公开(公告)号：US20220092402A9

公开(公告)日：2022-03-24

申请号：US16987789

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US20220019892A1

公开(公告)日：2022-01-20

申请号：US17379078

申请日：2021-07-19

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Bo Zong

IPC: G06N3/08 ,  G06N7/00 ,  G16H20/40

Abstract: A method for training a predictive model includes training a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information. An augmented model is trained to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.

公开(公告)号：US11221617B2

公开(公告)日：2022-01-11

申请号：US16653033

申请日：2019-10-15

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Jingchao Ni ,  Bo Zong ,  Wei Cheng ,  Haifeng Chen ,  LuAn Tang

IPC: G05B23/02 ,  G06N20/10 ,  G06F16/901 ,  G06K9/62 ,  G06F17/18

Abstract: Systems and methods for predicting system device failure are provided. The method includes performing graph-based predictive maintenance (GBPM) to determine a trained ensemble classification model for detecting maintenance ready components that includes extracted node features and graph features. The method includes constructing, based on testing data and the trained ensemble classification model, an attributed temporal graph and the extracted node features and graph features. The method further includes concatenating the extracted node features and graph features. The method also includes determining, based on the trained ensemble classification model, a list of prediction results of components that are to be scheduled for component maintenance.

公开(公告)号：US20210255363A1

公开(公告)日：2021-08-19

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 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US20210065278A1

公开(公告)日：2021-03-04

申请号：US16995052

申请日：2020-08-17

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  Xin Dong ,  Wei Cheng

IPC: G06Q30/06 ,  G06N20/00

Abstract: A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.

公开(公告)号：US20200089556A1

公开(公告)日：2020-03-19

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

IPC: G06F11/07 ,  G06K9/62 ,  G06N20/10 ,  G06N3/08

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.