{"id":437,"date":"2024-11-01T12:03:43","date_gmt":"2024-11-01T12:03:43","guid":{"rendered":"http:\/\/math-in.net\/184esgi\/?page_id=437"},"modified":"2024-11-01T12:03:43","modified_gmt":"2024-11-01T12:03:43","slug":"problem-4-convolutional-neural-networks-in-image-processing-and-analysis-sunntics","status":"publish","type":"page","link":"https:\/\/math-in.net\/184esgi\/en\/program\/problem-4-convolutional-neural-networks-in-image-processing-and-analysis-sunntics\/","title":{"rendered":"Problem 4: Convolutional neural networks in image processing and analysis (Sunntics)"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"background-color: rgba(255,255,255,0);background-position: center center;background-repeat: no-repeat;border-width: 0px 0px 0px 0px;border-color:var(--awb-color3);border-style:solid;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start\" style=\"max-width:1248px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-1\"><h4>Problem 4: Convolutional neural networks in image processing and analysis<\/h4>\n<\/div><div style=\"text-align:center;\"><span class=\" fusion-imageframe imageframe-none imageframe-1 hover-type-none\"><img width=\"300\" height=\"90\" title=\"Sunntics\" src=\"http:\/\/math-in.net\/184esgi\/wp-content\/uploads\/sites\/12\/2024\/11\/Sunntics-300x90.png\" alt class=\"img-responsive wp-image-410\" srcset=\"https:\/\/math-in.net\/184esgi\/wp-content\/uploads\/sites\/12\/2024\/11\/Sunntics-200x60.png 200w, https:\/\/math-in.net\/184esgi\/wp-content\/uploads\/sites\/12\/2024\/11\/Sunntics-400x119.png 400w, https:\/\/math-in.net\/184esgi\/wp-content\/uploads\/sites\/12\/2024\/11\/Sunntics.png 462w\" sizes=\"(max-width: 640px) 100vw, 300px\" \/><\/span><\/div><div class=\"fusion-text fusion-text-2\"><p class=\"rtejustify\"><strong>Academic Coordinator | <\/strong>TBA<strong><br \/>\n<\/strong><\/p>\n<p><strong>Business Coordinator<\/strong> | Manuel Quero Garc\u00eda, CEO of Sunntics.<\/p>\n<p><strong>Specialist<\/strong> | Roc\u00edo Mingorance Mingorance, Head of Algorithms and Processes at Sunntics.<\/p>\n<p><strong>Scope<\/strong> | Convolutional neural networks (CNNs) are powerful tools for image processing and analysis, making them suitable for addressing problems such as predicting cloud coverage over a solar field from previously captured images by a cloud camera. CNNs can learn to identify spatial and temporal patterns in cloud images, enabling a certain degree of accuracy in predicting the location of a cloud over the heliostats and its density. However, the reliability of CNNs for this type of task will depend on several factors, such as the quality and quantity of available data. Evaluating the amount of data required to make accurate predictions of affected heliostat positions and the degree of impact based on cloud density can determine the success of this system&#8217;s implementation. Additionally, CNNs can be combined with temporal processing techniques, such as recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, to improve time-based predictive capabilities. On the other hand, atmospheric variations, wind speed and direction, and the dynamics of cloud formation and dispersal can add uncertainty. If these variables are not adequately considered, predictions may be less reliable.<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-0{width:100% !important;margin-top : 0px;margin-bottom : 20px;}.fusion-builder-column-0 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 1.92%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 1.92%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-0{width:100% !important;order : 0;}.fusion-builder-column-0 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-0{width:100% !important;order : 0;}.fusion-builder-column-0 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-flex-container.fusion-builder-row-1{ padding-top : 0px;margin-top : 0px;padding-right : 0px;padding-bottom : 0px;margin-bottom : 0px;padding-left : 0px;}<\/style><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":5,"featured_media":0,"parent":120,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.0 - 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