The role of the shipping industry in international logistics has been highlighted with the development of the global economy and the increase in international trade. Simultaneously, some of the environmental problems caused by shipping activities have gradually surfaced. The development of modern communication technology and marine communication equipment increased the feasibility of real-time ship dynamic data, as an information source for monitoring ship sailing states, and provided a data basis for the control of ship pollutant emissions. Based on the Automatic Identification System (AIS) data and ship-related data obtained from the waters of the ports of Los Angeles and Long Beach in 2020, the dynamic method is combined with the ship traffic emissions model STEAM2 to calculate the ship pollutant emissions in the two ports, and the relevant analysis work is conducted to evaluate the control effect of the Emission Control Area (ECA) policies on pollutant emissions. Results show that the ship pollutant emissions for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 11,700, 6670, 248, and 232 tons, respectively. These results also indicate the possible presence of a large gap in the distribution trend of ship pollutant emissions, according to different ship types and sailing states. Moreover, the control effect of various ECA policies on pollutant emissions is not the same, that is, the impact of ECA policies on SO₂ and particulate matter is the largest, and that on NOX is minimal.
Finger vein is a new and promising trait in biometric recognition and some related progress have been achieved in recent years. Considering that there are many different sensors in a biometric system, sensor interoperability is a very important issue and still neglected in the state-ofthe-art finger vein recognition. Based on the analysis of the shortcomings in the current finger vein ROI extraction methods, this paper proposes a new superpixel based finger vein ROI extraction method with sensor interoperability. First, finger boundaries are determined by tracking superpixels which are very robust to image variations such as gray level and background noises. Furthermore, to handle finger displacement, the middle points of the detected finger boundaries are used to adjust finger direction. Finally, finger ROI is localized by the internal tangents of finger boundaries. Experimental results show that the proposed method can extract the ROIs accurately and adaptively from images which are captured by different sensors.978-1-4799-7824-3/15/$31.00
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