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公开(公告)号：US11144833B2
公开(公告)日：2021-10-12
申请号：US15692070
申请日：2017-08-31
Inventor： Soon Hyun Kwon , Eun Joo Kim , Hong Kyu Park , Hyun Joong Kang , Kwi Hoon Kim , Young Min Kim , Hyun Jae Kim , Ji Hoon Bae , Se Won Oh , Jae Hak Yu , Yeon Hee Lee , Ho Sung Lee , Nae Soo Kim , Sun Jin Kim , Cheol Sig Pyo
IPC: G06F11/36 , G06F16/25 , G06F16/24 , G06N5/00 , G06F8/10 , G06F16/28 , G06N5/04 , G06F8/36 , G06Q10/06
Abstract: Provided are a data processing apparatus and method for merging and processing deterministic knowledge and non-deterministic knowledge. The data processing apparatus and method may efficiently process various real-time and large-scale data to convert the data into knowledge by merging and processing non-deterministic knowledge and also deterministic knowledge perceived by an expert. Thus, it is possible to adaptively operate in accordance with a dynamically changing application service environment by converting a conversion rule for converting collected data generated from an application service system into semantic data, a context awareness rule for perceiving context information from given information, and a user query for searching for knowledge information into knowledge and gradually augmenting the knowledge information in accordance with an application service environment.
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公开(公告)号：US20180189679A1
公开(公告)日：2018-07-05
申请号：US15859937
申请日：2018-01-02
Inventor： Hyun Joong KANG , Hyun Jae Kim , Ho Sung Lee , Soon Hyun Kwon , Kwi Hoon Kim , Young Min Kim , Eun Joo Kim , Hong Kyu Park , Ji Hoon Bae , Se Won Oh , Jae Hak Yu , Yeon Hee Lee , Nae Soo Kim , Sun Jin Kim , Seong Ik Cho , Cheol Sig Pyo
Abstract: Provided are a self-learning system and method for automatically performing machine learning (ML). The self-learning system includes a memory configured to store an ML knowledge database (DB) in which ML knowledge is stored and a program for automatically performing ML based on request information of a user, and a processor configured to execute the program stored in the memory. Here, when executing the program, the processor creates or recommends at least one workflow corresponding to the request information of the user based on the ML knowledge stored in the ML knowledge DB and generates an execution code for performing the created or recommended workflow.
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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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