通道混合器运用原理解析

通道混合器(channel mixer)是设计软件中一个相对冷门却功能强大的调色工具,而之所以冷门,恐怕跟这个工具的设计理念与传统的调色工具,比如色阶(levels),曲线(curves)等运行原理有着较大的分别有关,通道混合器让人困惑的地方在于使用者对于在单色通道中引入不同的色彩区域这个概念是很少接触到的。在通道混合器中,通道决定颜色,色彩区域决定范围,这种独树一帜的调色思路,抬高了这一工具的使用门槛,使得这个强大的调色工具一直没有得到足够的重视。该文从通道入手,将调色中的通道概念与现实中的饮料机相比较,用大家都能理解的概念,深入浅出的剖析通道混合器的运作原理。

Adjacent vertex-distinguishing total colorings of K_s∨K_t

Let G be a simple graph and f be a proper total kcoloring of G. The color set of each vertex v of G is the set of colors appearing on v and the edges incident to v. The coloring f is said to be an adjacent vertex-distinguishing total coloring if the color sets of any two adjacent vertices are distinct. The minimum k for which such a coloring of G exists is called the adjacent vertex-distinguishing total chromatic number of G. The join graph of two vertex-disjoint graphs is the graph union of these two graphs together with all the edges that connect the vertices of one graph with the vertices of the other. The adjacent vertex-distinguishing total chromatic numbers of the join graphs of an empty graph of order s and a complete graph of order t are determined.

A Decision Model Based on Grey Rough Sets Integration with Incomplete Information

In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

In order to promote the application of Beijing-1 small satellite(BJ-1) remote sensing data,the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining.An improved Intensity-Hue-Saturation(IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images,in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree.This improved IHS fusion algorithm outper-forms traditional IHS fusion method to a certain extent,evidenced by its ability in preserving spectral information and enhancing spatial details.Dempster-Shafer(D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier.Based on this study,we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas,and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers,improve the overall accuracy and produce a more reliable land cover map.

Adjacent-Vertex-Distinguishing Total Chromatic Number of P_m×K_n

Let G be a simple graph. Let f be a mapping from V（G） U E（G） to {1, 2,..., k}. Let Cf（v） = {f（v）} U {f（vw）｜w ∈ V（G）,vw ∈ E（G）} for every v ∈ V（G）. If f is a k-propertotal-coloring, and if Cf（u） ≠ Cf（v） for uv ∈ V（G）,uv E E（G）, then f is called k-adjacentvertex-distinguishing total coloring of G（k-AVDTC of G for short）. Let χat（G） = min{k｜G has a k-adjacent-vertex-distinguishing total coloring}. Then χat（G） is called the adjacent-vertex-distinguishing total chromatic number. The adjacent-vertex-distinguishing total chromatic number on the Cartesion product of path Pm and complete graph Kn is obtained.