• Journal of KIISE:Databases
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• v.31 no.1
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• pp.30-38
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• 2004

In many scientific and commercial applications such as Earth Observation System (EOSDIS) and mobile Phone services tracking a large number of clients, it is a daunting task to archive and index ever increasing volume of complex data that are continuously added to databases. To efficiently manage multidimensional data in scientific and data warehousing environments, R-tree based index structures have been widely used. In this paper, we propose a scalable technique called seeded clustering that allows us to maintain R-tree indexes by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion as well as the results from our extensive experimental study. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods in terms of insertion cost and the quality of target R-trees measured by their query performance.

• Journal of KIISE
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• v.44 no.7
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• pp.703-709
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• 2017

Outsourcing databases is to offload storage and computationally intensive tasks to the third party server. Therefore, data owners can manage big data, and handle queries from clients, without building a costly infrastructure. However, because of the insecurity of network systems, the third-party server may be untrusted, thus the query results from the server may be tampered with. This problem has motivated significant research efforts on authenticating various queries such as range query, kNN query, function query, etc. Although aggregation queries play a key role in analyzing big data, authenticating aggregation queries has not been extensively studied, and the previous works are not efficient for data with high dimension or a large number of distinct values. In this paper, we propose the AMR-tree that is a data structure, applied to authenticate aggregation queries. We also propose an efficient proof construction method and a verification method with the AMR-tree. Furthermore, we validate the performance of the proposed algorithm by conducting various experiments through changing parameters such as the number of distinct values, the number of records, and the dimension of data.

• Journal of the Korean Society for information Management
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• v.29 no.1
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• pp.331-349
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• 2012

Structured abstracts have been regarded as an essential information factor to represent topics of journal articles. This study aims to provide an unconventional view to utilize structured abstracts with the analysis on sub fields of a structured abstract in depth. In this study, a structured abstract was segmented into four fields, namely, purpose, design, findings, and values/implications. Each field was compared in the performance analysis of document clustering. In result, the purpose statement of an abstract affected on the performance of journal article clustering more than any other fields. Furthermore, certain types of keywords were identified to be excluded in the document clustering to improve clustering performance, especially by Within group average clustering method. These keywords had stronger relationship to a specific abstract field such as research design than the topic of an article.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.87-87
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• 2018

지난 30년간 한반도의 연평균 기온은 $1.2^{\circ}C$ 상승하여, 전세계 100년간 평균 기온 상승 $0.74^{\circ}C$에 비해 그 상승폭이 크게 증가하였다. IPCC 5차 평가보고서(2013년)의 RCP 시나리오에 따르면, 4차 평가보고서(2007년)의 SRES 시나리오에서 보다 우리나라의 기후변화 영향이 크게 증가될 것으로 예상하고 있으며, 2050년 연평균기온의 변화가 기존 $2.0^{\circ}C$에서 $3.2^{\circ}C$로, 강수량은 기존 11.5%에서 15.6%대로 증가할 것으로 전망하고 있다. 우리나라의 최근 10년간 발생한 자연재난을 살펴보면 호우, 태풍에 의한 피해가 가장 크게 나타났고, 대설, 강풍에 의한 피해가 뒤를 이었으며, 기후변화의 영향으로 재난 재해의 형태는 점차 대형화 다양화 되어가고 있다. 이러한 기후변화에 대비하여 효율적인 재난 대응 및 대책수립을 위해 재난 위험도 평가의 필요성이 증대되고 있으며 국내의 다양한 부처에서 연구를 수행하고 있다. 그러나 재난 위험도 평가체계 및 방법론이 각 연구별로 다원화되어 있고, 실무적용 또한 미흡한 실정이다. 따라서 본 연구에서는 UNISDR 등 국제기구에서 제시하는 위험도 평가방법론을 기반으로 우리나라에 적용 가능한 위험도 평가 방법론을 정립하고, 홍수, 태풍, 대설, 가뭄 등에 대한 재난 위험도 평가 기법을 개발하였다. 또한 실제 피해 통계와 평가 결과에 대한 비교 및 적정성 분석을 통해 우리나라 실정에 맞는 최적의 위험도 평가 체계 및 방법론을 구축하고자 하였다. 본 연구를 통해 위해성, 노출성, 취약성 및 저감능력 지표로 구성된 재난 위험도 평가 기법을 개발하였고, 재난 유형별 지역별 10단계, 5등급의 위험도 평가 결과를 도출하였다. 본 연구에서는 홍수, 태풍, 대설, 가뭄 등 6개 자연재난 유형에 대한 위험도 평가 지표를 개발하였으며, 향후, 국가 및 지역 재난안전관리계획에 자연재난 위험도 평가 결과 활용을 위해 후속 연구를 추진 중에 있다.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.119-132
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• 2021

