An oil spill from a vessel is a critical maritime accident that can severely damage the environment. In this study; we utilize the basic construction of grey relational analysis to explore oil spill events statistics from 1974 to 2015 and successfully analyze the causes of incidents in 2016. The results illustrate that grey relational analysis effectively identifies the factors causing oil spills with an accuracy of over 96%. The research is aimed to reduce the marine accidents and predict the cause of oil spill in advance. The analysis is dealing with the incidents to approach the circumstance in various intensity of oil spill in the last 40 years. Moreover, an application of grey theory demonstrates accurate and reliable methodology to decision maker. Thus, the investigation can predict the causes of pollution from oil spill accidents in the future.
The paper presents a typology of electrical open and short defects on thin-film transistors (TFT) using an electrical tester and automatic optical inspection (AOI). The experiment takes the glass 8.5th generation to detect the electrical characteristics engaged with time delay and integration (TDI) charged-coupled-devices (CCDs), a fast line-scan, and a review CCD with five sets of magnification lenses for further inspection. An automatic data acquisition program (ADAP) controls the open/short (O/S) sensor, TDI-CCD, and motor device for machine vision and statistics of substrate defects simultaneously. Furthermore, the quartz mask installed on AOI verified its optical resolution; a TDI-CCD can grab an image of a moving object during transfers of the charge in synchronous scanning with the object that is significant.
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