두부류 검사 장치, 두부류 제조 시스템, 두부류의 검사 방법, 및 프로그램

두부류 검사 장치로서, 검사 대상이 되는 두부류를 촬영하는 촬상부와, 두부류의 촬영 화상을 포함하는 학습용 데이터를 사용하여 기계 학습을 행함으로써 생성된, 입력 데이터로 나타내어지는 두부류의 품질의 판정을 행하기 위한 학습 완료 모델에 대해, 상기 촬상부에 의해 촬영된 두부류의 촬영 화상을 입력 데이터로서 입력함으로써 얻어지는 출력 데이터로서의 평가치를 이용하여, 해당 촬영 화상으로 나타내어지는 두부류의 품질을 판정하는 검사 수단을 갖는다. An inspection device for tofu products includes a...

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Hauptverfasser: AMANO MOTONARI, SETO YUSUKE, TAKAI TOICHIRO
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SETO YUSUKE
TAKAI TOICHIRO
description 두부류 검사 장치로서, 검사 대상이 되는 두부류를 촬영하는 촬상부와, 두부류의 촬영 화상을 포함하는 학습용 데이터를 사용하여 기계 학습을 행함으로써 생성된, 입력 데이터로 나타내어지는 두부류의 품질의 판정을 행하기 위한 학습 완료 모델에 대해, 상기 촬상부에 의해 촬영된 두부류의 촬영 화상을 입력 데이터로서 입력함으로써 얻어지는 출력 데이터로서의 평가치를 이용하여, 해당 촬영 화상으로 나타내어지는 두부류의 품질을 판정하는 검사 수단을 갖는다. An inspection device for tofu products includes a processor and a memory storing instructions that, when executed by the processor, cause a computer to execute operations. The operations include: acquiring a captured image from an image capturing device configured to capture an image of a tofu product to be inspected; and determining a quality of the tofu product indicated by the captured image using an evaluation value as output data obtained by inputting the captured image of the tofu product captured by the image capturing device as input data with respect to a learned model for determining a quality of a tofu product indicated by input data, the learned model being generated by performing machine learning using learning data including a captured image of a tofu product.
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An inspection device for tofu products includes a processor and a memory storing instructions that, when executed by the processor, cause a computer to execute operations. 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An inspection device for tofu products includes a processor and a memory storing instructions that, when executed by the processor, cause a computer to execute operations. The operations include: acquiring a captured image from an image capturing device configured to capture an image of a tofu product to be inspected; and determining a quality of the tofu product indicated by the captured image using an evaluation value as output data obtained by inputting the captured image of the tofu product captured by the image capturing device as input data with respect to a learned model for determining a quality of a tofu product indicated by input data, the learned model being generated by performing machine learning using learning data including a captured image of a tofu product.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>FOODS OR FOODSTUFFS</subject><subject>FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BYSUBCLASSES A23B - A23J</subject><subject>HUMAN NECESSITIES</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES</subject><subject>MEASURING</subject><subject>PERFORMING OPERATIONS</subject><subject>PHYSICS</subject><subject>POSTAL SORTING</subject><subject>PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL</subject><subject>SEPARATING SOLIDS FROM SOLIDS</subject><subject>SORTING</subject><subject>SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTEDPIECE-MEAL, e.g. BY PICKING</subject><subject>TESTING</subject><subject>THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OFNUTRITIVE QUALITIES, PHYSICAL TREATMENT</subject><subject>THEIR TREATMENT, NOT COVERED BY OTHER CLASSES</subject><subject>TRANSPORTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZEh8PXHC620Nr5fOUHi1qeFN0xqFN_OWvtk5Q0cBIfFmwZw3CzcovOme86ZrydvWOUhyb-bC9b3esPL1pqlAuQ39Cm-ntLxeOOfV9h2v563gYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXx3kFGBkbGBgYGJqYGZo7GxKkCAOyeV8M</recordid><startdate>20230106</startdate><enddate>20230106</enddate><creator>AMANO MOTONARI</creator><creator>SETO YUSUKE</creator><creator>TAKAI TOICHIRO</creator><scope>EVB</scope></search><sort><creationdate>20230106</creationdate><title>두부류 검사 장치, 두부류 제조 시스템, 두부류의 검사 방법, 및 프로그램</title><author>AMANO MOTONARI ; SETO YUSUKE ; TAKAI TOICHIRO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_KR20230004506A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>kor</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>FOODS OR FOODSTUFFS</topic><topic>FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BYSUBCLASSES A23B - A23J</topic><topic>HUMAN NECESSITIES</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES</topic><topic>MEASURING</topic><topic>PERFORMING OPERATIONS</topic><topic>PHYSICS</topic><topic>POSTAL SORTING</topic><topic>PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL</topic><topic>SEPARATING SOLIDS FROM SOLIDS</topic><topic>SORTING</topic><topic>SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTEDPIECE-MEAL, e.g. BY PICKING</topic><topic>TESTING</topic><topic>THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OFNUTRITIVE QUALITIES, PHYSICAL TREATMENT</topic><topic>THEIR TREATMENT, NOT COVERED BY OTHER CLASSES</topic><topic>TRANSPORTING</topic><toplevel>online_resources</toplevel><creatorcontrib>AMANO MOTONARI</creatorcontrib><creatorcontrib>SETO YUSUKE</creatorcontrib><creatorcontrib>TAKAI TOICHIRO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>AMANO MOTONARI</au><au>SETO YUSUKE</au><au>TAKAI TOICHIRO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>두부류 검사 장치, 두부류 제조 시스템, 두부류의 검사 방법, 및 프로그램</title><date>2023-01-06</date><risdate>2023</risdate><abstract>두부류 검사 장치로서, 검사 대상이 되는 두부류를 촬영하는 촬상부와, 두부류의 촬영 화상을 포함하는 학습용 데이터를 사용하여 기계 학습을 행함으로써 생성된, 입력 데이터로 나타내어지는 두부류의 품질의 판정을 행하기 위한 학습 완료 모델에 대해, 상기 촬상부에 의해 촬영된 두부류의 촬영 화상을 입력 데이터로서 입력함으로써 얻어지는 출력 데이터로서의 평가치를 이용하여, 해당 촬영 화상으로 나타내어지는 두부류의 품질을 판정하는 검사 수단을 갖는다. An inspection device for tofu products includes a processor and a memory storing instructions that, when executed by the processor, cause a computer to execute operations. The operations include: acquiring a captured image from an image capturing device configured to capture an image of a tofu product to be inspected; and determining a quality of the tofu product indicated by the captured image using an evaluation value as output data obtained by inputting the captured image of the tofu product captured by the image capturing device as input data with respect to a learned model for determining a quality of a tofu product indicated by input data, the learned model being generated by performing machine learning using learning data including a captured image of a tofu product.</abstract><oa>free_for_read</oa></addata></record>
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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
FOODS OR FOODSTUFFS
FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BYSUBCLASSES A23B - A23J
HUMAN NECESSITIES
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES
MEASURING
PERFORMING OPERATIONS
PHYSICS
POSTAL SORTING
PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
SEPARATING SOLIDS FROM SOLIDS
SORTING
SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTEDPIECE-MEAL, e.g. BY PICKING
TESTING
THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OFNUTRITIVE QUALITIES, PHYSICAL TREATMENT
THEIR TREATMENT, NOT COVERED BY OTHER CLASSES
TRANSPORTING
title 두부류 검사 장치, 두부류 제조 시스템, 두부류의 검사 방법, 및 프로그램
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