Identification of associated knowledge based on content relevance is a fundamental functionality in managing service and security of core knowledge. This study compares the performance of methods to identify associated knowledge based on content relevance, i.e., the associated document network composition performance of keyword-based and word-embedding approach, to examine which method exhibits superior performance in terms of quantitative and qualitative perspectives. As a result, the keyword-based approach showed superior performance in core document identification and semantic information representation, while the word embedding approach showed superior performance in F1-Score and Accuracy, association intensity representation, and large-volume document processing. This study can be utilized for more realistic associated knowledge service management, reflecting the needs of companies and users.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.41-47
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• 2012

There are PFD(Process Flow Diagram) and P&ID(Piping and Instrument Diagram) for designing and managing chemical process efficiently. They provide the operation condition and equipment specifications of chemical process, but they do not provide the reliability of chemical process. Therefore, in this study, Reliability Flow Diagram(RFD) which provide the cycle and time of preventive maintenance has been developed using Directed Graph Analysis methodology. Directed Graph Analysis methodology is capable of assessing the reliability of chemical process. It models chemical process into Directed Graph with nodes and arcs and assesses the reliability of normal operation of chemical process by assessing Directed Graph sequential. In this paper, the chemical process reliability transition according to operation time was assessed. And then, Reliability Flow Diagram has been developed by inserting the result into P&ID. Like PFD and P&ID, Reliability Flow Diagram provide valuable and useful information for the design and management of chemical process.

• Journal of KIISE
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• v.44 no.9
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• pp.976-985
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• 2017

Wireless mesh network (WMN) is a promising technology for building a cost-effective and easily-deployed wireless networking infrastructure. To efficiently utilize limited radio resources in WMNs, packet transmissions (particularly, redundant packet transmissions) should be carefully managed. We therefore propose a lightweight traffic redundancy elimination (LTRE) scheme to reduce redundant packet transmissions in software-defined wireless mesh networks (SD-WMNs). In LTRE, the controller determines the optimal path of each packet to maximize the amount of traffic reduction. In addition, LTRE employs three novel techniques: 1) machine learning (ML)-based information request, 2) ID-based source routing, and 3) popularity-aware cache update. Simulation results show that LTRE can significantly reduce the traffic overhead by 18.34% to 48.89%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1089-1095
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• 2014

In this study, we compared and analyzed the displacement of final excavation with measured value through an inverse analysis method used in urban excavation construction. We maximized the effectiveness of the inverse analysis method, and plan to achieve cost-effective and practical construction management in the field with identical conditions. As the first stage, we suggest an example of a field which has the inverse analysis method. We applied the inverse analysis method to three different fields on which construction and measuring were finished. Of these three fields, two fields showed a very satisfactory result. However, in one field, there were significant differences between the analysis and measured value. The result of our analysis indicated that, we should unite the conditions of the inverse analysis method and field construction. We need to thoughtfully reconsider the RANKINE earth pressure application in a triangle type. This is because the uniformity of earth pressure is made by its arching effect, in the condition of the displacement of lower underground occurring widely, which is differentiated with the earth pressure conditions of RANKINE, even if the slurry wall has stiffness. Also, when recalculating the soil parameter, we should emphasize the adhesion of the weathering zone, and give experimental consideration to ground water level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6A
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• pp.445-457
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• 2009

Recently, there are growing interests in femto-cell for providing indoor users with various broadband multimedia services more efficiently. The technical issues regarding femto-cell such as interference management, self-organization, and resource allocation are now being intensively studied and investigated by researchers worldwide. In this paper, two novel schemes of neighboring cell list(NCL) management are proposed for the IEEE 802.16e system where a macro-cell and huge number of femto-cells coexist. The proposed schemes, named MS location-based neighboring cell list management and BS type-based neighboring cell list management, enable a mobile station(MS) to perform fast scanning and efficient handover by means of preselecting the candidate target femto-cells with high possibility for handover. The simulation result shows that the proposed schemes improve the MS's handover-related performance in terms of scanning power and scanning time compared with the conventional managements scheme of IEEE 802.16e system.