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公开(公告)号：US11782812B2
公开(公告)日：2023-10-10
申请号：US17491632
申请日：2021-10-01
Applicant: NEC Laboratories America, Inc.
Inventor： Yuncong Chen , Zhengzhang Chen , Cristian Lumezanu , Masanao Natsumeda , Xiao Yu , Wei Cheng , Takehiko Mizoguchi , Haifeng Chen
CPC classification number: G06F11/3476 , G06N3/045 , G06N3/08
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 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 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.
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公开(公告)号：US20230267305A1
公开(公告)日：2023-08-24
申请号：US18161428
申请日：2023-01-30
Applicant: NEC Laboratories America, Inc.
Inventor： Takehiko Mizoguchi , Liang Tong , Wei Cheng , Haifeng Chen
Abstract: A computer implemented method is provided. The method includes jointly encoding, by a dual-channel feature extractor, a current time series segment with corresponding static statuses into a compact feature. The method further includes converting, by a binary code extractor, the compact feature into a binary code. The method also includes computing distances between the binary code and all binary codes stored in a binary code database. The method additionally includes retrieving the top relevant multivariate time series segments based on the distances.
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公开(公告)号：US11650351B2
公开(公告)日：2023-05-16
申请号：US17165515
申请日：2021-02-02
Applicant: NEC Laboratories America, Inc.
Inventor： Yanchi Liu , Jingchao Ni , Bo Zong , Haifeng Chen , Zhengzhang Chen , Wei Cheng , Denghui Zhang
IPC: G01W1/00 , G06N3/08 , G06N3/02 , G01W1/10 , G06N20/00 , G01W1/02 , G06N3/04 , G06N5/00 , G06N3/088
CPC classification number: G01W1/00 , G06N3/0454 , G06N3/08 , G01W1/02 , G01W1/10 , G01W2001/003 , G06N3/02 , G06N3/0445 , G06N3/088 , G06N5/003 , G06N20/00
Abstract: A method for employing a unified semi-supervised deep learning (DL) framework for turbulence forecasting is presented. The method includes extracting historical and forecasted weather features of a spatial region, calculating turbulence indexes to fill feature cubes, each feature cube representing a grid-based 3D region, and building an encoder-decoder framework based on convolutional long short-term memory (ConvLSTM) to model spatio-temporal correlations or patterns causing turbulence. The method further includes employing a dual label guessing component to dynamically integrate complementary signals from a turbulence forecasting network and a turbulence detection network to generate pseudo-labels, reweighing the generated pseudo-labels by a heuristic label quality detector based on KL-Divergence, applying a hybrid loss function to predict turbulence conditions, and generating a turbulence dataset including the predicted turbulence conditions.
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公开(公告)号：US11461619B2
公开(公告)日：2022-10-04
申请号：US16787820
申请日：2020-02-11
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Dongkuan Xu
Abstract: Systems and methods for implementing a spatial and temporal attention-based gated recurrent unit (GRU) for node classification over temporal attributed graphs are provided. The method includes computing, using a GRU, embeddings of nodes at different snapshots. The method includes performing weighted sum pooling of neighborhood nodes for each node. The method further includes concatenating feature vectors for each node. Final temporal network embedding vectors are generated based on the feature vectors for each node. The method also includes applying a classification model based on the final temporal network embedding vectors to the plurality of nodes to determine temporal attributed graphs with classified nodes.
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公开(公告)号：US11379284B2
公开(公告)日：2022-07-05
申请号：US16245734
申请日：2019-01-11
Applicant: NEC Laboratories America, Inc.
Inventor： Wei Cheng , Haifeng Chen , Masanao Natsumeda
Abstract: Systems and methods for fault detection in a sensor network include receiving sensor data from sensors in the sensor network with a communication device. The sensor data is analyze to determine if the sensor data is indicative of a fault with a fault detection model, the fault detection model including; predicting the sensor data with an autoencoder by encoding the sensor data and decoding encoded the sensor data, autoregressively model the sensor data with an autoregressor, combining the modeled sensor data and the predicted sensor data with a combiner to produce reconstructed sensor data, and comparing the reconstructed sensor data to the sensor data with an anomaly evaluator to determine anomalies. An anomaly classification is produced by comparing the anomalies to historical anomalies with an anomaly classifier. Faults in the sensor network are automatically mitigated with a processing device based on the anomaly classification.
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Patent Agency Ranking
公开(公告)号：US20220111836A1
公开(公告)日：2022-04-14
申请号：US17493323
申请日：2021-10-04
Applicant: NEC Laboratories America, Inc.
Inventor： LuAn Tang , Wei Cheng , Haifeng Chen , Zhengzhang Chen , Yuxiang Ren
Abstract: A method for vehicle fault detection is provided. The method includes training, by a cloud module controlled by a processor device, an entity-shared modular and a shared modular connection controller. The entity-shared modular stores common knowledge for a transfer scope, and is formed from a set of sub-networks which are dynamically assembled for different target entities of a vehicle by the shared modular connection controller. The method further includes training, by an edge module controlled by another processor device, an entity-specific decoder and an entity-specific connection controller. The entity-specific decoder is for filtering entity-specific information from the common knowledge in the entity-shared modular by dynamically assembling the set of sub-networks in a manner decided by the entity specific connection controller.
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公开(公告)号：US20220068445A1
公开(公告)日：2022-03-03
申请号：US17408769
申请日：2021-08-23
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Bo Zong , Wei Cheng , Haifeng Chen , Yinjun Wu
Abstract: A method for managing data of dialysis patients by employing a Deep Dynamic Gaussian Mixture (DDGM) model to forecast medical time series data is presented. The method includes filling missing values in an input multivariate time series by model parameters, via a pre-imputation component, by using a temporal intensity function based on Gaussian kernels and multi-dimensional correlation based on correlation parameters to be learned and storing, via a forecasting component, parameters that represent cluster centroids used by the DDGM to cluster time series for capturing correlations between different time series samples.
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公开(公告)号：US20220067535A1
公开(公告)日：2022-03-03
申请号：US17465054
申请日：2021-09-02
Applicant: NEC Laboratories America, Inc. , NEC Corporation
Inventor： LuAn Tang , Wei Cheng , Haifeng Chen , Yuji Kobayashi
Abstract: Methods and systems for training and deploying a neural network mode include training a modular encoder model using training data collected from heterogeneous system types. The modular encoder model includes layers of neural network blocks and a selectively enabled connections between neural network blocks of adjacent layers. Each neural network block includes neural network layers. The modular encoder model is deployed to a system corresponding to one of the heterogeneous system types.
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公开(公告)号：US11169865B2
公开(公告)日：2021-11-09
申请号：US16562755
申请日：2019-09-06
Applicant: NEC Laboratories America, Inc.
Inventor： Haifeng Chen , Bo Zong , Wei Cheng , LuAn Tang , Jingchao Ni
Abstract: Systems and methods for implementing heterogeneous feature integration for device behavior analysis (HFIDBA) are provided. The method includes representing each of multiple devices as a sequence of vectors for communications and as a separate vector for a device profile. The method also includes extracting static features, temporal features, and deep embedded features from the sequence of vectors to represent behavior of each device. The method further includes determining, by a processor device, a status of a device based on vector representations of each of the multiple devices.
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50.发明申请公开(公告)号：US20210248462A1
公开(公告)日：2021-08-12
申请号：US17158466
申请日：2021-01-26
Applicant: NEC Laboratories America, Inc.
Inventor： Jingchao Ni , Zhengzhang Chen , Wei Cheng , Bo Zong , Haifeng Chen
Abstract: A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.
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