• 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.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Journal of Life Science
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• v.29 no.7
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• pp.800-808
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• 2019

Charcot-Marie-Tooth disease (CMT) is a group of rare peripheral neuropathies characterized by progressive muscle weakness and atrophy and areflexia in the upper and lower extremities. The most common subtype of CMT is CMT1A, which is caused by a tandem duplication of the PMP22 gene in the 17p12 region. Patients with CMT1A show a loose genotype-phenotype correlation, which suggests the existence of secondary genetic or association factors. Recently, polymorphisms of rs71428439 (n.83A>G) and rs2292832 (n.86T>C) in the MIR149 have been reported to be associated with late onset and mild phenotypic CMT1A severity. The aim of this study was to examine the intrafamilial heterogeneities of clinical phenotypes according to the genotypes of these two SNPs in MIR149. For this study, we selected 6 large CMT1A families who showed a wide range of phenotypic variation. This study suggested that both SNPs were related to the onset age and severity in the dominant model. In particular, the AG+GG (n.83A>G) and TC+CC genotypes (n.86T>C) were associated to late onset and mild symptoms. Motor nerve conduction velocity (MNCV) was not related to the MIR149 genotypes. These results were consistent with the previous studies. Therefore, we suggest that the rs71428439 and rs2292832 variants in MIR149 may serve as genetic modifiers of CMT1A intrafamilial phenotypic heterogeneity, as they have a role in the unrelated patients. This is the first study to show an association using large families with variable clinical CMT1A phenotypes. The results will be helpful in the molecular diagnosis and treatment of patients with CMT1A.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.