scholarly journals Classification of mini-dimmings associated with extreme ultraviolet eruptions by using graph theory

2016 ◽  
Vol 16 (2) ◽  
pp. 217-223
Author(s):  
S Bazargan ◽  
H Safari ◽  
H Kaashisaaz ◽  
◽  
◽  
...  
Keyword(s):  
Graph Theory ◽  
Extreme Ultraviolet

scholarly journals Rule based Classification of Graph Theory Concepts by Use Case Analysis

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
Anuja Bokhare ◽  
P. S. Metkewar ◽  
R. S. Walse
Keyword(s):  
Graph Theory ◽  
Case Analysis ◽  
Use Case ◽  
Rule Based

scholarly journals Graph Theory-Based Characterization and Classification of Household Photovoltaics

2021 ◽  
Vol 11 (22) ◽  
pp. 10999
Author(s):  
Jesús M. Ceresuela ◽  
Daniel Chemisana ◽  
Nacho López
Keyword(s):  
Graph Theory ◽  
Power Production ◽  
Expected Value ◽  
Maximum Power ◽  
Solar Panels ◽  
Zero Energy ◽  
Photovoltaic Panel ◽  
Clear Goal ◽  
Zero Energy Buildings

With the clear goal of improving photovoltaic (PV) technology performance towards nearly-zero energy buildings, a graph theory-based model that characterizes photovoltaic panel structures is developed. An algorithm to obtain all possible configurations of a given number of PV panels is presented and the results are exposed for structures using 3 to 7 panels. Two different classifications of all obtained structures are carried out: the first one regarding the maximum power they can produce and the second according to their capability to produce energy under a given probability that the solar panels will fail. Finally, both classifications are considered simultaneously through the expected value of power production. This creates structures that are, at the same time, reliable and efficient in terms of production. The parallel associations turn out to be optimal, but some other less expected configurations prove to be rated high.

An investigation into colour combination in paintings via graph theory

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Samaneh Jolany Vangah ◽  
Yousef Jamali ◽  
Mozaffar Jamali
Keyword(s):  
Image Processing ◽  
Graph Theory ◽  
Network Analysis ◽  
Visual Arts ◽  
Digital Image ◽  
Degree Distribution ◽  
Network Science ◽  
Node Centrality ◽  
Classification Of Images

Abstract In visual arts, painting is deeply reliant on the colour combination for its impact, depth and emotion. Recently, many studies have focused on image processing, regarding identification and classification of images, using some colour features such as saturation, hue, luminance and so forth. This study aims to delve into some of the painting styles from the perspective of graph theory and network science. We compared a number of famous paintings to find out the likely pattern that an artist uses for colour combination and juxtaposition. To achieve this aim, the digital image of a painting is converted to a graph where each vertex represents one of the painting’s colours. In this graph, two vertices would be adjacent if and only if the two relative colours could be found in at least two adjacent pixels in the digital image. Among the several tools for network analysis, clustering, node centrality and degree distribution are used. Outcomes showed that artists unconsciously are following subtle mathematical rules to reach harmony and coordination in their work.

A graph-based active learning method for classification of remote sensing images

2018 ◽  
Vol 16 (04) ◽  
pp. 1850023
Author(s):  
Lian-Zhi Huo ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
Active Learning ◽  
Image Classification ◽  
Selection Procedure ◽  
Experimental Tests ◽  
Maximization Problem ◽  
Support Vector ◽  
Training Samples

Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity selection procedure is converted to the densest k-subgraph [Formula: see text] maximization problem in graph theory. The [Formula: see text] maximization problem is solved by a greedy algorithm. The proposed technique is compared with competing methods adopted in RS community. Experimental tests are performed on very high resolution (VHR) multispectral and hyperspectral images. Experimental results demonstrate that the proposed technique leads to comparable or even better classification accuracies with respect to competing methods on the two datasets.

scholarly journals A complete classification of the complexity of the vertex 3-colourability problem for quadruples of induced 5-vertex prohibitions

2020 ◽  
Vol 22 (1) ◽  
pp. 38-47
Author(s):  
Dmitry S. Malyshev
Keyword(s):  
Graph Theory ◽  
Computational Complexity ◽  
Complete Classification ◽  
Induced Subgraphs ◽  
Simple Graphs ◽  
Forbidden Induced Subgraphs ◽  
The Third ◽  
Hereditary Class ◽  
Complexity Status

The vertex 3-colourability problem for a given graph is to check whether it is possible to split the set of its vertices into three subsets of pairwise non-adjacent vertices or not. A hereditary class of graphs is a set of simple graphs closed under isomorphism and deletion of vertices; the set of its forbidden induced subgraphs defines every such a class. For all but three the quadruples of 5-vertex forbidden induced subgraphs, we know the complexity status of the vertex 3-colourability problem. Additionally, two of these three cases are polynomially equivalent; they also polynomially reduce to the third one. In this paper, we prove that the computational complexity of the considered problem in all of the three mentioned classes is polynomial. This result contributes to the algorithmic graph theory.

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