Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.
A curve-shape piezoelectric unimorph is used for energy harvest. Deformation of the unimorph will gain electricity via piezoelectric direct effect. In this study, a cantilever unimorph encountered with external force at various vibration frequencies to generate electricity and save into a storage circuitry is investigated. Modeling of the unimorph and storage circuitry is derived. The analytical com putation and electrical charge [2]. Sodano et aI. used a PSI-5h4e piezo bonded onto a plate with dimension 80mmx40mmx2.41mm to conversion into electrical energy [6]. Mohammadi et al, used Key words: energy harvest, piezoelectric ceramics unimorph fiber-based piezoelectric (piezo fiber) material with 40% 978-1-4244-4349-9/09/$25.00
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.