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    FAULT DETECTION IN CYBER-PHYSICAL SYSTEMS
    112.
    发明申请
    FAULT DETECTION IN CYBER-PHYSICAL SYSTEMS 有权

    公开(公告)号：US20210350232A1

    公开(公告)日：2021-11-11

    申请号：US17241430

    申请日：2021-04-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Haifeng Chen ,  Wei Cheng ,  Junghwan Rhee ,  Jumpei Kamimura

    IPC: G06N3/08 ,  G06N3/04 ,  G07C5/08

    Abstract: Methods and systems for training a neural network model include processing a set of normal state training data and a set of fault state training data to generate respective normal state inputs and fault state inputs that each include data features and sensor correlation graph information. A neural network model is trained, using the normal state inputs and the fault state inputs, to generate a fault score that provides a similarity of an input to the fault state training data and an anomaly score that provides a dissimilarity of the input to the normal state training data.

    PEPTIDE-BASED VACCINE GENERATION SYSTEM
    113.
    发明申请
    PEPTIDE-BASED VACCINE GENERATION SYSTEM 有权

    公开(公告)号：US20210319847A1

    公开(公告)日：2021-10-14

    申请号：US17197166

    申请日：2021-03-10

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Wenchao Yu ,  Hans Peter Graf ,  Igor Durdanovic

    IPC: G16B15/30 ,  G16B40/00 ,  G06N3/08 ,  G06N3/04

    Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

    NEAR REAL-TIME RECONSTRUCTION USING DRONES
    119.
    发明申请
    NEAR REAL-TIME RECONSTRUCTION USING DRONES 有权

    公开(公告)号：US20210311504A1

    公开(公告)日：2021-10-07

    申请号：US17220046

    申请日：2021-04-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eugene Chai ,  Karthikeyan Sundaresan ,  Sampath Rangarajan

    IPC: G05D1/10 ,  G01S17/89 ,  G05D1/00 ,  B64C39/02

    Abstract: Systems and methods for automatically constructing a 3-dimensional (3D) model of a feature using a drone. The method includes generating a reconnaissance flight path that minimizes battery usage by the drone, and conducting a discovery flight that uses the reconnaissance flight path. The method further includes transmitting reconnaissance laser sensor data from the drone to a processing system for target identification, and selecting a target feature for 3D model construction based on the reconnaissance laser sensor data. The method further includes scanning the target feature using a laser sensor, transmitting laser sensor data for the target feature having a minimum point density from the drone to the processing system for 3D model construction, and constructing the 3D model from the minimum point density laser sensor data.

    Automated information technology system failure recommendation and mitigation
    120.
    发明授权
    Automated information technology system failure recommendation and mitigation 有权

    公开(公告)号：US11132248B2

    公开(公告)日：2021-09-28

    申请号：US16673144

    申请日：2019-11-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jianwu Xu ,  Haifeng Chen

    IPC: G06F11/07 ,  G06K9/62

    Abstract: A method for implementing automated information technology (IT) system failure recommendation and mitigation includes performing log pattern learning to automatically generate sparse time series for each log pattern for a set of classification logs corresponding to a failure, performing multivariate log time series extraction based on the log pattern learning to generate a failure signature for the set of classification logs, including representing the sparse time series as a run-length encoded sequence for efficient storage and computation, calculating a similarity distance between the failure signature for the set of classification logs and each failure signature from a failure signature model file, determining a failure label for the failure corresponding to a most similar known failure based on the similarity distance, and initiating failure mitigation based on the failure label.

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