{"id":6100,"date":"2021-05-18T00:00:00","date_gmt":"2021-05-18T00:00:00","guid":{"rendered":"https:\/\/tedae.org\/uncategorized\/machine-learning-para-predecir-el-comportamiento-de-componentes-electronicos-no-testeados\/"},"modified":"2024-04-02T10:50:02","modified_gmt":"2024-04-02T10:50:02","slug":"machine-learning-para-predecir-el-comportamiento-de-componentes-electronicos-no-testeados","status":"publish","type":"post","link":"https:\/\/tedae.org\/en\/espacio\/machine-learning-para-predecir-el-comportamiento-de-componentes-electronicos-no-testeados\/","title":{"rendered":"Machine learning para predecir el comportamiento de componentes electr\u00f3nicos no testeados"},"content":{"rendered":"<p>El Centro Nacional de Aceleradores (CNA) y ALTER TECHNOLOGY, lideran el proyecto de Predicci\u00f3n del Comportamiento El\u00e9ctrico de Dispositivos Electr\u00f3nicos bajo Radiaci\u00f3n (PRECEDER). Se trata de un subproyecto de transferencia del conocimiento, basado en la inteligencia artificial, cuyo objetivo es preparar una amplia base de datos y desarrollar t\u00e9cnicas de Aprendizaje Autom\u00e1tico (\u2018Machine learning\u2019) sobre un conjunto de resultados, que permitan predecir el comportamiento de otros componentes electr\u00f3nicos no testeados en base a la experiencia.<\/p>\n<p>El CNA, es un referente para los ensayos de irradiaci\u00f3n y la empresa Alter es experta en la irradiaci\u00f3n de dispositivos electr\u00f3nicos para el sector espacial. La evaluaci\u00f3n del comportamiento frente a la radiaci\u00f3n es esencial para el dise\u00f1o y montaje de sat\u00e9lites, sondas, robots, etc.<\/p>\n<p>La herramienta que se desarrolle en este proyecto permitir\u00e1 predecir el comportamiento, por lo que tiene una aplicaci\u00f3n directa en proyectos espaciales y de entornos hostiles. Esta permitir\u00e1 al usuario conocer en la fase de dise\u00f1o si un componente es adecuado para su instrumento, ahorrando as\u00ed costes de ejecuci\u00f3n de ensayos y tiempo.<\/p>\n<p>PRECEDER se enmarca en el Proyecto Ecosistema Innovador con Inteligencia Artificial para Andaluc\u00eda 2025 liderado por el Campus de Excelencia Internacional Andaluc\u00eda TECH para que la Universidad de Sevilla y la Universidad de M\u00e1laga act\u00faen con empresas tecnol\u00f3gicas tractoras para el desarrollo de tecnolog\u00edas de inteligencia artificial en todos los \u00e1mbitos de la Estrategia de Especializaci\u00f3n Inteligente (RIS3) en Andaluc\u00eda. Cuarenta y nueve subproyectos de transferencia del conocimiento se engloban dentro de esta iniciativa financiada por la Junta de Andaluc\u00eda, a trav\u00e9s de la Direcci\u00f3n General de Investigaci\u00f3n y Transferencia del Conocimiento de la Consejer\u00eda de Econom\u00eda, Conocimiento, Empresas y Universidad, enmarcada en el Programa Operativo FEDER. <\/p>","protected":false},"excerpt":{"rendered":"<p>Machine learning para predecir el comportamiento de componentes electr\u00f3nicos no testeados<\/p>","protected":false},"author":5,"featured_media":6101,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[436],"tags":[],"class_list":{"0":"post-6100","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-espacio"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine learning para predecir el comportamiento de componentes electr\u00f3nicos no testeados - 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