P-116: APL-Adaptive Variable Weight Function for Improving Low Gray-Level Linearity and Gray-Level Expression in PDP-TV

2006 ◽  
Vol 37 (1) ◽  
pp. 631 ◽  
Author(s):  
Sung-Jin Kang ◽  
Jung-Hwan Shin ◽  
Dong-Ho Lee ◽  
Sung-Il Chien ◽  
Il-Hun Choi
Keyword(s):  
Weight Function ◽  
Gray Level ◽  
Variable Weight

Grey cluster evaluation models based on mixed triangular whitenization weight functions

2015 ◽  
Vol 5 (3) ◽  
pp. 410-418 ◽  
Author(s):  
Si-feng Liu ◽  
Yingjie Yang ◽  
Zhi-geng Fang ◽  
Naiming Xie
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Weight Functions ◽  
The Novel ◽  
Content Type ◽  
Clustering Model ◽  
Cluster Evaluation ◽  
Fixed Weight ◽  
Variable Weight ◽  
Evaluation Models

Purpose – The purpose of this paper is to present two novel grey cluster evaluation models to solve the difficulty in extending the bounds of each clustering index of grey cluster evaluation models. Design/methodology/approach – In this paper, the triangular whitenization weight function corresponding to class 1 is changed to a whitenization weight function of its lower measures, and the triangular whitenization weight function corresponding to class s is changed to a whitenization weight function of its upper measures. The difficulty in extending the bound of each clustering indicator is solved with this improvement. Findings – The findings of this paper are the novel grey cluster evaluation models based on mixed centre-point triangular whitenization weight functions and the novel grey cluster evaluation models based on mixed end-point triangular whitenization weight functions. Practical implications – A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function. Originality/value – Particularly, compared with grey variable weight clustering model and grey fixed weight clustering model, the grey cluster evaluation model using whitenization weight function is more suitable to be used to solve the problem of poor information clustering evaluation. The grey cluster evaluation model using endpoint triangular whitenization weight functions is suitable for the situation that all grey boundary is clear, but the most likely points belonging to each grey class are unknown; the grey cluster evaluation model using centre-point triangular whitenization weight functions is suitable for those problems where it is easier to judge the most likely points belonging to each grey class, but the grey boundary is not clear.

A Variable Weight Function Model in Geological Variables Function Research

2013 ◽  
Vol 785-786 ◽  
pp. 1423-1429
Author(s):  
Wen Bo Liu ◽  
Lai Jun Liu
Keyword(s):  
Weight Function ◽  
Mineral Resources ◽  
Process Variable ◽  
Mathematics Model ◽  
Constant Weight ◽  
Connection Degree ◽  
State Variable ◽  
Variable Weight ◽  
The Status ◽  
Constant Coefficients

In mineral resources prediction and other research of geological variables, stability exactness of quantitative models concern modeling conditions, geological variables from model and the status of the variable. In traditional geological modeling process, variable support is measured under some contrains weight and this kind of weight is characterized by constant coefficients. Constant weight[1] has some limitations due to structuredness and dependency of variable. For overcoming the inflexibility of constant weight, this paper proposes geological variable mathematics model basedd state variable vector. We revise existing form of state variable weight and provide logarithm state variable vector as measurement level of geological variable weight coefficients. According to 1:200000 scale geochemistry measured data from Baishan area, we calculate the samples unit connection degree based on exponent and logarithm state variable vector and compare the connection degree based on constant weight. The connection degree sorting has the similarity as a whole among them, but there is the obvious difference locally. We can conclude that geological variable weight function based on state variable vector is more flexible and fine.

scholarly journals Chebyshev approximation of the multivariable functions by some nonlinear expressions

2021 ◽  
pp. 18-22
Author(s):  
Petro Malachivskyy
Keyword(s):  
Weight Function ◽  
Least Squares ◽  
Iterative Scheme ◽  
Chebyshev Approximation ◽  
Method Of Least Squares ◽  
Functional Transformation ◽  
Generalized Polynomial ◽  
Variable Weight

A method for constructing a Chebyshev approximation of the multivariable functions by exponential, logarithmic and power expressions is proposed. It consists in reducing the problem of the Chebyshev approximation by a nonlinear expression to the construction of an intermediate Chebyshev approximation by a generalized polynomial. The intermediate Chebyshev approximation by a generalized polynomial is calculated for the values of a certain functional transformation of the function we are approximating. The construction of the Chebyshev approximation of the multivariable functions by a polynomial is realized by an iterative scheme based on the method of least squares with a variable weight function.

Quantitative analysis of the cities’ innovation capacity based on grey variable weight clustering

2015 ◽  
Vol 5 (3) ◽  
pp. 344-353
Author(s):  
Yeqing Guan ◽  
Hua Liu ◽  
Ying Zhu
Keyword(s):  
Quantitative Analysis ◽  
Weight Function ◽  
Design Methodology ◽  
Clustering Method ◽  
Content Type ◽  
Clustering Model ◽  
Variable Weight ◽  
Grey Clustering ◽  
Innovation Capacity

Purpose – The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found. Design/methodology/approach – First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps. Findings – The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering. Originality/value – This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

On the Form of Decision Weight Function

2002 ◽  
Author(s):  
Shyhnan Liou ◽  
Chung-Ping Cheng
Keyword(s):  
Weight Function

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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