REGRESSION MODEL GENERATION AND LEAKAGE AMOUNT ESTIMATION SYSTEM
To provide a regression model generation device and a leakage amount estimation system that maintain estimation accuracy within a predetermined range regardless of a value of a target variable.SOLUTION: A server device 20 generates an estimation model M that uses a first physical amount as an object...
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| creator | TOKIOKA YOSHINORI |
| description | To provide a regression model generation device and a leakage amount estimation system that maintain estimation accuracy within a predetermined range regardless of a value of a target variable.SOLUTION: A server device 20 generates an estimation model M that uses a first physical amount as an objective variable and a second physical amount as an explanatory variable. The server device 20 includes a learning unit 22 that calculates an error between an estimation value of the first physical amount which is a value of the objective variable, and an actual measurement value of the first physical amount, and generates the estimated model M by machine learning so that a ratio of the error to the actual measurement value is within a predetermined range.SELECTED DRAWING: Figure 1
【課題】目的変数の値に関係なく、推定精度を所定範囲内に保持すること。【解決手段】サーバ装置２０は、第１物理量を目的変数とし、第２物理量を説明変数とする推定モデルＭを生成する。サーバ装置２０は、目的変数の値である第１物理量の推定値と第１物理量の実測値との誤差を算出し、誤差の前記実測値に対する割合が所定範囲内となるように機械学習により推定モデルＭを生成する学習部２２を備えている。【選択図】図１ |
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【課題】目的変数の値に関係なく、推定精度を所定範囲内に保持すること。【解決手段】サーバ装置２０は、第１物理量を目的変数とし、第２物理量を説明変数とする推定モデルＭを生成する。サーバ装置２０は、目的変数の値である第１物理量の推定値と第１物理量の実測値との誤差を算出し、誤差の前記実測値に対する割合が所定範囲内となるように機械学習により推定モデルＭを生成する学習部２２を備えている。【選択図】図１</description><language>eng ; jpn</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONTROL OR REGULATING SYSTEMS IN GENERAL ; CONTROLLING ; COUNTING ; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS ; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS ; PHYSICS ; REGULATING</subject><creationdate>2024</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=20240507&DB=EPODOC&CC=JP&NR=2024061364A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240507&DB=EPODOC&CC=JP&NR=2024061364A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>TOKIOKA YOSHINORI</creatorcontrib><title>REGRESSION MODEL GENERATION AND LEAKAGE AMOUNT ESTIMATION SYSTEM</title><description>To provide a regression model generation device and a leakage amount estimation system that maintain estimation accuracy within a predetermined range regardless of a value of a target variable.SOLUTION: A server device 20 generates an estimation model M that uses a first physical amount as an objective variable and a second physical amount as an explanatory variable. The server device 20 includes a learning unit 22 that calculates an error between an estimation value of the first physical amount which is a value of the objective variable, and an actual measurement value of the first physical amount, and generates the estimated model M by machine learning so that a ratio of the error to the actual measurement value is within a predetermined range.SELECTED DRAWING: Figure 1
【課題】目的変数の値に関係なく、推定精度を所定範囲内に保持すること。【解決手段】サーバ装置２０は、第１物理量を目的変数とし、第２物理量を説明変数とする推定モデルＭを生成する。サーバ装置２０は、目的変数の値である第１物理量の推定値と第１物理量の実測値との誤差を算出し、誤差の前記実測値に対する割合が所定範囲内となるように機械学習により推定モデルＭを生成する学習部２２を備えている。【選択図】図１</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONTROL OR REGULATING SYSTEMS IN GENERAL</subject><subject>CONTROLLING</subject><subject>COUNTING</subject><subject>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</subject><subject>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</subject><subject>PHYSICS</subject><subject>REGULATING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHAIcnUPcg0O9vT3U_D1d3H1UXB39XMNcgwBCTj6uSj4uDp6O7q7Kjj6-of6hSi4Bod4-kJkgyODQ1x9eRhY0xJzilN5oTQ3g5Kba4izh25qQX58anFBYnJqXmpJvFeAkYGRiYGZobGZiaMxUYoAFF8rRA</recordid><startdate>20240507</startdate><enddate>20240507</enddate><creator>TOKIOKA YOSHINORI</creator><scope>EVB</scope></search><sort><creationdate>20240507</creationdate><title>REGRESSION MODEL GENERATION AND LEAKAGE AMOUNT ESTIMATION SYSTEM</title><author>TOKIOKA YOSHINORI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_JP2024061364A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; jpn</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>CONTROL OR REGULATING SYSTEMS IN GENERAL</topic><topic>CONTROLLING</topic><topic>COUNTING</topic><topic>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</topic><topic>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</topic><topic>PHYSICS</topic><topic>REGULATING</topic><toplevel>online_resources</toplevel><creatorcontrib>TOKIOKA YOSHINORI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>TOKIOKA YOSHINORI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>REGRESSION MODEL GENERATION AND LEAKAGE AMOUNT ESTIMATION SYSTEM</title><date>2024-05-07</date><risdate>2024</risdate><abstract>To provide a regression model generation device and a leakage amount estimation system that maintain estimation accuracy within a predetermined range regardless of a value of a target variable.SOLUTION: A server device 20 generates an estimation model M that uses a first physical amount as an objective variable and a second physical amount as an explanatory variable. The server device 20 includes a learning unit 22 that calculates an error between an estimation value of the first physical amount which is a value of the objective variable, and an actual measurement value of the first physical amount, and generates the estimated model M by machine learning so that a ratio of the error to the actual measurement value is within a predetermined range.SELECTED DRAWING: Figure 1
【課題】目的変数の値に関係なく、推定精度を所定範囲内に保持すること。【解決手段】サーバ装置２０は、第１物理量を目的変数とし、第２物理量を説明変数とする推定モデルＭを生成する。サーバ装置２０は、目的変数の値である第１物理量の推定値と第１物理量の実測値との誤差を算出し、誤差の前記実測値に対する割合が所定範囲内となるように機械学習により推定モデルＭを生成する学習部２２を備えている。【選択図】図１</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING |
| title | REGRESSION MODEL GENERATION AND LEAKAGE AMOUNT ESTIMATION SYSTEM |
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