公开(公告)号：US12130841B2

公开(公告)日：2024-10-29

申请号：US16933361

申请日：2020-07-20

申请人： Adobe Inc.

发明人： Karan Aggarwal ,  Georgios Theocharous ,  Anup Rao

IPC分类号： G06F16/28 ,  G05B23/02 ,  G06F16/35 ,  G06F16/45 ,  G06F18/23213 ,  G06N3/02 ,  G06N3/044 ,  G06N3/0442 ,  G06N3/08 ,  G06V30/19 ,  G06F9/54 ,  G06N3/043 ,  G06N7/04

CPC分类号： G06F16/285 ,  G05B23/0281 ,  G06F16/35 ,  G06F16/45 ,  G06F18/23213 ,  G06N3/02 ,  G06N3/044 ,  G06N3/0442 ,  G06N3/08 ,  G06V30/19107 ,  G06F9/542 ,  G06N3/043 ,  G06N7/046

摘要： A single unified machine learning model (e.g., a neural network) is trained to perform both supervised event predictions and unsupervised time-varying clustering for a sequence of events (e.g., a sequence representing a user behavior) using sequences of events for multiple users using a combined loss function. The unified model can then be used for, given a sequence of events as input, predict a next event to occur after the last event in the sequence and generate a clustering result by performing a clustering operation on the sequence of events. As part of predicting the next event, the unified model is trained to predict an event type for the next event and a time of occurrence for the next event. In certain embodiments, the unified model is a neural network comprising a recurrent neural network (RNN) such as an Long Short Term Memory (LSTM) network.

公开(公告)号：US12045709B2

公开(公告)日：2024-07-23

申请号：US17655387

申请日：2022-03-18

申请人： MAGNA ELECTRONICS INC.

发明人： John Lu

IPC分类号： G06N3/043 ,  B60W30/00 ,  G05D1/00 ,  G06F9/50 ,  G06N7/02

CPC分类号： G06N3/043 ,  B60W30/00 ,  G05D1/0088 ,  G06F9/5088 ,  G06N7/02

摘要： A vehicular driving assistance system includes an exterior viewing camera disposed at a vehicle and viewing exterior of the vehicle. Image data captured by the camera is provided to and processed at an electronic control unit (ECU). The ECU performs processing tasks for multiple vehicle systems. The vehicular driving assistance system is operable to wirelessly upload captured image data to the cloud for processing at a remote processor. Processing tasks with a higher priority are determined at the ECU to be higher priority tasks. Responsive to determination at the ECU of a higher priority task, the vehicular driving assistance system (i) processes captured image data at the ECU for the higher priority task and (ii) uploads captured image data to the cloud for processing at the remote processor of processing at the remote processor.

公开(公告)号：US12032467B2

公开(公告)日：2024-07-09

申请号：US16542420

申请日：2019-08-16

申请人： Kabushiki Kaisha Toshiba ,  Kioxia Corporation

发明人： Mitsuhiro Kimura ,  Takahiro Takimoto ,  Akira Sugimoto ,  Kosuke Haruki ,  Masahiro Ozawa

IPC分类号： G06F11/00 ,  G06F11/07 ,  G06F11/30 ,  G06F11/34 ,  G06N3/043 ,  G06N3/08

CPC分类号： G06F11/3452 ,  G06F11/0766 ,  G06F11/302 ,  G06F11/3065 ,  G06N3/043 ,  G06N3/08

摘要： A monitoring system includes storage, and one or more processors. The storage stores at least one of first output data that is obtained from a learning model, or first statistical information that is obtained from the first output data. The processors calculate a degree of abnormality indicating a degree of change in statistical information of second output data with respect to the first statistical information, or a degree of change in the statistical information of the second output data with respect to second statistical information. The processors determine whether or not there is occurrence of an abnormality in the learning model, on the basis of the degree of abnormality. The processors output information indicating occurrence of the abnormality, in a case where occurrence of the abnormality is determined.

公开(公告)号：US11997137B2

公开(公告)日：2024-05-28

申请号：US18103760

申请日：2023-01-31

申请人： Microsoft Technology Licensing, LLC

发明人： Eleanor Catherine Quint ,  Jugal Parikh ,  Mariusz Hieronim Jakubowski ,  Nitin Kumar Goel ,  Douglas J Hines ,  Cristian Craioveanu

IPC分类号： H04L9/40 ,  G06N3/043 ,  G06N3/084

CPC分类号： H04L63/1483 ,  G06N3/043 ,  G06N3/084

摘要： Generally discussed herein are devices, systems, and methods for improving phishing webpage content detection. A method can include identifying first webpage content comprises phishing content, determining, using a reinforcement learning (RL) agent, at least one action, generating, based on the determined at least one action and the identified first webpage content, altered first webpage content, identifying that the altered first webpage content is benign, generating, based on the determined at least one action and second webpage content, altered second webpage content, and training, based on the altered second webpage content and a corresponding label of phishing, a phishing detector.

公开(公告)号：US20230252266A1

公开(公告)日：2023-08-10

申请号：US17624231

申请日：2021-03-09

申请人： CHINA THREE GORGES CORPORATION ,  CHINA INSTITUTE OF WATER RESOURCES AND HYDROPOWER RESEARCH

发明人： Hao WANG ,  Xiaohui LEI ,  Huichao DAI ,  Lingzhong KONG ,  Zhao ZHANG ,  Chao WANG ,  Heng YANG ,  Yongnan ZHU ,  Zhaohui YANG

IPC分类号： G06N3/043 ,  G06N3/08 ,  G06N3/063

CPC分类号： G06N3/043 ,  G06N3/08 ,  G06N3/063

摘要： A method for predicting and controlling a water level of a series water conveyance canal on the basis of a fuzzy neural network is disclosed. The method includes: performing the relationship between a sluice opening degree and an open canal control water level by means of a fuzzy neural network, and constructing an upstream water level controller of a coupled predictive control algorithm; solving an optimal control rate of the upstream water level controller using a gradient optimization algorithm on the basis of a control target of the upstream water level controller; and generating a control strategy by collecting actually measured water level change information and multiplying the actually measured water level change information by the optimal control rate on the basis of the solved optimal control rate, thereby fulfilling the object of predicting and controlling the water level.

