{"_buckets": {"deposit": "c88a9384-4679-42a0-861d-a4a6fbe24404"}, "_deposit": {"id": "10315", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "10315"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00010315"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2008-11", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "260", "bibliographicPageStart": "255", "bibliographic_titles": [{"bibliographic_title": "International Symposium on Micro-NanoMechatronics and Human Science (MHS 2008)"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Our parallel typed two-axial actuator was composed of two bimorph piezoelectric elements and two small links connected by three joints. We formulated kinematics for the parallel typed two-axial actuator because the endpoint is controlled in the two-dimensional coordinate. Since relationship between applied voltage and \ndisplacement cause by the voltage shows a hysteresis loop in the bimorph piezoelectric element used as components of the two-axial actuator, we produce a control system for the two-axial actuator based on a multi-layered artificial neural network to compensate the hysteresis. The neural network is comprised of 4 \nneurons in the input layer, 10 neurons in the hidden layer and ones neuron in the output layer. \nThe output neuron emits time derivative of voltage; two bits signal expressing increment or decrement condition is generated by two input neurons; one of the other two input neurons and the other calculate current values of voltage and displacement, respectively. In the learning process, the network learns the hysteresis including minor loops. In the verification test, the endpoint of the two-axial actuator traces \nthe desired circular trajectory in the two-dimensional coordinate system. After learning hysteresis loops including minor loops, the neural network simulates these hysteresis phenomena with very high accuracy.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1109/MHS.2008.4752459"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/12135"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 2008 IEEE. Reprinted from International Symposium on Micro-NanoMechatronics and Human Science, 2008. MHS 2008. p.255-260.\nThis material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University\u2019s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Esumi, Kazuya"}], "nameIdentifiers": [{"nameIdentifier": "31283", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ohka, Masahiro"}], "nameIdentifiers": [{"nameIdentifier": "31284", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Sawamoto, Yasuhiro"}], "nameIdentifiers": [{"nameIdentifier": "31285", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Matsukawa, Shiho"}], "nameIdentifiers": [{"nameIdentifier": "31286", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Miyaoka, Tetsu"}], "nameIdentifiers": [{"nameIdentifier": "31287", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "MHS2008_SA_2_7.pdf", "filesize": [{"value": "558.1 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 558100.0, "url": {"label": "MHS2008_SA_2_7.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/10315/files/MHS2008_SA_2_7.pdf"}, "version_id": "f239e466-117c-4370-af9e-82e35958a897"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network"}]}, "item_type_id": "10", "owner": "1", "path": ["312/598/599"], "permalink_uri": "http://hdl.handle.net/2237/12135", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2009-08-31"}, "publish_date": "2009-08-31", "publish_status": "0", "recid": "10315", "relation": {}, "relation_version_is_last": true, "title": ["Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network"], "weko_shared_id": null}