Method for extracting feature of machine learning for determining vibration measurement error and vibration measurement error determination system using the same
According to an embodiment of the present invention, a machine learning feature extraction method includes the steps of: measuring vibrations occurring in structures to acquire vibration data; extracting at least one sample data from the vibration data; calculating first statistical information from...
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| creator | YANG JAE HEUNG MAENG HYO YOUNG MOON BYEONG SUK KIM JU SIK CHOI BYEONG KEUN JEON I SEUL KIM DAE WOONG KIM MIN HO YU HYEON TAK |
| description | According to an embodiment of the present invention, a machine learning feature extraction method includes the steps of: measuring vibrations occurring in structures to acquire vibration data; extracting at least one sample data from the vibration data; calculating first statistical information from the sample data; and extracting feature information for machine learning which determines whether the vibration data is due to a measurement error based on the first statistical information.
본 발명의 실시예에 따른 머신러닝 특징 추출 방법은, 구조물에서 발생하는 진동을 측정하여 진동 데이터를 획득하는 진동 데이터 획득 단계, 상기 진동 데이터로부터 적어도 하나의 샘플 데이터를 추출하는 단계, 상기 샘플 데이터로부터 제1 통계 정보들을 연산하는 단계 및 상기 제1 통계 정보를 기반으로 상기 진동 데이터가 측정 오류에 의한 것인지를 판단하는 머신 러닝을 위한 특징 정보들을 추출하는 단계를 포함한다. |
| format | Patent |
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본 발명의 실시예에 따른 머신러닝 특징 추출 방법은, 구조물에서 발생하는 진동을 측정하여 진동 데이터를 획득하는 진동 데이터 획득 단계, 상기 진동 데이터로부터 적어도 하나의 샘플 데이터를 추출하는 단계, 상기 샘플 데이터로부터 제1 통계 정보들을 연산하는 단계 및 상기 제1 통계 정보를 기반으로 상기 진동 데이터가 측정 오류에 의한 것인지를 판단하는 머신 러닝을 위한 특징 정보들을 추출하는 단계를 포함한다.</description><language>eng ; kor</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC ORINFRASONIC WAVES ; MEASURING ; PHYSICS ; TESTING</subject><creationdate>2022</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=20221220&DB=EPODOC&CC=KR&NR=20220166978A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221220&DB=EPODOC&CC=KR&NR=20220166978A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>YANG JAE HEUNG</creatorcontrib><creatorcontrib>MAENG HYO YOUNG</creatorcontrib><creatorcontrib>MOON BYEONG SUK</creatorcontrib><creatorcontrib>KIM JU SIK</creatorcontrib><creatorcontrib>CHOI BYEONG KEUN</creatorcontrib><creatorcontrib>JEON I SEUL</creatorcontrib><creatorcontrib>KIM DAE WOONG</creatorcontrib><creatorcontrib>KIM MIN HO</creatorcontrib><creatorcontrib>YU HYEON TAK</creatorcontrib><title>Method for extracting feature of machine learning for determining vibration measurement error and vibration measurement error determination system using the same</title><description>According to an embodiment of the present invention, a machine learning feature extraction method includes the steps of: measuring vibrations occurring in structures to acquire vibration data; extracting at least one sample data from the vibration data; calculating first statistical information from the sample data; and extracting feature information for machine learning which determines whether the vibration data is due to a measurement error based on the first statistical information.
