• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.399-401
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• 2022

POI (Point-of-Interest) 추천은 다양한 위치 기반 서비스에서 중요한 역할을 있다. 기존 연구에서는 사용자의 모바일 선호도를 모델링하기 위해 과거의 체크인의 공간-시간적 관계를 추출한다. 그러나 사용자 궤적에 숨겨진 개인 방문 경향을 반영할 수 있는 structured feature 는 잘 활용되지 않는다. 이 논문에서는 궤적 그래프를 결합한 시공간 인식 attention 네트워크를 제안한다. 개인의 선호도가 시간이 지남에 따라 변할 수 있다는 점을 고려하면 Dynamic GCN (Graph Convolution Network) 모듈은 POI 들의 공간적 상관관계를 동적으로 집계할 수 있다. LBSN (Location-Based Social Networks) 데이터 세트에서 검증된 새 모델은 기존 모델보다 약 9.0% 성능이 뛰어나다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.151-155
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• 2001

Mobile agent technology has been the subject of much attention in the last few years, mainly due to the proliferation of distributed software technologies combined with the distributed AI research field. In this paper, we present a design of communication networks of agents that cooperate with each other for forwarding messages to the specific mobile agent in order to make the overall system location transparent. In order to make the material accessible to general intelligent system researchers, we present the general ideas abstractly in terms of the graph theory. In particular, a proxy network is defined as a directed acyclic graph satisfying some structural conditions. In turns out that the definition ensures some kind of reliability of the network, in the sense that as long as at most one proxy agent is abnormal, there agent exists a communication path, from every proxy agent to the target agent, without passing through the abnormal proxy. As the basis for the implementation of this scheme, an appropriate initial proxy network is specified and the dynamic nature of the network is represented by a set of graph transformation rules. It is shown that those rules are sound, in the sense that all graphs created from the initial proxy network by zero or more applications of the rules are guaranteed to be proxy networks. Finally, we will discuss some implementation issues.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.6
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• pp.235-242
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• 2021

In this work, we develop a Korean dependency parser based on a stack-pointer network that consists of a pointer network and an internal stack. The parser has an encoder and decoder and builds a dependency tree for an input sentence in a depth-first manner. The encoder of the parser encodes an input sentence, and the decoder selects a child for the word at the top of the stack at each step. Since the parser has the internal stack where a search path is stored, the parser can utilize information of previously derived subtrees when selecting a child node. Previous studies used only a grandparent and the most recently visited sibling without considering a subtree structure. In this paper, we introduce graph attention networks that can represent a previously derived subtree. Then we modify our parser based on the stack-pointer network to utilize subtree information produced by the graph attention networks. After training the dependency parser using Sejong and Everyone's corpus, we evaluate the parser's performance. Experimental results show that the proposed parser achieves better performance than the previous approaches at sentence-level accuracies when adopting 2-depth graph attention networks.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.20-23
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• 2020

개체 연결이란 문서에서 등장한 멘션(Mention)들을 지식 기반(Knowledge Base)상의 하나의 개체에 연결하는 문제를 말한다. 개체 연결은 개체를 찾는 멘션 탐지(mention detection)과정과 인식된 멘션에 대해 중의성을 해결하여 하나의 개체를 찾는 개체 중의성 해결(Entity disambiguation)과정으로 구성된다. 본 논문에서는 개체 정보를 강화하기 위해 wikipedia2vec정보를 결합하여 Entity 정보를 강화하고 문장 내에 모든 개체 정보를 활용하기 위해 집합적 개체를 정의하고 그래프 구조를 표현하기 위해 GNN을 활용하여 기존보다 높은 성능을 이끌어내었다.

• Knowledge Management Research
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• v.17 no.1
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• pp.1-16
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• 2016

The purpose of this study is to analyze 352 scholarly journals and 1496 keywords in Knowledge Management Research from 2000 to 2015 and provide systematical view point of research trend in the area of knowledge management using graph theory and network model. The relational patterns among keywords as well as keywords which recently received noticeable attention and keywords which receded from the spotlight in recent years in the knowledge management literature were identified. The result of this study can be used as a foundation of future research ideas in knowledge management.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1643-1652
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• 2022

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.