{"id":1718,"date":"2020-12-03T09:35:12","date_gmt":"2020-12-03T02:35:12","guid":{"rendered":"http:\/\/research.binus.ac.id\/airdc\/?p=1718"},"modified":"2021-11-01T11:11:14","modified_gmt":"2021-11-01T04:11:14","slug":"data-annotation-system-for-intelligent-energy-conservator-in-smart-building-2","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2020\/12\/data-annotation-system-for-intelligent-energy-conservator-in-smart-building-2\/","title":{"rendered":"Data Annotation System for Intelligent Energy Conservator in Smart Building"},"content":{"rendered":"<p style=\"text-align: justify\">The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However<span class=\"ls1\">, <\/span>massive annotated dataset is necessary to train the system<span class=\"ls1\">.\u00a0 <\/span>Therefore, we propose <span class=\"ls4\">a <\/span>system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average.<\/p>\n<p style=\"text-align: justify\">International Conference on Eco Engineering Development 2019<\/p>\n<p style=\"text-align: justify\"><strong>Bens Pardamean, Tjeng Wawan Cenggoro, Bloomest Chandra, and Reza Rahutomo<\/strong><\/p>\n<div><a href=\"https:\/\/www.researchgate.net\/publication\/339914339_Data_annotation_system_for_intelligent_energy_conservator_in_smart_building\">Read Full Paper<\/a><\/div>\n<div style=\"text-align: justify\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system.\u00a0 Therefore, [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":2047,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1718","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1718","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=1718"}],"version-history":[{"count":4,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1718\/revisions"}],"predecessor-version":[{"id":1919,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1718\/revisions\/1919"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/2047"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=1718"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=1718"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=1718"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}