• Proceedings of the Korean Radioactive Waste Society Conference
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• 2012.10a
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• pp.203-204
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• 2012

• Korean Journal of Pharmacognosy
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• v.54 no.2
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• pp.53-60
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• 2023

Athyrium acutipinnulum, called as Ulleungdo ladyfern (Seom-go-sa-ri), is a native plant of South Korea. A. acutipinnulum has been consumed as foods and also traditionally used for the treatment of epilepsy, gonorrhea and nerve disorder. The methanolic extract and EtOAc soluble fraction of A. acutipinnulum showed the antioxidant activity. Fractionation using various chromatographic techniques resulted in the isolation of 13 compounds. The structures were elucidated on the basis of spectroscopic methods as seven phenolic compounds, methyl 2-hydroxy-3-phenylpropanoate (1), protocatechualdehyde (2), caffeic acid (3), trans-p-coumaric acid (4), (-)-4-E-caffeoyl-L-threonic acid (5), 5-O-caffeoyl shikimic acid (6) and 5-O-caffeoyl quinic acid (7), three flavonoids, quercetin 3-O-β-glucoside (8), naringenin-7-O-β-glucoside (9) and sutchenoside A (10), two steroids, ponasterone A (11) and ecdysone (12) and a coumarin, esculetin (13). Among them, compounds 5 and 10 were first reported from Athyrium spp and compounds 2, 5, 6 and 7 showed the antioxidant activity.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.84-94
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• 2021

Graph databases have been developed to efficiently store and query graph data composed of vertices and edges to express relationships between objects. Since the query types of graph database show very different characteristics from traditional NoSQL databases, benchmarking tools suitable for graph databases to verify the performance of the graph database are needed. In this paper, we propose an efficient graph database benchmarking system that supports diversity in graph inputs and queries. The proposed system utilizes OrientDB to conduct benchmarking for graph databases. In order to support the diversity of input graphs and query graphs, we use LDBC that is an existing graph data generation tool. We demonstrate the feasibility and effectiveness of the proposed scheme through analysis of benchmarking results. As a result of performance evaluation, it has been shown that the proposed system can generate customizable synthetic graph data, and benchmarking can be performed based on the generated graph data.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2012.10a
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• pp.205-206
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• 2012

• Proceedings of the PSK Conference
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• 2001.04a
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• pp.173.2-173.2
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• 2001

• Proceedings of the PSK Conference
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• 2001.10a
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• pp.302.2-302.2
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• 2001

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.399-400
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• 2009

As a part of the era of human centric applications people started to care about their well being utilizing any possible mean. This paper proposes a framework for real time on-body sensor health-care system, addresses the current issues in such systems, and utilizes an enhanced online divisive agglomerative clustering algorithm (EODAC); an algorithm that builds a top-down tree-like structure of clusters that evolves with streaming data to rationally cluster on-body sensor data and give accurate diagnoses remotely, guaranteeing high performance, and scalability. Furthermore it does not depend on the number of data points.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.259-260
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• 2009

학습자와 학습객체간의 학습추론을 하기 위해서는 우선 학습자와 학습객체간의 연관성 정도를 파악해야 한다. 본 연구에서는 학습자가 전체 학습 중에 수강한 학습과 수강하지 않은 학습, 학습자가 수강한 학습을 전체 다른 학습자가 수강한 것과 수강하지 않는 정도, 학습자가 수강하지 않은 학습에 대한 전체 다른 학습자가 수강한 것과 수강하지 않는 정도를 가지고 2X2 메트릭스를 만들어 학습자와 학습객체간의 연관성 정도를 파악하여 이를 바탕으로 학습추론 시스템을 개발한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.375-376
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• 2009

트리기반 빈발 항목 집합 알고리즘들은 전체적으로 밀집 빈발 항목 집합에는 효율적이고 빠르게 빈발 항목 집합을 탐색하나 희소 빈발 항목 집합에는 효율적이지 않고 빈발 항목 집합을 빠르게 탐색하지 못한다. 반면에 배열기반 빈발 항목 집합 알고리즘은 희소 빈발 항목 집합에 효율적이고 빠르게 빈발 항목 집합을 탐색하나 밀집 빈발 항목 집합에는 효율적이지 않고 빈발 항목 집합을 빠르게 탐색하지 못한다. 밀집 및 희소 빈발 항목 집합 모두 효율적으로 빈발 항목 집합을 탐색 하고자 하는 시도가 있었으나 두 가지 종류의 알고리즘을 동시에 사용하므로 각각의 알고리즘을 사용할 정확한 기준 제시가 어렵고, 두 가지 알고리즘의 단점을 내포한다. 따라서 본 논문에서는 단일 알고리즘을 사용하여 밀집 빈발 항목 집합 및 희소 빈발 항목 집합 모두에 대해 작은 메모리 공간을 사용하면서도 효율적이고 빠르게 빈발 항목 집합을 탐색할 수 있는 CPFP-Tree라는 새로운 자료구조와 탐색 방법을 제안한다.

• Computational Structural Engineering
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• v.35 no.4
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• pp.14-22
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• 2022