Monitoring System Design for Ships Network

Article Preview

Abstract:

This paper designs a Vessel Remote Video Monitoring System based on internet protocol. The system uses positioning and network transmission technology to transmit real-time images of ships offshore to the server, to set up the client and visual monitoring ends. Ships monitoring personnel enter WEB monitoring platform with different permissions, to monitor and process ships image data through the network view, scheduling, monitoring terminals and other system modules. It also adds density ratio-based automatic alarm module to realize the intelligent early warning of offshore Ships density. Experiments show that the system has a better result to achieve vessel images' intelligent remote monitoring and early warning.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

1542-1545

Citation:

Online since:

November 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] HONG Kang, LIANG Hong, YANG Changsheng. Visual Simulation of Anti-torpedo System[C]. 2009 IEEE Youth Conference on information, Computing and Telecommunications Microsoft Cup, IEEE China Student paper Contest, 2009, 9: 243-246.

DOI: 10.1109/ycict.2009.5382379

Google Scholar

[2] Yam suchi. Brian Decentralized coordination for multirobot exploration[J], Robotics and Autonomous Systemss, 1999, 29(1): 543~544.

Google Scholar

[3] A sama H, et al. Design of an autonomous and distributed robot system[J]. ACTRESS[A] Proc IEEE/RSJ 2000: 283~286.

Google Scholar

[4] H.X. Wang,B. Luo Q.B. Zhang,S. Wi. Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm. Patrrern Recognition Letters, 25(16): 1799-1809, December 2004.

DOI: 10.1016/j.patrec.2004.07.007

Google Scholar

[5] M.A.T. Figueirdo A.K. Jain. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 381~396.

DOI: 10.1109/34.990138

Google Scholar