• Kyungpook Mathematical Journal
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• v.61 no.3
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• pp.661-669
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• 2021

For a simple, undirected graph G = (V, E), a perfect Roman dominating function (PRDF) f : V → {0, 1, 2} has the property that, every vertex u with f(u) = 0 is adjacent to exactly one vertex v for which f(v) = 2. The weight of a PRDF is the sum f(V) = ∑_v∈V f(v). The minimum weight of a PRDF is called the perfect Roman domination number, denoted by γ_R^P(G). Given a graph G and a positive integer k, the PRDF problem is to check whether G has a perfect Roman dominating function of weight at most k. In this paper, we first investigate the complexity of PRDF problem for some subclasses of bipartite graphs namely, star convex bipartite graphs and comb convex bipartite graphs. Then we show that PRDF problem is linear time solvable for bounded tree-width graphs, chain graphs and threshold graphs, a subclass of split graphs.

• Journal of Life Science
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• v.15 no.3 s.70
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• pp.365-373
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• 2005

The quantitative inheritance of some yield characters in Gosyium spp was carried out by means of a $10\times10$ diallel cross. In this study, 45 combinations of $F_1\;and\;F_2$ generations were genetically analyzed through 10 different cultivars diallel cross population of cotton (Gosyium spp) at an experimental field. The results of Vr-Wr graph analysis of six characters such as number of boll, boll weight, lint weight per boll, 100 seeds weight, fiber fineness and fiber length in those combinations by the Hayman's method were as follow: 1. The significant difference was observed from the genetic variance of all the examined characters. 2. On based the Vr-Wr graphical analysis, $F_1$ showed a complete dominance in all the experimental characters except boll weight, lint weight per boll and fiber fineness, but the dominance degree and gene arrangement of $F_2$ were somewhat different from those of $F_1$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4126-4142
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• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.153-160
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• 2022

A greedy algorithm is proposed to select a subset of nodes of a graph for bandlimited graph signals in which each signal value is generated with its weight. Since graph signals are weighted, we seek to minimize the weighted reconstruction error which is formulated by using the QR factorization and derive an analytic result to find iteratively the node minimizing the weighted reconstruction error, leading to a simplified iterative selection process. Experiments show that the proposed method achieves a significant performance gain for graph signals with weights on various graphs as compared with the previous novel selection techniques.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.178-184
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• 2014

Tendon driven robot mechanisms have many advantages such as allowing miniaturization and light-weight designs and/or enhancing flexibility in the design of structures. When designing or analyzing tendon driven mechanisms, it is important to determine how the tendons should be connected and whether the designed mechanism is easily controllable. Graph representation is useful to view and analyze such tendon driven mechanisms that are complicatedly interconnected between mechanical elements. In this paper, we propose a method of generalized graph representation that provides us with an intuitive analysis tool not only for tendon driven manipulators, but also various other kinds of mechanical systems which are combined with tendons. This method leads us to easily obtain structure matrix - which is the one of the most important steps in analyzing tendon driven mechanisms.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.517-524
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• 2020

Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.

• Bulletin of the Korean Mathematical Society
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• v.56 no.5
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• pp.1199-1210
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• 2019

In this paper, we consider a class of inhomogeneous random intersection graphs by assigning random weight to each vertex and two vertices are adjacent if they choose some common elements. In the inhomogeneous random intersection graph model, vertices with larger weights are more likely to acquire many elements. We show the Poisson convergence of the number of induced copies of a fixed subgraph as the number of vertices n and the number of elements m, scaling as $m={\lfloor}{\beta}n^{\alpha}{\rfloor}$ (${\alpha},{\beta}>0$), tend to infinity.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.189-194
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• 2012

In this paper, we propose a query expansion method based on word graphs using pseudo-relevant and pseudo non-relevant documents to achieve performance improvement in information retrieval. The initially retrieved documents are classified into a core cluster when a document includes core query terms extracted by query term combinations and the degree of query term proximity. Otherwise, documents are classified into a non-core cluster. The documents that belong to a core query cluster can be seen as pseudo-relevant documents, and the documents that belong to a non-core cluster can be seen as pseudo non-relevant documents. Each cluster is represented as a graph which has nodes and edges. Each node represents a term and each edge represents proximity between the term and a query term. The term weight is calculated by subtracting the term weight in the non-core cluster graph from the term weight in the core cluster graph. It means that a term with a high weight in a non-core cluster graph should not be considered as an expanded term. Expansion terms are selected according to the term weights. Experimental results on TREC WT10g test collection show that the proposed method achieves 9.4% improvement over the language model in mean average precision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.612-627
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• 2009

The high-level contribution of this paper is to illustrate the effectiveness of using graph theory tree traversal algorithms (pre-order, in-order and post-order traversals) to generate the chain of sensor nodes in the classical Power Efficient-Gathering in Sensor Information Systems (PEGASIS) data aggregation protocol for wireless sensor networks. We first construct an undirected minimum-weight spanning tree (ud-MST) on a complete sensor network graph, wherein the weight of each edge is the Euclidean distance between the constituent nodes of the edge. A Breadth-First-Search of the ud-MST, starting with the node located closest to the center of the network, is now conducted to iteratively construct a rooted directed minimum-weight spanning tree (rd-MST). The three tree traversal algorithms are then executed on the rd-MST and the node sequence resulting from each of the traversals is used as the chain of nodes for the PEGASIS protocol. Simulation studies on PEGASIS conducted for both TDMA and CDMA systems illustrate that using the chain of nodes generated from the tree traversal algorithms, the node lifetime can improve as large as by 19%-30% and at the same time, the energy loss per node can be 19%-35% lower than that obtained with the currently used distance-based greedy heuristic.