National Cheng Kung University

About: National Cheng Kung University is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Population & Thin film. The organization has 49723 authors who have published 69799 publications receiving 1437420 citations. The organization is also known as: NCKU.

Characterization of anodic films formed on AZ91D magnesium alloy

Abstract: Anodization of die-casted AZ91D magnesium alloy was performed in 3 M KOH+0.21 M Na 3 PO 4 +0.6 M KF base electrolyte with and without Al(NO 3 ) 3 addition. The anodic film was characterized by using X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The corrosion resistance of the various anodized alloys was then evaluated in 3.5 wt % NaCl solution using electrochemical impedance spectroscopy (EIS) and immersion testing. The results showed that the anodic film was mainly composed of MgO. The addition of Al(NO 3 ) 3 into the base electrolyte results in the formation of Al 2 O 3 and Al(OH) 3 in the anodic film. The maximum amount of Al 2 O 3 was found in the anodic film when the alloy was anodized in the electrolyte containing 0.15 M Al(NO 3 ) 3 . The results of EIS analysis and morphological examination showed that the MgO anodic film modified with Al 2 O 3 exhibited the superior corrosiom resistance for AZ91D Mg alloy.

Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Abstract: Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.