Modification of Fuzzy c-Means Method Using a Nonlinear Vector Criterion

2010 ◽  
Vol 42 (12) ◽  
pp. 13-21
Author(s):  
Anatoliy F. Bulat ◽  
Elena M. Kiseleva ◽  
Sergey A. Pichugov ◽  
Oleg B. Blyuss
Keyword(s):  
Vector Criterion ◽  
Fuzzy C Means ◽  
Nonlinear Vector

Method for Assessing the Ecological Risk to the Population in Areas Outside the Planning Zones of Emergency Protection Measures Around the NPP

2020 ◽  
Vol 17 (5) ◽  
pp. 34-47
Author(s):  
V. M. Polyakov ◽  
Z. S. Agalarov
Keyword(s):  
Ecological Risk ◽  
Environmental Risk ◽  
Risk Taking ◽  
Late Stage ◽  
Simulation Modeling ◽  
Radiation Accident ◽  
Emergency Protection ◽  
Protection Measures ◽  
Vector Criterion

The article offers a method for assessing the environmental risk in the territories adjacent to the planning zone of emergency protection measures around the NPP. The method is based on simulation modeling of territory pollution, which is formed at the late stage of a radiation accident and zoning of territories by risk, taking into account the characteristics of the population’s life in a potentially dangerous territory. A vector criterion of environmental risk is proposed that allows zoning these territories according to the degree of danger to the population.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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