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1.发明申请公开(公告)号：US20200007409A1
公开(公告)日：2020-01-02
申请号：US16456518
申请日：2019-06-28
Inventor： Kwi Hoon Kim , Wan Seon Lim , Yong Geun Hong
Abstract: An intelligent Internet of everything (IoE) edge computing system for a high reliable Internet of thins (IoT) service is provided. The intelligent IoE edge computing system for high reliable IoT services according to the present invention provides a modularized intelligent IoT framework for various applications and has a technical feature in that intelligent traffic analysis and prediction is performed.
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公开(公告)号：US11153388B2
公开(公告)日：2021-10-19
申请号：US17095610
申请日：2020-11-11
Inventor： Yeon Hee Lee , Hyun Joong Kang , Young Min Kim , Tae Hwan Kim , Hyun Jae Kim , Hoo Young Ahn , Tae Wan You , Ho Sung Lee , Wan Seon Lim , Cheol Sig Pyo
Abstract: Provided is an engine container configured by a workflow engine framework for a cross-domain extension, including a plurality of operators configured to interwork with a plurality of representational state transfer (REST) application programming interfaces (APIs), respectively, a runner configured to sequentially call the plurality of operators according to a request from a client, and a controller configured to control an operation of the plurality of operators and the runner, wherein each operator operates in a pipeline manner to call a corresponding REST API using uniform resource locator (URL) information transferred from the runner and to transfer a processing result obtained by processing data provided through the corresponding called REST API to a next operator.
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公开(公告)号：US10565699B2
公开(公告)日：2020-02-18
申请号：US15950408
申请日：2018-04-11
Applicant: Electronics and Telecommunications Research Institute , KOREA ATOMIC ENERGY RESEARCH INSTITUTE
Inventor： Ji Hoon Bae , Gwan Joong Kim , Se Won Oh , Doo Byung Yoon , Wan Seon Lim , Kwi Hoon Kim , Nae Soo Kim , Sun Jin Kim , Cheol Sig Pyo
Abstract: Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.
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公开(公告)号：US20180293723A1
公开(公告)日：2018-10-11
申请号：US15950408
申请日：2018-04-11
Applicant: Electronics and Telecommunications Research Institute , KOREA ATOMIC ENERGY RESEARCH INSTITUTE
Inventor： Ji Hoon BAE , Gwan Joong Kim , Se Won Oh , Doo Byung Yoon , Wan Seon Lim , Kwi Hoon Kim , Nae Soo Kim , Sun Jin Kim , Cheol Sig Pyo
CPC classification number: G06T7/0004 , G06K9/6231 , G06K9/6245 , G06K9/6262 , G06K9/629 , G06N3/126 , G06N20/00 , G06T2200/04 , G06T2207/20081 , G06T2207/20084 , G06T2207/30136
Abstract: Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections.
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