• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.91-98
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• 2009

In this paper, this model ensures confidentiality and integrity of mandatory access cotrol policy which based on fuzzy function with importance of information. And it solves authorization abuse problem through role graph creation algorithm and flowing policy that security grade is applied. Because this model composes role hierarchy which bind similar role concept to apply to commercial environment, it has expansile advantage by large scale security system as well as is easy that add new role.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.142-151
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• 2009

Previous document clustering method, NSTC measures similarities between two document pairs using TF-IDF during web document clustering. In this paper, we propose new similarity measure using common phrase-based relational graph, not TF-IDF. This method suggests that weighting common phrases by relational graph presenting relationship among common phrases in document collection. And experimental results indicate that proposed method is more effective in clustering document collection than NSTC.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.120.3-120
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• 2001

We propose a new method of path planning for cleaning robots. Path planning problem for cleaning robots is different from conventional path planning problems in which finding a collision-free trajectory from a start point to a goal point is focused. In the case of cleaning robots, however, a planned path should cover all area to be cleaned. To resolve this problem in a systematic way, we propose a method based on a graph model as follows: at first, partition a given map into proper regions, then transform a divided region to a vertex and a connectivity between regions to an edge of a graph. Finally, a region is divided into sub-regions so that the graph has a unary tree which is the simplest Hamilton path. The effectiveness of the proposed method is shown by computer simulation results.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.321-328
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• 2013

In this paper, we present a technique for obtaining conflict-free schedules for real-time automation protocol for industrial Ethernet (RAPIEnet) switches. Mathematical model of the switch is obtained using graph theory. Initially network traffic entry and exit parts in a single RAPIEnet switch are identified, so that a bipartite conflict graph can be constructed. The obtained conflict graph is transformed to three kinds of matrices to be used as inputs for our simulation model, and selection of any of the matrix forms is application-specific. A greedy edge-coloring algorithm is used to schedule the network traffic and to solve the minimum coloring problem. After scheduling, empty slots are identified for forwarding the non real-time traffic of asynchronous devices. Finally, an algorithm for synchronizing the schedules of adjacent switches is proposed using edge-contraction and minors. All simulations were carried out using Matlab.

• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.231-241
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• 2011

Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to guarantee successful termination of business processes at the design phase. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.65-67
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• 2004

Change of state of Channel between two wireless terminals which is caused by noise and multiple environmental conditions for happens frequently from the Wireles Network. So, When it is like that planning a wireless network protocol or performance analysis, it follows to change of state of time-varying channel and packet the analysis against a transmission efficiency is necessary. In this paper, analyzes transmission time of a packet and a packet in a time-varying and packet based Wireless Network. To reflecte the feature of the time-varying channel, we use a Signal Flow Graph model. From the model the mean of transmission time and the mean of queue length of the packet are analyzed in terms of the packet distribution function, the packet transmission service time, and the PER of the time-varying channel.

• Journal of Information Science Theory and Practice
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• v.6 no.1
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• pp.18-30
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• 2018

Interest in open data is continuing to grow around the world. In particular, open government data are considered an important element in securing government transparency and creating new industrial values. The South Korean government has enacted legislation on opening public data and provided diversified policy and technical support. However, there are also limitations to effectively utilizing open data in various areas. This paper introduces an administrative district knowledge model to improve the sharing and utilization of open government data, where the data are semantically linked to generate a knowledge graph that connects various data based on administrative districts. The administrative district knowledge model semantically models the legal definition of administrative districts in South Korea, and the administrative district knowledge graph is linked to data that can serve as an administrative basis, such as addresses and postal codes, for potential use in hospitals, schools, and traffic control.

• 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.