scholarly journals Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuehong Zhu ◽  
Ying Han

In modern society, with the rapid increase of population and the serious shortage of resources, the marine environment has been destroyed; there are also many people who go out to sea without permission, regardless of the legal constraints, fishing. This kind of behavior leads the marine environment to get worse and worse, so the real-time monitoring of the marine environment is very necessary. The main article marine environment monitoring, virtual reality technology, and fuzzy C-means clustering algorithm combine to improve the efficiency of monitoring and processing power of the data information. Through the application of virtual reality technology in the marine environment monitoring base and real-time simulation of the dynamics of the ocean, the monitoring personnel can understand the emergencies on the sea in time; the fuzzy C-means clustering algorithm is applied to the server receiving the data to classify the data. It is found in the experiment that when virtual reality technology and fuzzy C-means clustering algorithm are not used, the data of marine environment monitoring takes more than 1.3 s to return to the server, but, after applying two advanced technologies, the return efficiency is greatly improved, and the time consumed is less than 0.82 s. The results show that virtual reality technology and fuzzy C-means clustering algorithm can improve the efficiency of environmental monitoring, and through virtual reality technology, real-time monitoring of the marine environment can be achieved; in the absence of people out to sea, the actual situation on the sea can be clearly understood; and fuzzy C-means clustering algorithm can improve the speed of data processing, so that the monitoring personnel can solve the problem faster.

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.


2005 ◽  
Vol 29 (8-9) ◽  
pp. 375-380 ◽  
Author(s):  
Jesús Lázaro ◽  
Jagoba Arias ◽  
José L. Martín ◽  
Carlos Cuadrado ◽  
Armando Astarloa

2012 ◽  
Vol 605-607 ◽  
pp. 1769-1771 ◽  
Author(s):  
Xing Hong Kuang ◽  
Zhi Yu Wang ◽  
Ceng Yang ◽  
Hai Bo Huo ◽  
Yan Xiang Wu

The real-time monitor ability will be improved when the Ocean buoy technology is used on the marine environment monitoring. The design of the monitoring system based on marine environment buoy were described in detail in the paper, which including the integrated hardware and the monitoring software. The ocean remote monitoring function was implemented using the developed software system.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 32634-32649
Author(s):  
Ge Liu ◽  
Guosheng Rui ◽  
Wenbiao Tian ◽  
Liyao Wu ◽  
Tiantian Cui ◽  
...  

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