公开(公告)号：US20240012726A1

公开(公告)日：2024-01-11

申请号：US18218893

申请日：2023-07-06

申请人： SAMSUNG ELECTRONICS Co., LTD.

发明人： Helik Kanti THACKER ,  Adrita BARARI ,  Akhilesh Sudhir PATANKAR ,  Deokgu YOON ,  Damini Damini ,  Keerthi Kiran JAGANNATHACHAR ,  Paulami DAS ,  Sairam JUJJARAPU ,  Sudhanshu GUPTA

IPC分类号： G06F11/22 ,  G06N3/04 ,  G06N3/092

CPC分类号： G06F11/2263 ,  G06N3/04 ,  G06N3/092

摘要： A method for repairing a memory element in a memory device by an electronic device includes configuring a memory element as a graph with a vertex and an edge, a node associated with the memory element being encoded with information related to a fault, determining, from the graph, a repair policy using a probability distribution over one or more of a faulty line and a non-faulty line as predicted by a graph neural network (GNN) based on a final node feature value from message passing stages of the GNN, and determining a value of a state using a probability of the memory element being repaired from a particular state based on a global mean of all the final node feature values predicted by the GNN.

公开(公告)号：US11650874B2

公开(公告)日：2023-05-16

申请号：US17070250

申请日：2020-10-14

申请人： UiPath, Inc.

发明人： Prabhdeep Singh

IPC分类号： G06F11/00 ,  G06F11/07 ,  G06F9/451 ,  G06N20/00 ,  G06F11/22

CPC分类号： G06F11/0793 ,  G06F9/453 ,  G06F11/079 ,  G06F11/2263 ,  G06N20/00

摘要： Anomaly detection and self-healing for robotic process automation (RPA) via artificial intelligence (AI)/machine learning (ML) is disclosed. RPA robots that utilize AI/ML models and computer vision (CV) may interpret and/or interact with most encountered graphical elements via normal learned interactions. However, such RPA robots may occasionally encounter new, unhandled anomalies where graphical elements cannot be identified and/or normal interactions will not work. Such anomalies may be processed by an anomaly handler. The RPA robots may have self-healing functionality that seeks to automatically find information that addresses anomalies.

公开(公告)号：US09965208B1

公开(公告)日：2018-05-08

申请号：US13774688

申请日：2013-02-22

申请人： Micron Technology, Inc.

发明人： Frankie F. Roohparvar ,  Luca De Santis ,  Tommaso Vali ,  Kenneth J. Eldredge

IPC分类号： G06F3/06 ,  G06F11/22 ,  G06F11/10

CPC分类号： G06F3/0634 ,  G06F3/0626 ,  G06F3/0679 ,  G06F3/0688 ,  G06F11/1064 ,  G06F11/2263

摘要： Configurable operating mode memory devices are disclosed. In at least one embodiment, a memory device is configurable into one or more operating modes. An array of memory cells can be allocated into one or more partitions where each partition is associated only with a particular mode of operation. In at least one other embodiment, a memory device is configured to store user data in a portion of a memory array and to store data corresponding to a logical function associated with a different operating mode of the memory device in a different portion of the memory array.

公开(公告)号：US20160306725A1

公开(公告)日：2016-10-20

申请号：US14687112

申请日：2015-04-15

申请人： Hamilton Sundstrand Corporation ,  The University of Connecticut

发明人： James Z. Hare ,  Shalabh Gupta ,  Nayeff A. Najjar ,  Paul M. D'Orlando ,  Rhonda Dawn Walthall

IPC分类号： G06F11/22 ,  G06F11/30 ,  G06F11/34

CPC分类号： G06F11/2263 ,  G06F11/07 ,  G06F11/3013 ,  G06F11/3058

摘要： A hierarchical fault detection and isolation system, method, and/or computer program product that facilitates fault detection and isolation in a complex networked system while reducing the computational complexity and false alarms is provided. The system, method, and/or computer program product utilizes a system level isolation and detection algorithm and a diagnostic tree to systematically isolate faulty sub-systems, components, etc. of the complex networked system.

摘要翻译： 提供了一种分层故障检测和隔离系统，方法和/或计算机程序产品，其在减少计算复杂性和虚假警报的同时便于复杂网络系统中的故障检测和隔离。 系统，方法和/或计算机程序产品利用系统级隔离和检测算法和诊断树来系统地隔离复杂网络系统的故障子系统，组件等。

公开(公告)号：US20150091594A1

公开(公告)日：2015-04-02

申请号：US14313360

申请日：2014-06-24

申请人： The United States of America as represented by the Secretary of the Navy

发明人： Brett J Hamilton

IPC分类号： G01R1/07 ,  G06N99/00 ,  G01R31/28

CPC分类号： G01R31/2834 ,  G01R1/07 ,  G01R31/002 ,  G01R31/2872 ,  G06F11/2263 ,  G06N3/02

摘要： A test system including an embodiment having a sensor array adapted to test one or more devices under test in learning modes as well as evaluation modes. An exemplary test system can collect a variety of test data as a part of a machine learning system associated with known-good samples. Data collected by the machine learning system can be used to calculate probabilities that devices under test in an evaluation mode meet a condition of interest based on multiple testing and sensor modalities. Learning phases or modes can be switched on before, during, or after evaluation mode sequencing to improve or adjust machine learning system capabilities to determine probabilities associated with different types of conditions of interest. Multiple permutations of probabilities can collectively be used to determine an overall probability of a condition of interest which has a variety of attributes.

