{"id":36249,"date":"2019-10-31T22:10:29","date_gmt":"2019-10-31T19:10:29","guid":{"rendered":"https:\/\/prohoster.info\/blog\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry\/"},"modified":"2019-10-31T22:10:29","modified_gmt":"2019-10-31T19:10:29","slug":"nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Rete neurale nel vetro. Non richiede alimentazione, riconosce numeri","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Rete
\n
\nTutti noi conosciamo la capacit\u00e0 delle reti neurali di riconoscere il testo manoscritto. Le basi di questa tecnologia esistono da molti anni, ma solo di recente, un salto nelle capacit\u00e0 computazionali e nell'elaborazione parallela dei dati ha permesso di trasformare questa tecnologia in una soluzione molto pratica. Tuttavia, questa soluzione pratica sar\u00e0 essenzialmente presentata sotto forma di computer digitale che modifica continuamente i bit, proprio come nell'esecuzione di qualsiasi altro programma. Ma nel caso della rete neurale sviluppata dai ricercatori delle universit\u00e0 del Wisconsin, MIT e Columbia, la situazione \u00e8 diversa. Essi hanno creato un pannello di vetro che non richiede alimentazione propria, ma \u00e8 in grado di riconoscere i numeri manoscritti<\/a><\/noindex>.
\n<\/a><\/noindex>
\nQuesto vetro contiene inclusioni posizionate con precisione, come bolle d'aria, impurezze di grafene e altri materiali. Quando la luce colpisce il vetro, si generano complesse onde luminose che portano a un'intensificazione della luce in una delle dieci aree. Ciascuna di queste aree corrisponde a un numero. Ad esempio, di seguito sono riportati due esempi che illustrano come la luce si diffonde nel riconoscimento del numero \"due\".<\/p>\n

\"Rete
\n
\nCon un set di addestramento di 5000 immagini, la rete neurale \u00e8 in grado di riconoscere correttamente il 79% delle 1000 immagini di input. Il team ritiene di poter migliorare il risultato se riuscissero a superare le limitazioni imposte dal processo di produzione del vetro. Hanno iniziato con un design molto limitato del dispositivo per ottenere un prototipo funzionante. In seguito, intendono continuare a esplorare diversi modi per migliorare la qualit\u00e0 del riconoscimento, cercando di non complicare eccessivamente la tecnologia affinch\u00e9 possa essere utilizzata nella produzione. Il team ha anche in mente di creare una rete neurale tridimensionale nel vetro.
\n
Fonte: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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