公开(公告)号：WO2023086533A1

公开(公告)日：2023-05-19

申请号：PCT/US2022/049646

申请日：2022-11-11

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： TANG, LuAn ,  CHEN, Yuncong ,  CHENG, Wei ,  CHEN, Haifeng ,  CHEN, Zhengzhang ,  KOBAYASHI, Yuji

IPC: B60W50/02 ,  B60W50/00 ,  B60W60/00 ,  B60W40/02 ,  G07C5/08

Abstract: Systems and methods for defect detection for vehicle operations, including collecting a multiple modality input data stream from a plurality of different types of vehicle sensors, extracting one or more features from the input data stream using a grid-based feature extractor, and retrieving spatial attributes of objects positioned in any of a plurality of cells of the grid-based feature extractor. One or more anomalies are detected based on residual scores generated by each of cross attention-based anomaly detection and time-series-based anomaly detection. One or more defects are identified based on a generated overall defect score determined by integrating the residual scores for the cross attention-based anomaly detection and the time-series based anomaly detection being above a predetermined defect score threshold. Operation of the vehicle is controlled based on the one or more defects identified.

公开(公告)号：WO2020242553A1

公开(公告)日：2020-12-03

申请号：PCT/US2020/022531

申请日：2020-03-13

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： NATSUMEDA, Masanao ,  CHENG, Wei ,  CHEN, Haifeng ,  CHEN, Yuncong

IPC: G05B23/02 ,  G06F11/30 ,  G06N3/08

Abstract: Methods and systems for predicting failure in a cyber-physical system include determining a prediction index based on a comparison of input time series, from respective sensors in a cyber-physical system, to failure precursors. A failure precursor is detected in the input time series, responsive to a comparison of the prediction index to a threshold. A subset of the sensors associated with the failure precursor is determined, based on a gradient of the prediction index. A corrective action is performed responsive to the determined subset of sensors.

公开(公告)号：WO2022010731A1

公开(公告)日：2022-01-13

申请号：PCT/US2021/040081

申请日：2021-07-01

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： MIZOGUCHI, Takehiko ,  SONG, Dongjin ,  CHEN, Yuncong ,  LUMEZANU, Cristian ,  CHEN, Haifeng

IPC: G06F16/28 ,  G06F16/25 ,  G06F16/22 ,  G06F16/2458 ,  G06N3/08 ,  G06N3/04 ,  G06N20/00 ,  G06K9/0053 ,  G06K9/6215 ,  G06K9/6232 ,  G06K9/6255 ,  G06K9/6261 ,  G06K9/6277 ,  G06N3/0445

Abstract: Systems and methods for retrieving similar multivariate time series segments are provided. The systems and methods include extracting (920) a long feature vector and a short feature vector from a time series segment, converting (930) the long feature vector into a long binary code, and converting (930) the short feature vector into a short binary code. The systems and methods further include obtaining (940) a subset of long binary codes from a binary dictionary storing dictionary long codes based on the short binary codes, and calculating (950) similarity measure for each pair of the long feature vector with each dictionary long code. The systems and methods further include identifying (960) a predetermined number of dictionary long codes having the similarity measures indicting a closest relationship between the long binary codes and dictionary long codes, and retrieving (970) a predetermined number of time series segments associated with the predetermined number of dictionary long codes.

公开(公告)号：WO2021041631A1

公开(公告)日：2021-03-04

申请号：PCT/US2020/048139

申请日：2020-08-27

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： SONG, Dongjin ,  CHEN, Yuncong ,  LUMEZANU, Cristian ,  MIZOGUCHI, Takehiko ,  CHEN, Haifeng ,  ZHU, Dixian

IPC: G06N3/08 ,  G06N3/04 ,  G06T9/00 ,  G06F11/30

Abstract: A computer-implemented method for monitoring computing system status by implementing a deep unsupervised binary coding network includes receiving (41) multivariate time series data from one or more sensors associated with a system, implementing (420) a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, computing (430) a minimal distance from the binary code to historical data, and obtaining (440) a status determination of the system based on a similar pattern analysis using the minimal distance.

公开(公告)号：WO2021041144A1

公开(公告)日：2021-03-04

申请号：PCT/US2020/047171

申请日：2020-08-20

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： LUMEZANU, Cristian ,  CHEN, Yuncong ,  SONG, Dongjin ,  MIZUGUCHI, Takehiko ,  CHEN, Haifeng ,  DONG, Bo

IPC: G10L15/04 ,  G10L15/06 ,  G10L25/93 ,  G06F16/43 ,  G10L15/08

Abstract: A method is provided. Intermediate audio features are generated (610) from an input acoustic sequence. Using a nearest neighbor search, segments of the input acoustic sequence are classified (620) based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic sequence. Each segment corresponds to a respective different acoustic window. The generating step includes learning (610A) the intermediate audio features from Multi-Frequency Cepstral Component (MFCC) features extracted from the input acoustic sequence. The generating step includes dividing (610B) the same scene into the different acoustic windows having varying MFCC features. The generating step includes feeding (610E) the MFCC features of each of the different acoustic windows into respective LSTM units such that a hidden state of each respective LSTM unit is passed through an attention layer to identify feature correlations between hidden states at different time steps corresponding to different ones of the different acoustic windows.

