公开(公告)号：US12263849B2

公开(公告)日：2025-04-01

申请号：US17961169

申请日：2022-10-06

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Yuxiang Ren

IPC: B60W40/09 ,  G06F18/25 ,  G06N3/084

Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

公开(公告)号：US20250005373A1

公开(公告)日：2025-01-02

申请号：US18749887

申请日：2024-06-21

Applicant: NEC Laboratories America, Inc.

Inventor： Junxiang Wang ,  Wei Cheng ,  LuAn Tang ,  Haifeng Chen

IPC: G06N3/096 ,  G06N3/0455 ,  G06N3/084

Abstract: Systems and methods are provided for adapting a model trained from multiple source time-series domains to a target time-series domain, including integrating input data from source time-series domains to pretrain a model with a set of domain-invariant representations, fine-tuning the model by learning prompts specific to each source time-series domain using data from the source time-series domains, and applying instance normalization and segmenting the time-series data into subseries-level normalized patches for the target time-series domain. The normalized patches are fed into a transformer encoder to generate high-dimensional representations of the normalized patches, and a limited number of samples from the target time-series domain are utilized to learn the prompt specific to the target domain. Cosine similarity between the prompt of the target domain and the prompts of source domains is calculated to identify a nearest neighbor prompt, which is utilized for model prediction in the target time-series domain.

公开(公告)号：US20240104344A1

公开(公告)日：2024-03-28

申请号：US18467069

申请日：2023-09-14

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Peng Yuan ,  Yuncong Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Jiafan He

IPC: G06N3/0442

CPC classification number: G06N3/0442

Abstract: Methods and systems for training a model include distinguishing hidden states of a monitored system based on condition information. An encoder and decoder are generated for each respective hidden state using forward and backward autoencoder losses. A hybrid hidden state is determined for an input sequence based on the hidden states. The input sequence is reconstructed using the encoders and decoders and the hybrid hidden state. Parameters of the encoders and decoders are updated based on a reconstruction loss.

公开(公告)号：US11842271B2

公开(公告)日：2023-12-12

申请号：US17003112

申请日：2020-08-26

Applicant: NEC Laboratories America, Inc.

Inventor： Yanchi Liu ,  Wei Cheng ,  Bo Zong ,  LuAn Tang ,  Haifeng Chen ,  Denghui Zhang

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/049

CPC classification number: G06N3/08 ,  G06N3/049

Abstract: Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.

公开(公告)号：US20230280739A1

公开(公告)日：2023-09-07

申请号：US18173452

申请日：2023-02-23

Applicant: NEC Laboratories America, Inc.

Inventor： Peng Yuan ,  LuAn Tang ,  Haifeng Chen ,  Motoyuki Sato

IPC: G05B23/02

CPC classification number: G05B23/0283

Abstract: Methods and systems for anomaly detection include training an anomaly detection histogram model using historical categorical value data. Training the anomaly detection histogram model includes generating a histogram template based on historical categorical data, converting the historical categorical data to a histogram using the histogram template, and determining a normal range and anomaly threshold for the categorical data using the histogram.

公开(公告)号：US20230152791A1

公开(公告)日：2023-05-18

申请号：US17984413

申请日：2022-11-10

Applicant: NEC Laboratories America, Inc.

Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Haifeng Chen ,  Zhengzhang Chen ,  Yuji Kobayashi

IPC: G05B23/02 ,  G06N20/00

CPC classification number: G05B23/0221 ,  G06N20/00 ,  G05B23/0235 ,  G05B23/0237

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：US20230112441A1

公开(公告)日：2023-04-13

申请号：US17961169

申请日：2022-10-06

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Haifeng Chen ,  Yuji Kobayashi ,  Yuxiang Ren

IPC: B60W40/09 ,  G06K9/62

Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

公开(公告)号：US11297082B2

公开(公告)日：2022-04-05

申请号：US16535521

申请日：2019-08-08

Applicant: NEC Laboratories America, Inc.

Inventor： Junghwan Rhee ,  LuAn Tang ,  Zhengzhang Chen ,  Chung Hwan Kim ,  Zhichun Li ,  Ziqiao Zhou

IPC: H04L29/06 ,  G05B19/418

Abstract: A computer-implemented method for implementing protocol-independent anomaly detection within an industrial control system (ICS) includes implementing a detection stage, including performing byte filtering using a byte filtering model based on at least one new network packet associated with the ICS, performing horizontal detection to determine whether a horizontal constraint anomaly exists in the at least one network packet based on the byte filtering and a horizontal model, including analyzing constraints across different bytes of the at least one new network packet, performing message clustering based on the horizontal detection to generate first cluster information, and performing vertical detection to determine whether a vertical anomaly exists based on the first cluster information and a vertical model, including analyzing a temporal pattern of each byte of the at least one new network packet.

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.

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.