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Analyzing The Proteins From The Bacteria

為了解決Topo Map的問題，作者阮慶隆 這樣論述：

Bacterial wilt is one of the most devastating agricultural diseases in the world, most commonly affecting tomatoes. Four strains were identified based on colony morphology on TTC medium and PCR products homologous to primers pairs 759-760 and 16S-23S rRNA.Three of them were identified as R. solanac

earum and one of them was L. amnigena. L. amnigena. Pathogenicity in tomatoes proves that L. amnigena has the ability to cause bacterial wilt. Therefore, it is necessary to analyze the basic protein system of these two species, the main cause of bacterial wilt in tomatoes, to find ways to prevent wi

lt disease in future studies. In this study, we had tested the pathogenicity on tomato plants of four bacterial strains and preliminarily analyzed their proteins by 2D-gel.Strains PSS4 and 581 were death rates were 30% and 33% after 30 days of infection. Strains S2 and L2R showed symptoms of green w

ilt after 15 days, with death at the rate of 10% and 23.3% after 30 days. The total protein extracted from strain S2 was completely higher than that of other strains. There is a relative similarity of protein between two strains PSS4 and 581.

智能工廠網頁資訊系統實務場域建置與AI瑕疵檢測實務技術開發

為了解決Topo Map的問題，作者鄭家宸 這樣論述：

本論文以開發工業4.0技術為主軸，主要分成智能工廠網頁資訊系統和人工智慧視覺辨識系統。本論文依照實務上所需，建置相關伺服器提供智能工廠網頁資訊系統和人工智慧視覺辨識系統，並開發各類機台資訊可視化畫面，實現遠端監控廠區和機台狀況。智能工廠網頁資訊系統，主要目的是將工廠內資訊串接至雲端資料庫，協助管理者了解廠區狀況，並導入人工智慧視覺辨識系統來輔助管理者管理廠區。人工智慧視覺辨識系統主要分成人臉辨識、瑕疵檢測、距離量測。人臉辨識部分，可導入工廠資安，協助管理廠區人員出入和系統登入，而瑕疵檢測和距離量測部分，以取代人工目視檢測作業，除了改善人工檢測精準度不足，並減少誤判率與提升檢測速度。最後人工智

慧視覺辨識系統，成功整合雲端通訊、雲端伺服器、資料庫及可視化看板等相關技術，將其與智能工廠網頁資訊系統結合完成實務上建置。