INTEGRATED LEARNING SYSTEM AND METHOD OF CLIENT SEVER LINKED WITH CLOUDING SEVER
The present technology discloses an integrated learning system and method of a client server interlocking with a cloud server. A specific implementation example for the present technology performs data standardization and preprocessing after checking a characteristic of sensor data received in a clo...
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creator | KIM CHANGWOO KIM HYEONG GOO CHAE CHULSEOUNG KANG JEONG HOON |
description | The present technology discloses an integrated learning system and method of a client server interlocking with a cloud server. A specific implementation example for the present technology performs data standardization and preprocessing after checking a characteristic of sensor data received in a cloud server, matches a learning model of a client server and a neural network of the cloud server that matches a preregistered model parameter, enables each learning model of a plurality of client servers to be each integrate-trained according to training of the learning model of the client server, and enables the learning model to be universally applied to a lightweight device according to a reduction of resource usage amount and process amount of the client server. The integrated learning system comprises: a plurality of client servers; and one cloud server.
본 기술은 클라우드 서버와 연동하는 클라이언트 서버의 통합 학습 시스템 및 방법이 개시된다. 이러한 본 기술에 대한 구체적인 구현 예는 클라우드 서버에서 수신된 센서 데이터의 특성을 확인한 다음 데이터 표준화 및 전처리를 수행하고 기 등록된 모델 파라미터와 매칭되는 클라이언트 서버의 학습 모델과 클라우드 서버의 신경망을 매칭시켜 클라이언트 서버의 학습 모델을 훈련함에 따라 복수의 클라이언트 서버의 각각 학습 모델을 각각 통합 훈련할 수 있고, 클라이언트 서버의 자원 사용량 및 처리량을 감소함에 따라, 학습모델을 경량의 디바이스에 범용적으로 적용할 수 있다. |
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본 기술은 클라우드 서버와 연동하는 클라이언트 서버의 통합 학습 시스템 및 방법이 개시된다. 이러한 본 기술에 대한 구체적인 구현 예는 클라우드 서버에서 수신된 센서 데이터의 특성을 확인한 다음 데이터 표준화 및 전처리를 수행하고 기 등록된 모델 파라미터와 매칭되는 클라이언트 서버의 학습 모델과 클라우드 서버의 신경망을 매칭시켜 클라이언트 서버의 학습 모델을 훈련함에 따라 복수의 클라이언트 서버의 각각 학습 모델을 각각 통합 훈련할 수 있고, 클라이언트 서버의 자원 사용량 및 처리량을 감소함에 따라, 학습모델을 경량의 디바이스에 범용적으로 적용할 수 있다.</description><language>eng ; kor</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; PHYSICS ; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230703&DB=EPODOC&CC=KR&NR=20230097364A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76419</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230703&DB=EPODOC&CC=KR&NR=20230097364A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>KIM CHANGWOO</creatorcontrib><creatorcontrib>KIM HYEONG GOO</creatorcontrib><creatorcontrib>CHAE CHULSEOUNG</creatorcontrib><creatorcontrib>KANG JEONG HOON</creatorcontrib><title>INTEGRATED LEARNING SYSTEM AND METHOD OF CLIENT SEVER LINKED WITH CLOUDING SEVER</title><description>The present technology discloses an integrated learning system and method of a client server interlocking with a cloud server. A specific implementation example for the present technology performs data standardization and preprocessing after checking a characteristic of sensor data received in a cloud server, matches a learning model of a client server and a neural network of the cloud server that matches a preregistered model parameter, enables each learning model of a plurality of client servers to be each integrate-trained according to training of the learning model of the client server, and enables the learning model to be universally applied to a lightweight device according to a reduction of resource usage amount and process amount of the client server. The integrated learning system comprises: a plurality of client servers; and one cloud server.