公开(公告)号：WO2022251004A1

公开(公告)日：2022-12-01

申请号：PCT/US2022/029614

申请日：2022-05-17

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： CHEN, Zhengzhang ,  CHEN, Haifeng ,  CHEN, Yuncong

IPC: G06N3/08 ,  G06N3/04 ,  H04L41/0695 ,  H04L67/1001

Abstract: Methods and systems for detecting and responding to an anomaly include determining (404) a first system-level performance prediction using system-level statistics. A second system-level performance prediction is determined (406) using system-level statistics and service-level statistics. The first prediction to the second prediction are compared (408) to identify a discrepancy. It is determined (308) that a service corresponding to the service-level statistics is a cause of a detected failure in a distributed computing system. An action directed to the service is performed (310) responsive to the detected failure.

公开(公告)号：WO2022216792A1

公开(公告)日：2022-10-13

申请号：PCT/US2022/023620

申请日：2022-04-06

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： MIZOGUCHI, Takehiko ,  LUMEZANU, Cristian ,  CHEN, Yuncong ,  CHEN, Haifeng

IPC: G06N3/04 ,  G06F11/30 ,  G06N3/08

Abstract: Methods and systems for training a model include training a feature extraction model to extract a feature vector from a multivariate time series segment, based on a set of training data corresponding to measurements of a system in a first domain. Adapting the feature extraction model to a second domain, based on prototypes of the training data in the first domain and new time series data corresponding to measurements of the system in a second domain.

公开(公告)号：WO2021015936A1

公开(公告)日：2021-01-28

申请号：PCT/US2020/040649

申请日：2020-07-02

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： CHEN, Yuncong ,  YUAN, Hao ,  SONG, Dongjin ,  LUMEZANU, Cristian ,  CHEN, Haifeng ,  MIZOGUCHI, Takehiko

IPC: G06F16/33 ,  G06F16/35 ,  G06N3/08

Abstract: A system (200) for cross-modal data retrieval is provided that includes a neural network having a time series encoder (211) and text encoder (212) which are jointly trained using an unsupervised training method which is based on a loss function. The loss function jointly evaluates a similarity of feature vectors of training sets of two different modalities of time series and free-form text comments and a compatibility of the time series and the free-form text comments with a word-overlap-based spectral clustering method configured to compute pseudo labels for the unsupervised training method. The computer processing system further includes a database (205) for storing the training sets with feature vectors extracted from encodings of the training sets. The encodings are obtained by encoding a training set of the time series using the time series encoder and encoding a training set of the free-form text comments using the text encoder.

公开(公告)号：WO2022072772A1

公开(公告)日：2022-04-07

申请号：PCT/US2021/053078

申请日：2021-10-01

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： CHEN, Yuncong ,  CHEN, Zhengzhang ,  LUMEZANU, Cristian ,  NATSUMEDA, Masanao ,  YU, Xiao ,  CHENG, Wei ,  MIZOGUCHI, Takehiko ,  CHEN, Haifeng

IPC: G06N3/08 ,  G06N3/04 ,  G06N5/02

Abstract: A method for system metric prediction and influential events identification by concurrently employing metric logs and event logs is presented. The method includes concurrently modeling (1301) multivariate metric series and individual events in event series by a multi-stream recurrent neural network (RNN) to improve prediction of future metrics, where the multi- stream RNN includes a series of RNNs, one RNN for each metric and one RNN for each event sequence and modeling (1303) causality relations between the multivariate metric series and the individual events in the event series by employing an attention mechanism to identify target events most responsible for fluctuations of one or more target metrics.

公开(公告)号：WO2022055698A1

公开(公告)日：2022-03-17

申请号：PCT/US2021/047286

申请日：2021-08-24

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： LUMEZANU, Cristian ,  CHEN, Yuncong ,  MIZOGUCHI, Takehiko ,  SONG, Dongjin ,  CHEN, Haifeng ,  NAZAROVS, Jurijs

IPC: G06F11/30 ,  G05B23/02 ,  G06N3/08 ,  G06N5/04

Abstract: A method classifies missing labels. The method computes (320), using a neural network model trained on training data, rank-based statistics of a feature of a time series segment to attempt to select two candidate labels from the training data that the segment most likely belongs to. The method classifies(350) the segment using k-NN-based classification applied to the training data, responsive to the two candidate labels being present in the training data. The method classifies (335) the segment by hypothesis testing, responsive to only one candidate label being present in the training data. The method classifies (345) the segment into a class with higher values of the rank-based statistics from among a plurality of classes with different values of the rank-based statistics, responsive to no candidate labels being present in the training data. The method corrects (340) a prediction by an applicable one of the classifying steps by majority voting with time windows.