• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.1
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• pp.22-34
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• 2002

In this paper, we consider ring embeding problem in faulty (n,k) star graphs which is recently proposed as an alternative interconnection network topology, By effectively utilizing such strategies as series of dimension expansions and even distribution of faulty nodes into sub-stars in graph itself. we prove that it is possible to construct a maximal fault-free ring excluding only faulty nodes when the number of faults is no more than n-3 and $n-k{\geq}2$, and also propose an algorithm which can embed the corresponding ring in (n.k)-star graphs This results will be applied into the multicasting applications that the underlying cycle properties on the multi-computer system.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.272-275
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• 2019

단어 중의성 해소 방법은 지식 정보를 활용하여 문제를 해결하는 지식 기반 방법과 각종 기계학습 모델을 이용하여 문제를 해결하는 지도학습 방법이 있다. 지도학습 방법은 높은 성능을 보이지만 대량의 정제된 학습 데이터가 필요하다. 반대로 지식 기반 방법은 대량의 정제된 학습데이터는 필요없지만 높은 성능을 기대할수 없다. 최근에는 이러한 문제를 보완하기 위해 지식내에 있는 정보와 정제된 학습데이터를 기계학습 모델에 학습하여 단어 중의성 해소 방법을 해결하고 있다. 가장 많이 활용하고 있는 지식 정보는 상위어(Hypernym)와 하위어(Hyponym), 동의어(Synonym)가 가지는 의미설명(Gloss)정보이다. 이 정보의 표상을 기존의 문장의 표상과 같이 활용하여 중의성 단어가 가지는 의미를 파악한다. 하지만 정확한 문장의 표상을 얻기 위해서는 단어의 표상을 잘 만들어줘야 하는데 기존의 방법론들은 모두 문장내의 문맥정보만을 파악하여 표현하였기 때문에 정확한 의미를 반영하는데 한계가 있었다. 본 논문에서는 의미정보와 문맥정보를 담은 단어의 표상정보를 만들기 위해 구문정보, 의미관계 그래프정보를 GCN(Graph Convolutional Network)를 활용하여 임베딩을 표현하였고, 기존의 모델에 반영하여 문맥정보만을 활용한 단어 표상보다 높은 성능을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.23-26
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• 2024

본 논문에서는 그래프 구조의 데이터에서 각 노드의 신호를 예측하는 연구를 진행하였다. 이를 위해 분석하고자 하는 그래프에 대해 연결 관계를 기반으로 각 노드에 비-유클리드 공간 상에서의 좌표를 부여하여 그래프의 공간적 임베딩을 얻은 뒤, 각 노드의 공간적 임베딩을 입력으로 받고 해당 노드의 신호를 예측하는 그래프 암시적 신경 표현 모델을 제안 하였다. 제안된 모델의 검증을 위해 네트워크형 데이터와 3차원 메시 데이터 두 종류의 그래프 데이터에 대하여 신호 학습, 신호 예측 및 메시 데이터의 초해상도 과정 실험들을 진행하였다. 전반적으로 기존의 그래프 암시적 신경 표현 모델과 비교하였을 때 비슷하거나 더 우수한 성능을 보였으며, 특히 네트워크형 그래프 데이터 신호 예측 실험에서 큰 성능 향상을 보였다.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.191-196
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• 2002

It is one of the important measures in the area of algorithm design that any interconnection network should be embedded into another interconnection network for the practical use of algorithm. A HCN(n, n), HFN(n, n) graph also has such a good properties of a hypercube and has a lower network cost than a hypercube. In this paper, we propose a method to embed between hypercube $Q_2n$ and HCN(n, n), HFN(n, n) graph. We show that hypercube $Q_2n$ can be embedded into an HCN(n, n) and KFN(n, n) with dilation 3, and average dilation is smaller than 2. Also, we has a result that the embedding cost, a HCN(n, n) and KFN(n, n) can be embedded into a hypercube, is O(n)

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.801-808
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• 2022

Research on how to embed knowledge in large-scale Linked Data and apply neural network models for entity matching is relatively scarce. The most fundamental problem with this is that different labels lead to lexical heterogeneity. In this paper, we propose an extended GCN (Graph Convolutional Network) model that combines re-align structure to solve this lexical heterogeneity problem. The proposed model improved the performance by 53% and 40%, respectively, compared to the existing embedded-based MTransE and BootEA models, and improved the performance by 5.1% compared to the GCN-based RDGCN model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.291-299
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• 2016

This paper raises a question of whether a simple periodic phenomenon is associated with the topology and provides the convincing answers to it. A variety of music instrumental sound signals are used to prove our assertion, which are embedded in Euclidean space to analyze their topologies by computing the homology groups. A commute time embedding is employed to transform segments of waveforms into the corresponding geometries, which is implemented by organizing patches according to the graph-based metric. It is shown that commute time embedding generates the intrinsic topological complexities although their geometries are varied according to the spectrums of the signals. This paper employs a persistent homology to determine the topological invariants of the simplicial complexes constructed by randomly sampling the commute time embedding of the waveforms, and discusses their applications.

• Bulletin of the Korean Mathematical Society
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• v.47 no.1
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• pp.17-27
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• 2010

A Cayley map is a 2-cell embedding of a Cayley graph into an orientable surface with the same local orientation induced by a cyclic permutation of generators at each vertex. In this paper, we provide classifications of prime-valent regular Cayley maps on abelian groups, dihedral groups and dicyclic groups. Consequently, we show that all prime-valent regular Cayley maps on dihedral groups are balanced and all prime-valent regular Cayley maps on abelian groups are either balanced or anti-balanced. Furthermore, we prove that there is no prime-valent regular Cayley map on any dicyclic group.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.164-167
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• 2021

최근 구글, 아마존, LOD 등을 중심으로 지식 그래프(Knowledge graph)와 같은 검색 고도화 연구가 활발히 수행되고 있다.그러나 대규모 지식 그래프 인덱싱 시스템에서 데이터가 어떻게 임베딩(embedding)되고, 딥러닝(deep learning) 되는지는 상대적으로 거의 연구가 되지 않고 있다. 이에 본 논문에서는 임베딩 모델에 대한 성능평가를 통해 데이터셋에 대해 어떤 모델이 가장 좋은 지식 임베딩 방법을 도출하는지 분석한다.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.