본 기술은 클라우드 서버와 연동하는 클라이언트 서버의 통합 학습 시스템 및 방법이 개시된다. 이러한 본 기술에 대한 구체적인 구현 예는 클라우드 서버에서 수신된 센서 데이터의 특성을 확인한 다음 데이터 표준화 및 전처리를 수행하고 기 등록된 모델 파라미터와 매칭되는 클라이언트 서버의 학습 모델과 클라우드 서버의 신경망을 매칭시켜 클라이언트 서버의 학습 모델을 훈련함에 따라 복수의 클라이언트 서버의 각각 학습 모델을 각각 통합 훈련할 수 있고, 클라이언트 서버의 자원 사용량 및 처리량을 감소함에 따라, 학습모델을 경량의 디바이스에 범용적으로 적용할 수 있다.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>PHYSICS</subject><subject>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZAjw9AtxdQ9yDHF1UfBxdQzy8_RzVwiODA5x9VVw9HNR8HUN8fB3UfB3U3D28XT1C1EIdg1zDVLw8fTzBuoI9wzxAEr4h7qAtYGkeBhY0xJzilN5oTQ3g7Kba4izh25qQX58anFBYnJqXmpJvHeQkYGRsYGBpbmxmYmjMXGqACFoL9E</recordid><startdate>20230703</startdate><enddate>20230703</enddate><creator>KIM CHANGWOO</creator><creator>KIM HYEONG GOO</creator><creator>CHAE CHULSEOUNG</creator><creator>KANG JEONG HOON</creator><scope>EVB</scope></search><sort><creationdate>20230703</creationdate><title>INTEGRATED LEARNING SYSTEM AND METHOD OF CLIENT SEVER LINKED WITH CLOUDING SEVER</title><author>KIM CHANGWOO ; KIM HYEONG GOO ; CHAE CHULSEOUNG ; KANG JEONG HOON</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_KR20230097364A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; kor</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>KIM CHANGWOO</creatorcontrib><creatorcontrib>KIM HYEONG GOO</creatorcontrib><creatorcontrib>CHAE CHULSEOUNG</creatorcontrib><creatorcontrib>KANG JEONG HOON</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>KIM CHANGWOO</au><au>KIM HYEONG GOO</au><au>CHAE CHULSEOUNG</au><au>KANG JEONG HOON</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>INTEGRATED LEARNING SYSTEM AND METHOD OF CLIENT SEVER LINKED WITH CLOUDING SEVER</title><date>2023-07-03</date><risdate>2023</risdate><abstract>The present technology discloses an integrated learning system and method of a client server interlocking with a cloud server. A specific implementation example for the present technology performs data standardization and preprocessing after checking a characteristic of sensor data received in a cloud server, matches a learning model of a client server and a neural network of the cloud server that matches a preregistered model parameter, enables each learning model of a plurality of client servers to be each integrate-trained according to training of the learning model of the client server, and enables the learning model to be universally applied to a lightweight device according to a reduction of resource usage amount and process amount of the client server. The integrated learning system comprises: a plurality of client servers; and one cloud server.
본 기술은 클라우드 서버와 연동하는 클라이언트 서버의 통합 학습 시스템 및 방법이 개시된다. 이러한 본 기술에 대한 구체적인 구현 예는 클라우드 서버에서 수신된 센서 데이터의 특성을 확인한 다음 데이터 표준화 및 전처리를 수행하고 기 등록된 모델 파라미터와 매칭되는 클라이언트 서버의 학습 모델과 클라우드 서버의 신경망을 매칭시켜 클라이언트 서버의 학습 모델을 훈련함에 따라 복수의 클라이언트 서버의 각각 학습 모델을 각각 통합 훈련할 수 있고, 클라이언트 서버의 자원 사용량 및 처리량을 감소함에 따라, 학습모델을 경량의 디바이스에 범용적으로 적용할 수 있다.</abstract><oa>free_for_read</oa></addata></record> |
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title | INTEGRATED LEARNING SYSTEM AND METHOD OF CLIENT SEVER LINKED WITH CLOUDING SEVER |
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