• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.213-217
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• 2007

소프트웨어 테스팅은 소프트웨어의 개발 과정에 있어서 가장 중요하고 많은 비용이 드는 부분이다. 소프트웨어 테스팅을 수동으로 행하는 것은 많은 문제를 발생시킬 수 있다. 소프트웨어 자동 테스팅을 하기 위해서 최근 활발히 연구되고 있는 부분이 모델 기반 소프트웨어 자동 테스팅 기법이다. 본 논문에서는 UML 모델 기반 테스트 케이스 자동 생성 기법을 제안한다. UML state chart 로 모델링 된 테스트 대상 소프트웨어를 제안된 자료구조에 저장 한 후, 이를 flow graph 로 변환한다. 최종적으로 변환된 flow graph 에서 테스트 케이스를 생성한다.

• 전기의세계
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• v.19 no.5
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• pp.1-7
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• 1970

Starting from the review of signal flow graphs and flow graphs, this paper gives an example of sampled-data systems for Sedlar & Bekey's formulation. In this purpose it discussed the difference between Mason's signal-flow graphs and Coates flow graphs for drawing th flow graph of a linear system, and then a new flow-graph symbol introduced in order to distinguish between continuous and discrete systems. Thus, the paper is analysed and compared with a sampled-data systems between conventional methods and new method of signal flow graphs.

• International Journal of Aerospace System Engineering
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• v.2 no.1
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• pp.47-52
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• 2015

The transient phenomenon of self-powered energy-harvesting is assessed using a bond-graph method. The bond-graph is an energy-based approach to describing physical-dynamic systems. It shows power flow graphically, which helps us understand the behavior of complicated systems in simple terms. Because energy-harvesting involves conversion of power in mechanical form to the electrical one, the bond-graph is a good tool to analyze this power flow. Although the bond-graph method can be used to calculate the dynamics of combining mechanical and electrical systems simultaneously, it has not been used for harvesting analysis. We demonstrate the usability and versatility of bond-graph for not only steady analysis but also transient analysis of harvesting.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.171-180
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• 2002

The effective and accurate data flow problem analysis uses the dominator tree and DJ graphs. The data flow problem solving is to safely reduce the flow graph to the dominator tree. The flow graph replaces a parse tree and used to accurately reduce either reducible or irreducible flow graph to the dominator tree. In this paper, in order to utilize Tarian's path compress algorithm, the Top node finding algorithm is suggested and the existing delay reduction algorithm is improved using Path compression. The delayed reduction a1gorithm using path compression actually compresses the pathway of the dominator tree by hoisting the node while reducing to delay the DJ graph. Realty, the suggested algorithm had hoisted nodes in 22% and had compressed path in 20%. The compressed dominator tree makes it possible to analyze the effective data flow analysis and brings the improved effect for the complexity of code optimization process with the node hoisting effect of code optimization process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Journal of the Korean Mathematical Society
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• v.55 no.5
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• pp.1091-1101
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• 2018

We concern in this paper the graph Kazdan-Warner equation $${\Delta}f=g-he^f$$ on an infinite graph, the prototype of which comes from the smooth Kazdan-Warner equation on an open manifold. Different from the variational methods often used in the finite graph case, we use a heat flow method to study the graph Kazdan-Warner equation. We prove the existence of a solution to the graph Kazdan-Warner equation under the assumption that $h{\leq}0$ and some other integrability conditions or constrictions about the underlying infinite graphs.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2536-2542
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• 2000

The dominator tree presents the dominance frontier from directed graph to the tree. we present the effective algorithm for constructing the dominator tree from arbitrarY directed graph. The reducible flow graph was reduced to dominator tree after dominator calculation. And the irreducible flow graph was constructed to dominator-join graph using join-edge information of information table. For reducing the dominator tree from dominator-join graph, we present the effective sequency reducible algorithm and delay reducible algorithm.

• 전기의세계
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• v.22 no.4
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• pp.25-29
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• 1973

The computation of transmittances between arbitrary input and output nodes is of particular interest in the signal flow graph theory imput. The signal flow matrix [T] can be defined by [X]=-[T][X] where [X] and [Y] are input nose and output node matrices, respectively. In this paper, the followings are discussed; 1) Reduction of nodes by reforming the signal flow matrix., 2) Solution of input-output relationships by means of Gauss-Jordan reduction method, 3) Extension of the above method to the matrix signal flow graph.

• 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 KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.876-880
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• 2009

A control flow graph(CFG) is an essential data structure for program analyses based on graph theory or control-/data- flow analyses. Esterel is an imperative synchronous language and its synchronous parallelism makes it difficult to construct a CFG of an Esterel program. In this work, we present a method to construct over-approximated CFGs for Esterel. Our method is very intuitive and generated CFGs include not only exposed paths but also invisible ones. Though the CFGs may contain some inexecutable paths due to complex combinations of parallelism and exception handling, they are very useful for other program analyses.