machinery.

    AUTOMATED PRESSURE LEVEL DETECTION AND CORRECTION
    10.
    发明申请
    AUTOMATED PRESSURE LEVEL DETECTION AND CORRECTION 有权

    公开(公告)号：US20220327420A1

    公开(公告)日：2022-10-13

    申请号：US17301565

    申请日：2021-04-08

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Deng Xin Luo ,  Miao Guo ,  Xiang Yu Yang ,  Yi Ming Wang ,  Yan Yu Zhang ,  Wen Wang

    IPC: G06N20/00 ,  G06N5/02 ,  G08B21/18

    Abstract: A method, system, and computer program product for implementing automated pressure level detection and correction is provided. The method includes retrieving from sensors, measurement attributes associated with geological conditions occurring during a mining process. Subsequently, threshold levels configured to activate an alarm associated with measurement attributes exceeding the threshold levels are determined and code is executed with respect to the threshold levels. A combined threshold severity level associated with the safety threshold levels is determined and code is executed with respect to the safety threshold levels. A combined clustering level associated with the safety threshold levels is determined and a difference value between the combined threshold severity level and combined clustering level is generated. A relationship between the difference value and a threshold value is determined and automated software and hardware control systems are enabled for controlling machinery associated with the mining process resulting in operation of the machinery.

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