Machine Learning Apparatus And Coil Producing Apparatus


Ontology type: sgo:Patent     


Patent Info

DATE

N/A

AUTHORS

Yasunori Sugimoto

ABSTRACT

A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program. More... »

Related SciGraph Publications

  • 2011-07. Reinforcement learning in feedback control in MACHINE LEARNING
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2746", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "name": "Yasunori Sugimoto", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s10994-011-5235-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036156720", 
              "https://doi.org/10.1007/s10994-011-5235-x"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "description": "

    A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.

    ", "id": "sg:patent.US-20170091674-A1", "keywords": [ "Learning", "machine", "apparatus", "observing", "state", "dimension", "actual value", "resistance", "execution time", "unit", "operation" ], "name": "MACHINE LEARNING APPARATUS AND COIL PRODUCING APPARATUS", "recipient": [ { "id": "https://www.grid.ac/institutes/grid.471089.3", "type": "Organization" } ], "sameAs": [ "https://app.dimensions.ai/details/patent/US-20170091674-A1" ], "sdDataset": "patents", "sdDatePublished": "2019-03-07T15:36", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com.uberresearch.data.dev.patents-pipeline/full_run_10/sn-export/5eb3e5a348d7f117b22cc85fb0b02730/0000100128-0000348334/json_export_c290c18d.jsonl", "type": "Patent" } ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/patent.US-20170091674-A1'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-20170091674-A1'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-20170091674-A1'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-20170091674-A1'


     

    This table displays all metadata directly associated to this object as RDF triples.

    36 TRIPLES      14 PREDICATES      25 URIs      18 LITERALS      2 BLANK NODES

    Subject Predicate Object
    1 sg:patent.US-20170091674-A1 schema:about anzsrc-for:2746
    2 schema:author N09ef4b3da05849edb7629c0849545f70
    3 schema:citation sg:pub.10.1007/s10994-011-5235-x
    4 schema:description <p id="p-0001" num="0000">A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.</p>
    5 schema:keywords Learning
    6 actual value
    7 apparatus
    8 dimension
    9 execution time
    10 machine
    11 observing
    12 operation
    13 resistance
    14 state
    15 unit
    16 schema:name MACHINE LEARNING APPARATUS AND COIL PRODUCING APPARATUS
    17 schema:recipient https://www.grid.ac/institutes/grid.471089.3
    18 schema:sameAs https://app.dimensions.ai/details/patent/US-20170091674-A1
    19 schema:sdDatePublished 2019-03-07T15:36
    20 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    21 schema:sdPublisher N6f50a46d0dd14994936db8e162d42998
    22 sgo:license sg:explorer/license/
    23 sgo:sdDataset patents
    24 rdf:type sgo:Patent
    25 N09ef4b3da05849edb7629c0849545f70 rdf:first N759a768918bf4682a99ab7ebec608daa
    26 rdf:rest rdf:nil
    27 N6f50a46d0dd14994936db8e162d42998 schema:name Springer Nature - SN SciGraph project
    28 rdf:type schema:Organization
    29 N759a768918bf4682a99ab7ebec608daa schema:name Yasunori Sugimoto
    30 rdf:type schema:Person
    31 anzsrc-for:2746 schema:inDefinedTermSet anzsrc-for:
    32 rdf:type schema:DefinedTerm
    33 sg:pub.10.1007/s10994-011-5235-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1036156720
    34 https://doi.org/10.1007/s10994-011-5235-x
    35 rdf:type schema:CreativeWork
    36 https://www.grid.ac/institutes/grid.471089.3 schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...