본 발명의 실시예에 따른 머신러닝 특징 추출 방법은, 구조물에서 발생하는 진동을 측정하여 진동 데이터를 획득하는 진동 데이터 획득 단계, 상기 진동 데이터로부터 적어도 하나의 샘플 데이터를 추출하는 단계, 상기 샘플 데이터로부터 제1 통계 정보들을 연산하는 단계 및 상기 제1 통계 정보를 기반으로 상기 진동 데이터가 측정 오류에 의한 것인지를 판단하는 머신 러닝을 위한 특징 정보들을 추출하는 단계를 포함한다.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC ORINFRASONIC WAVES</subject><subject>MEASURING</subject><subject>PHYSICS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEKwkAURNNYiHqHD9ZCjBC1FFEEsRH78E1mzUJ2N-z-iB7HmxqjtmI1DO_N9KPHAVK6gpTzhJt4zkXbCymwNB7kFBnOS21BFdjbjrVqAYE3uutXffYs2lky4NCuDKwQvG89tsVP_v1583APAkNNeN1KCQpsMIx6iquA0ScH0Xi7Oa13E9QuQ6g5h4Vk-2MSJ0k8TdPlfLGa_Wc9AeupWDI</recordid><startdate>20221220</startdate><enddate>20221220</enddate><creator>YANG JAE HEUNG</creator><creator>MAENG HYO YOUNG</creator><creator>MOON BYEONG SUK</creator><creator>KIM JU SIK</creator><creator>CHOI BYEONG KEUN</creator><creator>JEON I SEUL</creator><creator>KIM DAE WOONG</creator><creator>KIM MIN HO</creator><creator>YU HYEON TAK</creator><scope>EVB</scope></search><sort><creationdate>20221220</creationdate><title>Method for extracting feature of machine learning for determining vibration measurement error and vibration measurement error determination system using the same</title><author>YANG JAE HEUNG ; MAENG HYO YOUNG ; MOON BYEONG SUK ; KIM JU SIK ; CHOI BYEONG KEUN ; JEON I SEUL ; KIM DAE WOONG ; KIM MIN HO ; YU HYEON TAK</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_KR20220166978A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; kor</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC ORINFRASONIC WAVES</topic><topic>MEASURING</topic><topic>PHYSICS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>YANG JAE HEUNG</creatorcontrib><creatorcontrib>MAENG HYO YOUNG</creatorcontrib><creatorcontrib>MOON BYEONG SUK</creatorcontrib><creatorcontrib>KIM JU SIK</creatorcontrib><creatorcontrib>CHOI BYEONG KEUN</creatorcontrib><creatorcontrib>JEON I SEUL</creatorcontrib><creatorcontrib>KIM DAE WOONG</creatorcontrib><creatorcontrib>KIM MIN HO</creatorcontrib><creatorcontrib>YU HYEON TAK</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>YANG JAE HEUNG</au><au>MAENG HYO YOUNG</au><au>MOON BYEONG SUK</au><au>KIM JU SIK</au><au>CHOI BYEONG KEUN</au><au>JEON I SEUL</au><au>KIM DAE WOONG</au><au>KIM MIN HO</au><au>YU HYEON TAK</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Method for extracting feature of machine learning for determining vibration measurement error and vibration measurement error determination system using the same</title><date>2022-12-20</date><risdate>2022</risdate><abstract>According to an embodiment of the present invention, a machine learning feature extraction method includes the steps of: measuring vibrations occurring in structures to acquire vibration data; extracting at least one sample data from the vibration data; calculating first statistical information from the sample data; and extracting feature information for machine learning which determines whether the vibration data is due to a measurement error based on the first statistical information.
본 발명의 실시예에 따른 머신러닝 특징 추출 방법은, 구조물에서 발생하는 진동을 측정하여 진동 데이터를 획득하는 진동 데이터 획득 단계, 상기 진동 데이터로부터 적어도 하나의 샘플 데이터를 추출하는 단계, 상기 샘플 데이터로부터 제1 통계 정보들을 연산하는 단계 및 상기 제1 통계 정보를 기반으로 상기 진동 데이터가 측정 오류에 의한 것인지를 판단하는 머신 러닝을 위한 특징 정보들을 추출하는 단계를 포함한다.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC ORINFRASONIC WAVES MEASURING PHYSICS TESTING |
| title | Method for extracting feature of machine learning for determining vibration measurement error and vibration measurement error determination system using the same |
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