{"id":7087,"date":"2012-02-05T21:57:52","date_gmt":"2012-02-05T19:57:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=7087"},"modified":"2012-02-05T21:57:52","modified_gmt":"2012-02-05T19:57:52","slug":"efficient-computation-of-som-for-outage-database","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7087","title":{"rendered":"Efficient Computation of SOM for Outage Database"},"content":{"rendered":"<p>This paper describes a utilization of the Self Organizing Map (SOM) method for the analysis of power outage data. SOM, to be already used in many fields, is based on the Kohonen self-organizing neural network and it is known to capture underlying concepts. We apply this method for a unified database of power outages to be collected for several years in the Czech Republic. The most significant attributes are selected from the database and records are used for the training of the SOM. We utilize our previously introduced application EDAS (Electrical Data Analysis using SOM) for the visualization, understanding, and analysis of the trained SOM. Because of performance issues in the previous introduced approaches, we implement our SOM on GPU environment and compare this method with previous solutions in this article.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes a utilization of the Self Organizing Map (SOM) method for the analysis of power outage data. SOM, to be already used in many fields, is based on the Kohonen self-organizing neural network and it is known to capture underlying concepts. We apply this method for a unified database of power outages to [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,667,34,20,503,199,134],"class_list":["post-7087","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-databases","tag-neural-networks","tag-nvidia","tag-self-organizing-map","tag-tesla-c1060","tag-visualization"],"views":2301,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7087","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7087"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7087\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7087"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7087"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7087"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}