• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권12호
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• pp.577-582
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• 2002

The objective of this paper is to produce a weighted graph map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor circumstance. The AMR navigates in th unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. Then, the occupancy grid map is converted to a weighted graph map suing morphological image processing and thinning algorithms. the path- planning for autonomous navigation of a mobile robot can be carried out based on the occupancy grid map. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• 대한원격탐사학회지
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• 제27권6호
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• pp.717-728
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• 2011

An efficient method for the unsupervised classification of high resolution imagery is suggested in this paper. It employs pixel-linking and merging based on the adjacency graph. The proposed algorithm uses the neighbor lines of 8 directions to include information in spatial proximity. Two approaches are suggested to employ neighbor lines in the linking. One is to compute the dissimilarity measure for the pixel-linking using information from the best lines with the smallest non. The other is to select the best directions for the dissimilarity measure by comparing the non-homogeneity of each line in the same direction of two adjacent pixels. The resultant partition of pixel-linking is segmented and classified by the merging based on the regional and spectral adjacency graphs. This study performed extensive experiments using simulation data and a real high resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for object-based analysis and proper land-cover map for high resolution imagery of urban area.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.470-474
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• 2008

A well-designed computer network technology produces benefits on several fields within the organization, between the organizations(suborganizations) or among different organizations(suborganizations). Network technology streamlines business processes, decision process. Graphs are useful data structures capable of efficiently representing a variety of networks in the various fields. Metagraph is a like graph theoretic construct introduced recently by Basu and Blanning in which there is set to set mapping in place of node to node as in a conventional graph structure. Metagraph is thus a new type of data structure occupying its popularity among the computer scientists very fast. Every graph is special case of Metagraph. In this paper the authors introduce the notion of Meta-Networking as a new network technological representation, which is having all the capabilities of crisp network as well as few additional capabilities. It is expected that the notion of meta-networking will have huge applications in due course. This paper will play the role of introducing this new concept to the network technologists and scientists.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5401-5421
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• 2016

Both low-density parity-check (LDPC) codes and the multiple access technique of spatially coupling data transmission (SCDT) can be expressed in bipartite graphs. To improve the performance of iterative demodulation and decoding for SCDT, a novel joint sparse graph (JSG) with SCDT and LDPC codes is constructed. Based on the JSG, an approach for iterative joint demodulation and decoding by belief propagation (BP) is presented as an exploration of the flooding schedule, and based on BP, density evolution equations are derived to analyze the performance of the iterative receiver. To accelerate the convergence speed and reduce the complexity of joint demodulation and decoding, a novel serial schedule is proposed. Numerical results show that the joint demodulation and decoding for SCDT based on JSG can significantly improve the system's performance, while roughly half of the iterations can be saved by using the proposed serial schedule.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.238-246
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• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• 한국콘텐츠학회논문지
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• 제19권1호
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• pp.141-151
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• 2019

개체 간의 상호 작용을 나타내기 위해 그래프 데이터 형태의 네트워크가 많은 애플리케이션에서 사용되고 있다. 최근에는 빅데이터 기술의 발달로 처리해야할 네트워크의 크기가 점점 커짐에 따라 하나의 서버에서 이를 처리하기 어려워졌기 때문에 분산 처리의 필요성 또한 증가하고 있다. 본 논문에서는 이러한 그래프 데이터가 분산 저장되어있는 환경에서 서브 그래프 탐색을 효율적으로 수행하기 위한 분산 처리시스템을 제안한다. 불필요한 탐색을 줄이기 위해 데이터의 통계정보를 활용해 확률적인 스코어링을 통해 탐색 순서를 정한다. 그래프 네트워크의 정점과 차수의 관계는 데이터의 종류에 따라 다른 특성을 보일 수 있기 때문에 여러 분포적 특성을 갖는 그래프에 대해 다른 스코어링 방법을 통해 불필요한 탐색을 줄이기 위한 스코어를 계산하여 탐색 순서를 결정한다. 결정된 순서에 따라 그래프가 분산 저장된 서버에서 순차적으로 탐색한다. 성능평가에서는 제안하는 기법의 우수성을 입증하기 위해 기존 기법과의 비교를 수행하였으며, 그 결과 기존 기법보다 탐색 시간이 약 3~10% 향상됨을 보였다.

• 정보처리학회논문지D
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• 제8D권3호
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• pp.257-264
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• 2001

본 논문은 데이터 슬라이스에서의 데이터 토큰들의 정보 흐름에 기초하여 프로그램에서의 정보 흐름을 모델링하는 SIFG(Slicw-based information Graph)를 개발하였다. 다음으로, SIFG에서의 정보 흐름의 복잡도 측정을 통해서 프로그램의 복잡도를 측정하기 위해 SCM(Slice-based Complexity Measure)을 정의하였다. SCM은 Briand가 제시하는 복잡도 메트릭에 필요한 특성들을 만족하였고, 그리고 기존 척도들과는 달리, SCM은 프로그램 내에서의 제어와 데이터 흐름뿐만 아니라 프로그램의 물리적 크기를 반영하는 측정이 이루어졌다.