公开(公告)号：US12118476B2

公开(公告)日：2024-10-15

申请号：US16963873

申请日：2018-12-20

Applicant: NEC CORPORATION

Inventor： Yoshiaki Sakae ,  Kazuhiko Isoyama ,  Takayoshi Asakura

IPC: G06N5/025 ,  G06F9/54 ,  G06F11/30

CPC classification number: G06N5/025 ,  G06F9/542 ,  G06F9/544 ,  G06F11/3051

Abstract: Based on a normal model, it is detected whether or not an event signal of a computer system is anomalous. In parallel with the normal-model-based anomaly detection, it is detected based on a rule whether or not the event signal is anomalous. Then, a final anomaly detection result is generated by performing comprehensive determination based on detection results of the normal-model-based anomaly detection and the rule-based anomaly detection.

公开(公告)号：US11288159B2

公开(公告)日：2022-03-29

申请号：US16304472

申请日：2017-05-25

Applicant: CHUBU ELECTRIC POWER COMPANY, INC. ,  NEC CORPORATION

Inventor： Motohiro Ichiba ,  Hideyuki Ishigami ,  Takayoshi Asakura ,  Tomoya Soma ,  Mayumi Takagi

IPC: G06F11/30 ,  G05B17/02 ,  G06F11/34 ,  G05B23/02

Abstract: There is provided a system model evaluation system including a system model candidate creation part configured to create a candidate(s) of a system model by changing a pattern of selecting an inter-sensor-value relationship created by using sensor values acquired from sensors arranged in a system to which the system model is directed. This system model evaluation system further includes a system model evaluation part configured to evaluate the candidate(s) of the system model by inputting predetermined evaluation data to the created candidate(s) of the system model.

公开(公告)号：US20220319709A1

公开(公告)日：2022-10-06

申请号：US17711453

申请日：2022-04-01

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

IPC: G16H50/20 ,  G16H10/60 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

公开(公告)号：US20220318626A1

公开(公告)日：2022-10-06

申请号：US17711408

申请日：2022-04-01

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen ,  Takayoshi Asakura

IPC: G06N3/08

Abstract: A method for performing dialysis event prediction by employing a meta-training strategy for model personalization includes, in a meta-training stage, generating segments from temporal records of patient dialysis data, generating, from the segments, a support set and a query set for each patient of a plurality of patients, formulating tasks for each patient in a pre-training set defined as a meta-training framework (M-DCCN), where each task includes the support set and the query set, and sending the tasks to a two-level meta-training algorithm supported training coordinator. The method further includes, in a finetuning stage, sending the M-DCCN to local machines where a finetuning dataset is collected for new patients, the finetuning dataset including a limited amount of data pertaining the new patients, fine-tuning the M-DCCN for personalization, and using the fine-tuned M-DCCN for future predictive dialysis analysis of future new patients by generating prognostic predictive scores.