摘要翻译： 一种测试系统，包括具有适于在学习模式下测试被测试的一个或多个设备以及评估模式的传感器阵列的实施例。 示例性测试系统可以收集各种测试数据作为与已知好样本相关联的机器学习系统的一部分。 机器学习系统收集的数据可用于计算评估模式中的被测设备基于多个测试和传感器模式满足感兴趣条件的概率。 学习阶段或模式可以在评估模式排序之前，期间或之后开启，以改善或调整机器学习系统功能，以确定与不同类型的感兴趣条件相关的概率。 概率的多重排列可以统一用于确定具有各种属性的感兴趣条件的总体概率。

公开(公告)号：US06363494B1

公开(公告)日：2002-03-26

申请号：US09624248

申请日：2000-07-24

申请人： Jeffrey Joseph Farah

发明人： Jeffrey Joseph Farah

IPC分类号： G06F1100

CPC分类号： G06F11/2263

摘要： System and methods for performing actions include detecting a particular state of a given system after the system performs various actions to transition the system from previous states to subsequent states. The system then compares detected states to expected states of the system. If a particular detected state differs from a related expected state, then one or more actions are performed to cause the system to transition from the detected state to a recovery state. The recovery actions performed are determined using one or more experience nodes storing historical recovery information.

摘要翻译： 用于执行动作的系统和方法包括在系统执行将系统从先前状态转换到后续状态的各种动作之后检测给定系统的特定状态。 然后，系统将检测到的状态与系统的预期状态进行比较。 如果特定检测状态与相关预期状态不同，则执行一个或多个动作以使系统从检测状态转换到恢复状态。 使用存储历史恢复信息的一个或多个经验节点来确定所执行的恢复动作。

公开(公告)号：US20240184678A1

公开(公告)日：2024-06-06

申请号：US17797133

申请日：2021-10-27

申请人： Hefei University of Technology

发明人： Qiang Zhang ,  Ting Huang ,  Shanlin Yang ,  Xianghong Hu ,  Chunhui Wang ,  Yuanhang Wang ,  Xiaojian Ding

IPC分类号： G06F11/22

CPC分类号： G06F11/2263

摘要： A deep learning fault diagnosis method includes the following steps: a fault diagnosis data set X is processed based on sliding window processing, to obtain a picture-like sample data set {tilde over (X)}, and obtain an attention matrix A of the picture-like sample data set {tilde over (X)}; and a 2D-CNN model is constructed to process the picture-like sample data set {tilde over (X)} to obtain a corresponding feature map F, and in the meantime, the feature map F is processed based on channel-oriented average pooling and channel-oriented maximum pooling to obtain an output P1 of the average pooling and an output P2 of the maximum pooling, and a weight matrix W is obtained based on the attention matrix A, the output P1 of the average pooling, and the output P2 of the maximum pooling, so that an output of the model is a feature map {tilde over (F)} based on an attention mechanism, where {tilde over (F)}=WF.

公开(公告)号：US11886311B2

公开(公告)日：2024-01-30

申请号：US17616663

申请日：2020-08-04

申请人： SOOCHOW UNIVERSITY

发明人： Changqing Shen ,  Xu Wang ,  Jing Xie ,  Aiwen Zhang ,  Dong Wang ,  Xiaofeng Shang ,  Dongmiao Song ,  Xingxing Jiang ,  Jun Wang ,  Juanjuan Shi ,  Weiguo Huang ,  Zhongkui Zhu

IPC分类号： G06F11/00 ,  G06F11/22 ,  G06N3/08

CPC分类号： G06F11/2263 ,  G06N3/08

摘要： The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.

公开(公告)号：US11853149B2

公开(公告)日：2023-12-26

申请号：US17471258

申请日：2021-09-10

申请人： International Business Machines Corporation

发明人： Anjali Shah ,  Jennifer A. Mallette ,  Salim Roukos

IPC分类号： G06F11/00 ,  G06F11/07 ,  G06F40/205 ,  G06F40/284 ,  G06F11/22

CPC分类号： G06F11/0769 ,  G06F11/2263 ,  G06F40/205 ,  G06F40/284

摘要： Generating error event descriptions by receiving a set of error messages associated with an error event, generating a tokenization of at least one line of the set of error messages, providing the tokenization to an attention head according to a context of the tokenization, providing an output of the attention head as input to a generative model, generating a description of the error event according to the output, and providing the description to a user.

公开(公告)号：US11789842B2

公开(公告)日：2023-10-17

申请号：US17498156

申请日：2021-10-11

申请人： Dell Products L.P.

发明人： Dale Wang ,  Min Gong ,  Ashok Narayanan Potti

IPC分类号： G06F11/00 ,  G06F11/30 ,  G06F11/07 ,  G06N5/04 ,  G06F16/33 ,  G06F11/22

CPC分类号： G06F11/3072 ,  G06F11/079 ,  G06F11/0787 ,  G06F16/3337 ,  G06N5/04 ,  G06F11/2263

摘要： Methods and systems for managing deployments are disclosed. A deployment may include one or more devices. The devices may include hardware and/or software components. The operation of the deployment may depend on the operation of these devices and components. To manage the operation of the deployment, a system may include a deployment manager. The deployment manager may obtain logs for components of the deployment reflecting the historical operation of these components and use the log to predict the future operation of the deployment. Based on the predictions, the deployment manager may take proactive action to reduce the likelihood of the deployment becoming impaired.