• Annual Conference of KIPS
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• 2022.11a
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• pp.574-576
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• 2022

최근 여러 분야에서 그래프 신경망(graph neural network, GNN)이 활발히 연구되고 있다. 하지만 지금까지 대부분의 GNN 연구는 단일 GNN 모델의 성능을 향상하는 데 집중되었다. 본 논문에서는 앙상블(ensemble) 기법의 대표적 기법인 그래디언트 부스팅(gradient boosting)을 이용하여 GNN의 앙상블 모델을 만드는 방법을 제안한다. 제안 방법은 앞서 만들어진 GNN의 오차를 경사 하강법(gradient descent)을 이용하여 감소시키는 방향으로 다음 GNN을 생성한다. 이 과정을 반복하여 GNN의 최종 앙상블 모델을 얻는다. 실험에서 GNN의 대표적인 모델인 그래프 합성곱 신경망(graph convolutional network, GCN)에 제안 방법을 적용하여 앙상블 모델을 생성한 결과, 단일 GCN 모델에 비해 노드 분류 정확도가 11.3%p까지 증가하였음을 확인하였다.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.113-116
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• 2021

As artificial intelligence technologies, including deep learning, develop, these technologies are being introduced to code similarity analysis. In the traditional analysis method of calculating the graph edit distance (GED) after converting the source code into a control flow graph (CFG), there are studies that calculate the GED through a trained graph neural network (GNN) with the converted CFG, Methods for analyzing code similarity through CNN by imaging CFG are also being studied. In this paper, to determine which approach will be effective and efficient in researching code similarity analysis methods using artificial intelligence in the future, code similarity is measured through funcGNN, which measures code similarity using GNN, and Siamese Network, which is an image similarity analysis model. The accuracy was compared and analyzed. As a result of the analysis, the error rate (0.0458) of the Siamese network was bigger than that of the funcGNN (0.0362).

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.192-198
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• 2020

The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

• Journal of IKEEE
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• v.25 no.3
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• pp.451-461
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• 2021

As various smart devices spread and the damage caused by malicious codes becomes more serious, malicious code detection technology using machine learning technology is attracting attention. However, if the training data of machine learning is constructed based on only the fragmentary characteristics of the code, it is still easy to create variants and new malicious codes that avoid it. To solve such a problem, a research using the function call relationship of malicious code as training data is attracting attention. In particular, it is expected that more advanced malware detection will be possible by measuring the similarity of graphs using GNN. This paper proposes an efficient method to generate a function call graph from binary code to utilize GNN for malware detection.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.89-98
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• 2024

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.

• Annual Conference of KIPS
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• 2024.05a
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• pp.678-679
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• 2024

그래프 신경망(Graph Neural Network, GNN)은 실세계 그래프 데이터에 대한 다양한 다운스트림 작업들에서 우수한 성능을 보여 왔다. 그러나, 최근 연구는 GNN 의 예측 결과가 데이터 내 특정 집단에 대한 차별을 내포할 수 있음을 지적했다. 이러한 문제를 해결하기 위해, 공정성을 고려할 수 있는 GNN 방법들이 설계되어 오고 있으나, 아직 실세계 그래프 데이터가 공정성 관점에서 어떠한 특성을 가지고 있는지에 대한 분석은 충분히 이루어지지 않았다. 따라서, 본 논문에서는 다양한 공정성 평가 지표를 활용하여 실세계 그래프 데이터의 공정성을 비교 분석한다. 실험 결과, 실세계 그래프 데이터들은 도메인 혹은 평가 지표에 따라 다른 특성을 가진다는 것을 확인하였다.

• Nuclear Engineering and Technology
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• v.51 no.8
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• pp.1897-1904
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• 2019

Three-layer back propagation network (BPN) and genetic neural network (GNN) were developed in this study to predict the flow boiling heat transfer coefficient (HTC) in conventional and small-diameter channels. The GNN has higher precision than BPN (with root mean square errors of 17.16% and 20.50%, respectively) and other correlations. The inputs include vapor quality x, mass flux G, heat flux q, diameter D and physical parameter φ, and the predicted flow boiling HTC is set as the outputs. Influences of input parameters on the flow boiling HTC are discussed based on the trained GNN: nucleate boiling promoted by a larger saturated pressure, a larger heat flux and a smaller diameter is dominant in small channels; convective boiling improved by a larger mass flux and a larger vapor quality is more significant in conventional channels. The HTC increases with pressure both in conventional and small channels. The HTC in conventional channels rises when mass flux increases but remains almost unaffected in small channels. A larger heat flux leads to the HTC growth in small channels and an increase of HTC was observed in conventional channels at a higher vapor quality. HTC increases inversely with diameter before dry out.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.