The definition of complex uncertainties in bspline surface by using normal type-2 triangular fuzzy number

In this paper, Normal Type-2 Triangular Fuzzy Number (NT2TFN) is used for defining complex uncertainties data to construct an approximation of B-spline surface. The type-2 fuzzy set is used to define the complex uncertainties due to type-1 fuzzy has limited efficiency to define the complex uncertainties problem.NT2TFN is based on the concept of Normal Type-2 Fuzzy Set Theory (NT2FST) and the Type-2 Fuzzy Number (T2FN). NT2TFNis used to define the complex uncertainties data before the three technique were implemented. These techniques include the fuzzification process by using alpha-cut operation with two determined value of alpha which is 0.5 and 0.8, the type-reduction process and the defuzzification process. Therefore, the finalize model of Normal Type-2Fuzzy B-spline Surface (NT2FBsS) for two determined value of alpha can be achieved and a new implementation to construct a geometry model by using NT2TFN on defining complex uncertainty data to fuse with Bspline surface is accomplishe

Utilising fuzzy interpolation Bezier curves for alphabet verification

In this paper, alphabet verification is conducted using fuzzy interpolation Bezier curves. Uncertain data can be defined by using the fuzzy number concept. Firstly, Fuzzification in the form of triangular fuzzy numbers is discussed. Then, the defuzzification process is implemented to produce crisp fuzzy data points. An error is obtained by comparing the defuzzified model of alphabet verification with the crisp model. The small error value obtained indicates that the fuzzy interpolation Bezier curve model is acceptable and can be used in modeling alphabet verification

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Image enhancement is an important method in the process of image processing. This paper proposes an image enhancement method base on an improved fuzzy c-means clustering. The method consists of the following steps: firstly, proposed a fuzzy c-means clustering with a cooperation center (FCM-co). Secondly, using the FCM-co, divide the image pixels into different clusters and marked membership values to those clusters. Thirdly, modify the membership values. Finally, calculate the new pixel gray levels. This enhancement method can overcome the disadvantage of overexposure and better retain image details. Through the experiment, the test results show that the proposed enhancement method could achieve better performance

Fuzzy image enhancement based on algebraic function and cycloid arc length

Fuzzy image enhancement is an important method in the process of image processing. In this paper, we present two intensifier operators in fuzzy image enhancement process based on algebraic function and cycloid arc length respectively. The first method directly uses the algebraic function as a membership intensifier operator. The second method also using a intensifier operator which established established by the cycloid arc length as the independent variable. In the last section, the test image is experimentally analyzed, and the results show that the method we proposed can improve enhance the contrast of the image

An image enhancement method based on a s-sharp function and pixel neighborhood information

Image enhancement is a significant field in image processing. This paper proposes an enhancement method based on an S-sharp function of grayscale transformation and neighborhood information. Firstly, a function is established based on the sine function. Then, the image threshold is added into the function. Finally, the result grayscales are modified by parameter, where parameter is determined by the image pixel neighborhood information. In general, in the result image, each pixel grayscale is determined by both the sine function with threshold and the parameter. In the experiment results, the NIEM method (we proposed) achieves better performance than the comparison algorithms. It gets the smallest MSE and the highest PSNR, SSIM. In image Lena test, MSE value:330.8151, PSNR value:22.9350, and SSIM value: 0.9451. In image Pout test, MSE value:132.0988, PSNR value:26.9218, and SSIM value: 0.9604

Perfectly normal type-2 fuzzy interpolation B-spline curve

In this paper, we proposed another new form of type-2 fuzzy data points(T2FDPs) that is perfectly normal type-2 data points(PNT2FDPs). These kinds of brand-new data were defined by using the existing type-2 fuzzy set theory(T2FST) and type-2 fuzzy number(T2FN) concept since we dealt with the problem of defining complex uncertainty data. Along with this restructuring, we included the fuzzification(alpha-cut operation), type-reduction and defuzzification processes against PNT2FDPs. In addition, we used interpolation B-soline curve function to demonstrate the PNT2FDPs.Comment: arXiv admin note: substantial text overlap with arXiv:1304.786

ATTENUATION OF WAVES FROM BOAT WAKES IN MIXED MANGROVE FOREST OF RHIZOPHORA AND BRUGUIERA SPECIES IN MATANG, PERAK

In Malaysia, there are several small rivers and estuaries which are frequented by fishing boats. The wave action due to the movement of boats impact the coastal morphology of the area. In this paper, we have studied the wave reduction in mixed mangrove forest of Rhizosphere and Bruguiera species based on field observations of waves from boat wakes in Sg. Sangga Kecil of Matang forest reserve, west coast of Peninsular Malaysia. The unique physical characteristics of Bruguiera sp. and Rhizophora sp. such as the intricate knee root and numerous pneumatophores, respectively, impact the wave amplitudes in the mangrove forest. The reduction of wave amplitudes in a 15 m long transect of mixed mangrove forest at a given study site has been analysed in the present study. It is found that the wave reduction for each 5-m distance from the vegetation edge ranged from 47.4% to 9.6%. However, on a cumulative basis the wave reduction inside the mixed mangrove forest ranged between 47.4% to 72.8%, with an average of 63%. As far as the vertical trend is concerned the wave reduction in (0-10cm) level was 88.7% while in (10-20cm) level it was found to be 61.2%

Conceptual of Type-2 Fuzzy Geometric Modelling

Geometric modelling is a method of data representation illustrated through the formation of curves and surfaces in various forms. The construction of curves and surfaces is complicated when it comes to data that has complex uncertainty characteristics. Type-1 Fuzzy Set Theory (T1FST) is unable to define this complex uncertainty problem. To overcome this problem, Type-2 Fuzzy Set Theory (T2FST) is used due to its ability to define a higher level of uncertainty problem. In certain cases, both uncertainty and complex uncertainty data occur when there is a combination of degrees of ambiguity in a collection of data sets that would be modelled through the representation of curves and surfaces. Therefore, this paper will review some significant reason for implementation of T1FST and T2FST in geometric modelling. A review on type-1 and type-2 in fuzzy geometric modelling is also presented

Type-2 Intuitionistic Interpolation Cubic Fuzzy Bézier Curve Modeling using Shoreline Data

The notion of fuzzy sets is fast becoming a key instrument in defining the uncertainty data and has increasingly been recognised by practitioners and researchers across different disciplines in recent decades. The uncertainty data cannot be modeled directly and this causes hindrance in obtaining accurate information for analysis or predictions. Hence, this paper contributes to another approach in which an application of type-2 intuitionistic fuzzy set (T-2IFS) in geometric modeling onto complex uncertainty data where the data are defined using the type-2 fuzzy concept. T-2IFS is the generalized forms of fuzzy sets, intuitionistic fuzzy sets, interval valued fuzzy sets, and interval-valued intuitionistic fuzzy sets. Based on the concept of T2IFS, type-2 intuitionistic fuzzy point (T-2IFP) is defined in order to generate a type-2 intuitionistic fuzzy control point (T-2IFCP). Following, the T-2IFCP will be blended with the Bernstein blending function through the interpolation method, resulting to a type-2 intuitionistic interpolation cubic fuzzy Bézier curve. Shoreline data is used as the data and further verifies that the model can be conceivably accepted. In conclusion, the proposed methods are reliable and can be expanded to many other areas