• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.172-178
• /
• 2005

순환탐색 알고리즘 및 스택기반 알고리즘 등은 유방향그래프에서 순환과 순환경로를 발견하는 특정 정점으로부터 출발하여 연결된 그래프에서 순환을 탐색하는 것이다. 기존 연구의 단점은 모든 순환을 다 찾아내지지 못하는 경우라든지, 동일한 순환을 중복해서 찾아내는 문제가 있었다. 본 연구에서 제시하는 정점제거 순환탐색 알고리즘은 특정 정점의 순환을 발견한 뒤 그 정점을 삭제하므로 중복된 순환을 발견하지 않고 모든 순환을 찾을 수 있다. 또한 순환을 발견했을 때, 순환경로를 출력하는데 있어서 스택의 인덱스를 이용해, 저장경로를 탐색하지 않고 출력하는 방법을 제안하였다. 실험에서는 임의의 정점과 간선을 생성하여 그래프로 만들고, 각 알고리즘에 따른 모든 정점을 찾을 수 있는지, 그래프 상황에 따라 어떠한 장단점이 있는지, 간선이 많아질수록 인덱스 순환탐색 알고리즘보다 탐색시간이 얼마나 차이를 보이는지를 확인하였다. 웹 구조처럼 일정한 크기의 웹페이지와 많은 수의 링크가 존재하는 그래프에서 정점제거 순환탐색 알고리즘이 순환을 찾는데 적합하다는 것을 입증했다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.152-154
• /
• 2013

페타 바이트 이상의 규모의 빅 데이터 분석은 다양한 분야에서 연구되고 있다. 최근 소셜 네트워크, XML 등과 같은 구조적인 정보를 갖는 대용량의 그래프들을 분석하는 기술이 활발히 연구되고 있다. 이러한 대용량의 그래프를 분석하기 위한 연산중의 하나로 순환 그래프가 사용되고 있다. 대용량의 그래프 환경에서 순환을 검출하는 연산은 단일 컴퓨팅 시스템에서 처리가 불가능하거나 많은 시간 비용이 발생하여 분산처리가 필요하다. 본 논문에서는 그래프 처리에 효율적인 프리겔 프레임워크를 이용하여 효율적으로 순환을 검출하고, 중복 순환을 제거하기 위해 정규 순환 코드를 제안한다. 실험을 통하여 제안하는 기법이 대용량 그래프에서 효율적으로 순환을 찾을 수 있음을 보인다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.641-651
• /
• 2006

거대한 웹 컨텐츠 안에는 수많은 링크들로 인한 순환들이 존재하게 된다. 그 순환들은 강하게 뭉쳐있는 실타래 처럼, 강하게 결합한 순환들의 덩어리 형태로 존재하게 된다. 웹 컨텐츠는 흔히 방향그래프로 표현되는데, 즉 웹 컨텐츠에서 나타나는 수많은 링크둘을 방향그래프에서 강결합요소를 이용하면 모든 순환을 효율적으로 발견할 수 있다. 본 논문에서는 강결합요소를 이용하여 거대한 그래프에서 보다 효율적으로 모든 순환을 찾아낼 수 있는 방법을 제시하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.12
• /
• pp.33-41
• /
• 2011

From Complicated graph structures, duplicated operations can be executed and the operations give low efficiency. In this paper, we propose an efficient graph mining algorithm of minimizing the extension of duplicated graph patterns in which the priorities of cyclic edges are considered. In our approach, the cyclic edges with lower priorities are first extended and so duplicated extensions can be reduced. For performance test, we implement our algorithm and compare our algorithm with a state of the art, Gaston algorithm. Finally, We show that ours outperforms Gaston algorithm.

• Journal of Korea Society of Industrial Information Systems
• /
• v.9 no.2
• /
• pp.9-16
• /
• 2004

Recently, lattice conditional independence models(LCIMs) have been introduced for the analysis of non-monotone missing data patterns and of non-nested dependent regression models. This approach has been successfully applied to solve various problems in data pattern analysis, however, it suffers from computational burden to search LCIMs. In order to cope with this drawback, we propose a new scheme for finding LCIMs based on the essential graph. Also, we show that the class of LCIMs coincides with the class of all transitive acyclic directed graph(TADG) models which are Markov equivalent to a specific acyclic directed graph(ADG) models.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.649-654
• /
• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.139-155
• /
• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

• Korean Chemical Engineering Research
• /
• v.53 no.5
• /
• pp.614-619
• /
• 2015

A plasma surface modification of powders has been carried out in a circulating fluidized bed reactor under reduced pressure. Polystyrene (PS) particles treated by plasma are grafted with polyethylene glycol (PEG) on the surface. The virgin, plasma-treated and grafted powders were characterized by DPPH method, FTIR, SEM and contact angle meter. The plasma-treated PS powders have well formed peroxide on the surface, By PEG grafting polymerization, PEG is well grafted and dispersed on the surface of the plasma-treated PS powders. The PEG-g-PS particle was successfully synthesized using the plasma circulating fluidized bed reactor under reduced pressure.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.12
• /
• pp.3088-3098
• /
• 1998

The arrangement graph, which is a viable interconnection scheme for parallel and distributed systems, has been proposed as an attactive altemative to the n-cube. However, A fault tolerant design model which is well suitable for the arrangement graph doesn't has been proposd until recently, but fault tolerant design modelsfor many schemes have been proposed ina large number of paper. So, our paper presents a new fault tolerant design technique suited for the arrangement graph. To maintains the previous structures when it ocurs a fault in the current processing, the scheme properly sugbstitutes a fault-componnent into the existing structures by adding a spare component. the first of all, it converts arrangement graph into a circulant graph using the hamiltonian property and then uses automorphism of circulant graph to tolerate faults. Also, We optimize the cost of rate fault tolerant architectures by adding exactly k spare processor while tolerating up to k processor and minimizing the maximum number of limks per processor. Specially, we proposes a new techniue to minimize the maximum number of links.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.111-117
• /
• 2023

There is a lot of research on using a combination of graph neural networks and recurrent neural networks as a way to account for both temporal and spatial dependencies. In particular, graph neural networks are an emerging area of research. Seoul's bicycle rental service (aka Daereungi) has rental stations all over the city of Seoul, and the rental information at each station is a time series that is faithfully recorded. The rental information of each rental station has temporal characteristics that show periodicity over time, and regional characteristics are also thought to have important effects on the rental status. Regional correlations can be well understood using graph neural networks. In this study, we reconstructed the time series data of Seoul's bicycle rental service into a graph and developed a rental prediction model that combines a graph neural network and a recurrent neural network. We considered temporal characteristics such as periodicity over time, regional characteristics, and the degree importance of each rental station.