Paper Titles
Database Design of Three Dimensional Simulation and Optimization System for Mine Ventilation Network
p.3065
Application of Fusioncharts in Web System
p.3069
Study on the Equipment Management System Based on Internet of Things
p.3074
An Fuzzy Petri Net Model of Accident Evolution Process of Coal and Gas Outburst
p.3079
Fuzzy Clustering for Military Decision Analysis with Remote Sensing Information
p.3085
Research on a Scheme for Improving the Chord Routing Algorithm
p.3089
An Improved Dynamic Programming Algorithm for Bitonic TSP
p.3094
The Research of ARMA Model in CPI Time Series
p.3099
A Novel Fingerprint Matching Algorithm Based on Compatible Multi-Area Alignment
p.3104

Fuzzy Clustering for Military Decision Analysis with Remote Sensing Information

Article Preview

Abstract:

In ocean military actions, there are many environmental factors to influent the military decision. In this paper, the fuzzy C-means (FCM) clustering is used to evaluate the influents of ocean environment, including sea winds, sea wave, sea current and the sea surface temperature gradient in military game. To different military application, the influence coefficient are different, in this paper a weighting exponent is import to estimate the influence grade of the entire ocean environment. The experimental results demonstrate that this algorithms utility in the classification of ocean area of military game.

You might also be interested in these eBooks
Instruments, Measurement, Electronics and Information Engineering View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

3085-3088

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.3085

Citation:

Cite this paper

Online since:

August 2013

Authors:

Ji Feng Yang, Yong Zhi Liu, Bing Jie Liu, Wei Liang Chen

Keywords:

Current, Fuzzy C-Means Clustering, Sea Surface Temperature Gradient, Sea Winds, Wave

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] Kachouie, Nezamoddin N., Alirezaie, Javad, Raahemifar, Kaamran. Genetically derived fuzzy C-means clustering algorithm for segmentation. Canadian Conference on Electrical and Computer Engineering, 2003, v 2, 1119-1122.

DOI: 10.1109/ccece.2003.1226093

Google Scholar

[2] Ronhua Tao, Jie Chen, Biao chen, Chuihua Liu. GLCM and Fuzzy Clustering for Ocean Features Classification. Proc of the 2010 IEEE Int'l. Conf on MVHI, 1340-1343.

Google Scholar

[3] Xizhao Wang, Yadong Wang, Lijuan Wang. Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Letters 25(2004) 1123-1132.

DOI: 10.1016/j.patrec.2004.03.008

Google Scholar

[4] Krinidis, S. A Robust Fuzzy Local Information C-Means Clustering Algorithm. Image Processing, IEEE Transactions on Date of Publication: May 2010, Volume: 19, Issue: 5 , Page(s): 1328 – 1337.

DOI: 10.1109/tip.2010.2040763

Google Scholar

[5] Sivakumar, S., Chandrasekar, C. Lungs image segmentation through weighted FCM. Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference, Page(s): 109 – 113.

DOI: 10.1109/racss.2012.6212707

Google Scholar

[6] Zhang D, Kamel M, Elmasry M. Fuzzy clustering neural network(FCNN): competitive learning and parallel architecture. Intellingent and Fuzzy Systems, v 2, 289-298(1994).

DOI: 10.3233/ifs-1994-2402

Google Scholar

[7] Yu J, Huan HK, Tian SF. An efficient optimality test for the fuzzy C-means algorithm. Proc of the 2002 IEEE Int'l. Conf on Fuzzy Systems. 789-795(2002).

Google Scholar

[8] Tian Junwei, Huang Yongxuan. Histogram Constraint Based Fast FCM Cluster Image Segmentation. Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on Topic(s): Components, Circuits, Devices & Systems ; Computing & Processing (Hardware/Software) ; Power, Energy, & Industry Applications. Page(s): 1623 - 1627.

DOI: 10.1109/isie.2007.4374847

Google Scholar