• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.2
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• pp.93-99
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• 2001

A new type geometric and mathematical network reduction model is introduced. Large-scale network is analyzed with analytic approach. The graph has many nodes, branches and loops. Circuit equation are obtained from these elements and connection rule. In this paper, the analytic relation between voltage source has a mutual different graphic property. Node-reduction procedure is achieved with this circuit property. Consequently voltage source value is included into the adjacent node-analyzing equation. A resultant model equations are reduced as much as voltage source number. Matrix rank is (n-1-k), where n, k is node and voltage source number. The reduction procedure is described and verified with geometric principle and circuit theory. Matrix type circuit equation can be composed with this technique. The last results shall be calculated by using computer.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.278-286
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• 2000

A new large-scale network geometric analysis is introduced. For a large-scale circuit, it must be analyzed with a geometric diagram and figure. So many equations are induced from a geometric loop-node diagram. The results are arranged into a simple matrix, of course. In case of constructing a network diagram, it is not easy to handle voltage and current sources together. Geometric loop analysis is related to voltage sources, and node analysis is to current sources. The reciprocal transfer is possible only to have series or parallel impedance. If not having this impedance, in order to obtain equivalent circuit, many equations must be derived. In this paper a loop-reduction method is proposed. With this method current source branch is included into the other branch, and disappears in circuit diagram. So the number of independent circuit equations are reduced as much as that of current sources. The number is not (b-n+1), but (b-n+1-p). Where p is the number of current sources. The reduction procedure is verified with a geometric principle and circuit theory. A resultant matrix can be constructed directly from this diagram structure, not deriving circuit equations. We will obtain the last results with the help of a computer.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.193-202
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• 2021

The update of a high-precision map was carried out by modifying the geometric information using ortho-images or point-cloud data as the source data and then reconstructing the relationship between the spatial objects. These series of processes take considerable time to process the geometric information, making it difficult to apply real-time route planning to a vehicle quickly. Therefore, this study proposed a method to update the road network for route planning using a graph data structure and storage type of graph data structure considering the characteristics of the road network. The proposed method was also reviewed to assess the feasibility of real-time route information transmission by applying it to actual road data.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.6
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• pp.393-399
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• 2022

During the ship hull design process, resistance performance estimation is generally calculated by simulation using computational fluid dynamics. Since such hull resistance performance simulation requires a lot of time and computation resources, the time taken for simulation is reduced by CPU clusters having more than tens of cores in order to complete the hull design within the required deadline of the ship owner. In this paper, we propose a method for estimating resistance performance of ship hull by simulation using a graph neural network. This method converts the 3D geometric information of the hull mesh and the physical quantity of the surface into a mathematical graph, and is implemented as a deep learning model that predicts the future simulation state from the input state. The method proposed in the resistance performance experiment of simple hull showed an average error of about 3.5 % throughout the simulation.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Annual Conference of KIPS
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• 2019.05a
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• pp.498-500
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• 2019

Point cloud is a flexible set of points that can provide a scalable geometric representation which can be applied in different computer graphic task. We propose a method based on EdgeConv and densely connected layers to aggregate the features for better classification. Our proposed approach shows significant performance improvement compared to the state-of-the-art deep neural network-based approaches.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.451-460
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• 2012

Natural disasters often lead to regional failures that can cause network nodes and links co-located in a large geographical area to fail. Novel approaches are required to assess the network vulnerability under such regional failures. In this paper, we investigate the vulnerability of networks by considering the geometric properties of regional failures and network nodes. To evaluate the criticality of node locations and determine the critical areas in a network, we propose the concept of ${\alpha}$-critical-distance with a given failure impact ratio ${\alpha}$, and we formulate two optimization problems based on the concept. By analyzing the geometric properties of the problems, we show that although finding critical nodes or links in a pure graph is a NP-complete problem, the problem of finding critical areas has polynomial time complexity. We propose two algorithms to deal with these problems and analyze their time complexities. Using real city-level Internet topology data, we conducted experiments to compute the ${\alpha}$-critical-distances for different networks. The computational results demonstrate the differences in vulnerability of different networks. The results also indicate that the critical area of a network can be estimated by limiting failure centers on the locations of network nodes. Additionally, we find that with the same impact ratio ${\alpha}$, the topologies examined have larger ${\alpha}$-critical-distances when the network performance is measured using the giant component size instead of the other two metrics. Similar results are obtained when the network performance is measured using the average two terminal reliability and the network efficiency, although computation of the former entails less time complexity than that of the latter.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.281-285
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• 2008