公开(公告)号：US20240361202A1

公开(公告)日：2024-10-31

申请号：US18765013

申请日：2024-07-05

申请人： Pipesense, LLC

发明人： Dingding Chen ,  Michael David Nash ,  Kamy Tehranchi ,  Scott Bauer ,  Stuart Mitchell

IPC分类号： G01M3/28 ,  F17D5/02 ,  G06N3/043 ,  G06N3/0464

CPC分类号： G01M3/2815 ,  F17D5/02 ,  G06N3/043 ,  G06N3/0464

摘要： Provided herein are systems and methods to detect pipeline leaks. The systems and method can identify a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites, transfer pressure surge information obtained from at least a portion of the plurality of sensor sites to a cloud site, and determine whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information. The plurality of sensor sites collect pipeline pressure measurement data. The pressure surge information corresponds to the identified pipeline pressure surge.

公开(公告)号：US12039413B2

公开(公告)日：2024-07-16

申请号：US15707830

申请日：2017-09-18

申请人： Scianta Analytics, LLC

发明人： Michael E. Cormier ,  Earl D. Cox ,  William E. Thackrey ,  Joseph McGlynn ,  Harry Gardner

IPC分类号： G06N20/00 ,  G06F16/27 ,  G06N3/043 ,  G06N3/126 ,  G06N5/02 ,  G06N5/022 ,  G06N5/025 ,  G06N5/043 ,  G06N5/048 ,  G06N7/01 ,  G06N7/02 ,  G06N7/04 ,  G05B17/02 ,  G06F17/15 ,  G06F17/18 ,  G06F18/2411 ,  G06F18/2433

CPC分类号： G06N20/00 ,  G06F16/27 ,  G06N3/043 ,  G06N5/02 ,  G06N5/022 ,  G06N5/025 ,  G06N5/043 ,  G06N5/048 ,  G06N7/01 ,  G06N7/02 ,  G06N7/046 ,  G05B17/02 ,  G06F17/15 ,  G06F17/18 ,  G06F18/2411 ,  G06F18/2433 ,  G06N3/126

摘要： The present design is directed to a series of interconnected compute servers including a supervisory hardware node and a plurality of knowledge hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population, wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy, distribute normalized data relatively evenly across the plurality of knowledge hardware nodes, supervise algorithm execution across knowledge hardware nodes, and collate and present results of analysis of the seemingly infinite actor population.

公开(公告)号：US11881014B2

公开(公告)日：2024-01-23

申请号：US18155461

申请日：2023-01-17

申请人： Hebei University of Technology

发明人： Bin Cao ,  Jianwei Zhao ,  Xin Liu ,  Hua He ,  Yuchun Chang ,  Yun Li

IPC分类号： G06N3/08 ,  G06V10/82 ,  G06N3/082 ,  G06N3/098 ,  G06N3/043

CPC分类号： G06V10/82 ,  G06N3/043 ,  G06N3/082 ,  G06N3/098

摘要： The present invention discloses an intelligent image sensing device for sensing-computing-cloud integration based on a federated learning framework. The device comprises: intelligent image sensors, edge servers and a remote cloud, wherein the intelligent image sensor is used for perceiving and generating images, and uploading the images to the edge server; the edge server is used as a client; the remote cloud is used as a server; the clients train a convolutional fuzzy rough neural network based on the received images and the proposed federated learning framework; and the intelligent image sensors download the weight parameters of the trained convolutional fuzzy rough neural network from the clients, and classify and recognize the images based on the trained weight parameters. The present invention searches a lightweight deep learning architecture through neuroevolution, and deploys the lightweight deep learning architecture in the image sensors to automatically discriminate and analyze the perceived images.

公开(公告)号：US11852778B2

公开(公告)日：2023-12-26

申请号：US17497477

申请日：2021-10-08

申请人： BEYOND LIMITS, INC.

发明人： Azarang Golmohammadizangabad ,  Shahram Farhadi Nia

IPC分类号： G01V99/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/088 ,  G06F30/20 ,  G06N3/043 ,  G06N3/045

CPC分类号： G01V99/005 ,  G06F30/20 ,  G06N3/04 ,  G06N3/043 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088

摘要： Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. Feature vectors are generated using the neural network. The feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. A 3D populated log is generated by propagating well log values of the feature vectors across the volume. Uncertainty is quantified by generating a plurality of realizations including the 3D populated log. Each of the realizations is different and equally probable. core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.

公开(公告)号：US11763152B2

公开(公告)日：2023-09-19

申请号：US17820268

申请日：2022-08-17

申请人： BlueOwl, LLC

发明人： Kenneth J. Sanchez

IPC分类号： H03M7/00 ,  G06N3/08 ,  H03M7/30 ,  G06F16/904 ,  G06F18/214 ,  G06N3/086 ,  G06N3/10 ,  G06N3/043

CPC分类号： G06N3/08 ,  G06F16/904 ,  G06F18/2155 ,  H03M7/60 ,  G06N3/043 ,  G06N3/086 ,  G06N3/10 ,  G06N3/105

摘要： A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.