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公开(公告)号：US20220058240A9
公开(公告)日：2022-02-24
申请号：US16987734
申请日：2020-08-07
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Jingchao Ni , Dongkuan Xu , Wenchao Yu
Abstract: A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.
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公开(公告)号：US11204602B2
公开(公告)日：2021-12-21
申请号：US16433206
申请日：2019-06-06
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Masanao Natsumeda
Abstract: Systems and methods for early anomaly prediction on multi-variate time series data are provided. The method includes identifying a user labeled abnormal time period that includes at least one anomaly event. The method also includes determining a multi-variate time series segment of multivariate time series data that occurs before the user labeled abnormal time period, and treating, by a processor device, the multi-variate time series segment to include precursor symptoms of the at least one anomaly event. The method includes determining instance sections from the multi-variate time series segment and determining at least one precursor feature vector associated with the at least one anomaly event for at least one of the instance sections based on applying long short-term memory (LSTM). The method further includes dispatching predictive maintenance based on the at least one precursor feature vector.
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公开(公告)号：US20210350636A1
公开(公告)日：2021-11-11
申请号：US17241481
申请日：2021-04-27
Applicant: NEC Laboratories America, Inc.
Inventor： LuAn Tang , Haifeng Chen , Wei Cheng , Junghwan Rhee , Jumpei Kamimura
IPC: G07C5/08 , G06N3/04 , G06N3/08 , B60W50/02 , G07C5/00 , B60W50/035 , B60W50/038
Abstract: Methods and systems for vehicle fault detection include collecting operational data from sensors in a vehicle. The sensors are associated with vehicle sub-systems. The operational data is processed with a neural network to generate a fault score, which represents a similarity to fault state training scenarios, and an anomaly score, which represents a dissimilarity to normal state training scenarios. The fault score is determined to be above a fault score threshold and the anomaly score is determined to be above an anomaly score threshold to detect a fault. A corrective action is performed responsive the fault, based on a sub-system associated with the fault.
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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
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.
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公开(公告)号：US20210133080A1
公开(公告)日：2021-05-06
申请号：US17072526
申请日：2020-10-16
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Shuchu Han , Haifeng Chen
Abstract: Methods and systems for detecting and responding to anomalous system behavior include detecting an anomaly in a cyber-physical system, based on a classification of time series information, from sensors that monitor the cyber-physical system, as being anomalous. A transition rule is extracted from the time series information to characterize a cause of the anomalous behavior, using a temporal gradient boosting tree. A corrective action is performed responsive to the detected anomaly, prioritized by the cause of the anomalous behavior.
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Patent Agency Ranking
公开(公告)号：US20210103706A1
公开(公告)日：2021-04-08
申请号：US17060850
申请日：2020-10-01
Applicant: NEC Laboratories America, Inc.
Inventor： Wenchao Yu , Bo Zong , Wei Cheng , Haifeng Chen , Xiusi Chen
Abstract: Methods and systems for performing a knowledge graph task include aligning multiple knowledge graphs and performing a knowledge graph task using the aligned multiple knowledge graphs. Aligning the multiple knowledge graphs includes updating entity representations based on representations of neighboring entities within each knowledge graph, updating entity representations based on representations of entities from different knowledge graphs, and learning machine learning model parameters to align the multiple knowledge graphs, based on the updated entity representations.
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公开(公告)号：US20210067558A1
公开(公告)日：2021-03-04
申请号：US17004547
申请日：2020-08-27
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Haifeng Chen , Bo Zong , LuAn Tang , Wei Cheng
Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.
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公开(公告)号：US10402289B2
公开(公告)日：2019-09-03
申请号：US15661625
申请日：2017-07-27
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Guofei Jiang , Jingchao Ni
Abstract: A computer-implemented method for diagnosing system faults by fine-grained causal anomaly inference is presented. The computer-implemented method includes identifying functional modules impacted by causal anomalies and backtracking causal anomalies in impaired functional modules by a low-rank network diffusion model. An invariant network and a broken network are inputted into the system, the invariant network and the broken network being jointly clustered to learn a degree of broken severities of different clusters as a result of fault propagations.
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公开(公告)号：US20190130212A1
公开(公告)日：2019-05-02
申请号：US16169184
申请日：2018-10-24
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Kenji Yoshihira , Wenchao Yu
Abstract: Methods and systems for embedding a network in a latent space include generating a representation of an input network graph in the latent space using an autoencoder model and generating a representation of a set of noise samples in the latent space using a generator model. A discriminator model discriminates between the representation of the input network graph and the representation of the set of noise samples. The autoencoder model, the generator model, and the discriminator model are jointly trained by minimizing a joint loss function that includes parameters for each model. A final representation of the input network graph is generated using the trained autoencoder model.
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30.发明申请公开(公告)号：US20180307994A1
公开(公告)日：2018-10-25
申请号：US15888472
申请日：2018-02-05
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen
CPC classification number: G06N5/048 , G06F17/16 , G06F17/30958 , G06N99/005
Abstract: A system identifies multiple causal anomalies in a power plant having multiple system components. The system includes a processor. The processor constructs an invariant network model having (i) nodes, each representing a respective system component and (ii) invariant links, each representing a stable component interaction. The processor constructs a broken network model having (i) the invariant network model nodes and (ii) broken links, each representing an unstable component interaction. The processor ranks causal anomalies in node clusters in the invariant network model to obtain anomaly score results. The processor generates, using a joint optimization clustering process applied to the models, (i) a model clustering structure and (ii) broken cluster scores. The processor performs weighted fusion ranking on the anomaly score results and broken cluster scores, based on the clustering structure and implicated degrees of severity of any abnormal system components, to identify the multiple causal anomalies in the power plant.
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