Wireless sensor networks are closely related with the geometric environment in which they are deployed. We consider the probable case when a routing protocol runs on an environment with many complex obstacles like downtown surroundings. In addition, there are no unrealistic assumptions in order to increase practicality of the protocol. Our goal is to find a routing protocol for maximizing network lifetime by using only connectivity information in the complex sensor network environment. We propose a topology-based routing algorithm that accomplishes good performance in terms of network lifetime and routing complexity as measures. Our routing algorithm makes routing decision based on a weighted graph as topological abstraction of the complex network. The graph conduces to lifetime enhancement by giving alternative paths, distributing the skewed burden. An energy estimation method is used so as to maintain routing information without any additional cost. We show how our approach can be used to maximize network lifetime and by extensive simulation we prove that out approach gives good results in terms of both measures-network lifetime and routing complexity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.285-292
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• 2013

This study proposes a network matching method based on a network division technique. The proposed method generates polygons surrounded by links of the original network dataset, and detects corresponding polygon group pairs using a intersection-based graph clustering. Then corresponding sub-network pairs are obtained from the polygon group pairs. To perform the geometric correction between them, the Iterative Closest Points algorithm is applied to the nodes of each corresponding sub-networks pair. Finally, Hausdorff distance analysis is applied to find link pairs of networks. To assess the feasibility of the algorithm, we apply it to the networks from the KTDB center and commercial CNS company. In the experiments, several Hausdorff distance thresholds from 3m to 18m with 3m intervals are tested and, finally, we can get the F-measure of 0.99 when using the threshold of 15m.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.11
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• pp.1353-1362
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• 1999

그래프 드로잉이란 추상적인 그래프를 시각적으로 구성하여 2차원 평면상에 그려주는 작업으로 대칭성은 그래프 드로잉시 고려해야 하는 미적 기준들 중에서 그래프의 구조 및 특성을 표현해주는 가장 중요한 기준이다. 그러나 일반 그래프에서 대칭성을 찾아 그려 주는 문제는 NP-hard로 증명이 되어 있기 때문에 현재까지는 트리, 외부평면 그래프, 직병렬 유향 그래프나 평면 그래프 등으로 대상을 한정시켜 연구가 진행되어 왔다. 본 논문에서는 병렬 컴퓨터나 컴퓨터 네트워크 구조를 가시화 시키기 위하여 많이 사용되는 그래프인 상호연결망(interconnection network)의 대칭성을 분석하고 분석된 대칭성을 최대로 보여주는 대칭 드로잉 알고리즘을 제안하였다. 그리고 이를 기반으로 하여 상호연결망의 기존 드로잉 방법들과 본 논문에서 제안한 대칭 드로잉 등 다양한 드로잉을 지원하는 WWW 기반의 상호연결망 드로잉 시스템을 구현하였다.Abstract Graph drawing is constructing a visually-informative drawing of an abstract graph. Symmetry is one of the most important aesthetic criteria that clearly reveals the structures and the properties of graphs. However, the problem of finding geometric symmetry in general graphs is NP-hard. So the previous work has focused on the subclasses of general graphs such as trees, outerplanar graphs, series-parallel digraphs and planar graphs.In this paper, we analyze the geometric symmetry on the various interconnection networks which have many applications in the design of computer networks, parallel computer architectures and other fields of computer science. Based on these analysis, we develope algorithms for constructing the drawings of interconnection networks which show the maximal symmetries.We also design and implement Interconnection Network Drawing System (INDS) on WWW which supports the various drawings including the conventional drawings and our suggested symmetric drawings.