• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.231-250
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• 2014

Due to the recent development of Information and Communication Technologies (ICT), the amount of research publications has increased exponentially. In response to this rapid growth, the demand of automated text processing methods has risen to deal with massive amount of text data. Biomedical text mining discovering hidden biological meanings and treatments from biomedical literatures becomes a pivotal methodology and it helps medical disciplines reduce the time and cost. Many researchers have conducted literature-based discovery studies to generate new hypotheses. However, existing approaches either require intensive manual process of during the procedures or a semi-automatic procedure to find and select biomedical entities. In addition, they had limitations of showing one dimension that is, the cause-and-effect relationship between two concepts. Thus;this study proposed a novel approach to discover various relationships among source and target concepts and their intermediate concepts by expanding intermediate concepts to multi-levels. This study provided distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.505-516
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• 2018

In order to establish an efficient bridge maintenance strategy, the future performance of a bridge must be estimated by considering the current performance, which allows more rational way of decision-making in the prediction model with higher accuracy. However, personnel-based existing maintenance may result in enormous maintenance costs since it is difficult for a bridge administrator to estimate the bridge performance exactly at a targeting management level, thereby disrupting a rational decision making for bridge maintenance. Therefore, in this work, we developed a representative performance prediction model for each bridge element considering uncertainty using domestic bridge inspection data, and proposed a bayesian updating method that can apply the developed model to actual maintenance bridge with higher accuracy. Also, the feasibility analysis based on calculation of maintenance cost for monitoring maintenance scenario case is performed to propose advantages of the Bayesian-updating-driven preventive maintenance in terms of the cost efficiency in contrast to the conventional periodic maintenance.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.427-431
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• 2004

We use simulations of large-scale structure formation to study the build-up of magnetic fields (MFs) in the intergalactic medium. Our basic assumption is that cosmological MFs grow in a magnetohy-drodynamical (MHD) amplification process driven by structure formation out of a magnetic seed field present at high redshift. This approach is motivated by previous simulations of the MFs in galaxy clusters which, under the same hypothesis that we adopt here, succeeded in reproducing Faraday rotation measurements (RMs) in clusters of galaxies. Our ACDM initial conditions for the dark matter density fluctuations have been statistically constrained by the observed large-scale density field within a sphere of 110 Mpc around the Milky Way, based on the IRAS 1.2-Jy all-sky redshift survey. As a result, the positions and masses of prominent galaxy clusters in our simulation coincide closely with their real counterparts in the Local Universe. We find excellent agreement between RMs of our simulated galaxy clusters and observational data. The improved numerical resolution of our simulations compared to previous work also allows us to study the MF in large-scale filaments, sheets and voids. By tracing the propagation of ultra high energy (UHE) protons in the simulated MF we construct full-sky maps of expected deflection angles of protons with arrival energies $E = 10^{20}\;eV$ and $4 {\times} 10^{19}\;eV$, respectively. Accounting only for the structures within 110 Mpc, we find that strong deflections are only produced if UHE protons cross galaxy clusters. The total area on the sky covered by these structures is however very small. Over still larger distances, multiple crossings of sheets and filaments may give rise to noticeable deflections over a significant fraction of the sky; the exact amount and angular distribution depends on the model adopted for the magnetic seed field. Based on our results we argue that over a large fraction of the sky the deflections are likely to remain smaller than the present experimental angular sensitivity. Therefore, we conclude that forthcoming air shower experiments should be able to locate sources of UHE protons and shed more light on the nature of cosmological MFs.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.109-114
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• 2015

This paper presents a computational model on the transfer of airborne fine particles to analyze the similarities and influences among the 25 districts in Seoul by quantifying a time series data collected from each district. The properties of each district are driven with the model of a time series of the fine particle concentrations, and the calculation of edge-based weights are carried out with the transfer entropies between all pairs of the districts. We applied a modularity-based graph clustering technique to detect the communities among the 25 districts. The result indicates the discovered clusters correspond to a high transfer-entropy group among the communities with geographical adjacency or high in-between traffic volumes. We believe that this approach can be further extended to the discovery of significant flows of other indicators causing environmental pollution.

• Information Systems Review
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• v.4 no.2
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• pp.1-17
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• 2002

Proliferation of IT applications and developments lead to many useful yet diverse information systems being used in an organization. Users of information system have been satisfied with useful functions and prompt outputs, however they have become realized the problem of incompatibility of diverse systems and asked for more compatible systems in which more data and software can be shared among users. Information technology architecture (ITA) is suggested as one way of avoiding such problem by many researchers. This paper ananlyzes and introduces the ITA approach used primarily in US governments and in many other organizations. In Korea, many researchers view ITA as a technical concept that deal with interperability and standards. However, ITA is more managerial oriented concept than technical, because it allows us to achieve managerial objective, for example alignment of IT to strategic objectives, with ease and more confidence. This paper proposes the necessity of ITA in IT management and clarifies the relationships of managerial focus with technical focus of ITA.

• Korean Journal of Construction Engineering and Management
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• v.15 no.2
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• pp.53-61
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• 2014

Previous studies reveal the need for a tool to cost estimation of building design in early design stages. This paper proposes an internal finishes cost model tool to address this need. The tool allows users to evaluate the functionality, economics and quality of finishes concurrently with high-rise building design. Lack of information in the early stages of the project enables a relatively accurate estimates of work to raise up. Measurements are automatically extracted from simple design information and profile driven estimates are revised in real-time. The data model uses a flexible unit rate system that can easily be extended to other estimate dimensions such as mix-use building surcharge rate estimation. The approach illustrated in this paper is applicable to BIM tool conceptual estimation that support for massing purposes other than the one chosen for this study.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.29-43
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• 2008

Although RDF ontologies might be expressed in XML tree model, existing methods for protection of XML documents are not suitable for securing RDF ontologies. The graph style and inference feature of RDF demands a new security model development. Driven by this goal, this paper proposes a new query-oriented model for the RDF ontology access control. The proposed model rewrites a user query using a three-layered ontology view. The proposal resolves the problem that the existing approaches should generate inference models depending on inference rules. Accessible ontology concepts and instances which a user can visit are defined as ontology views, and the inference view defined for controling an inference query enables a controlled inference capability for the user. This paper defines the three-layered view and describes algorithms for query rewriting according to the views. An implemented prototype with its system architecture is shown. Finally, the experiment and comparative evaluation result of the proposal and the previous approach is described.

• Advances in nano research
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• v.12 no.5
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.170-178
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• 2022

A high-performing Earned Value Management System (EVMS) can influence project success and help stakeholders meet project objectives. Although EVMS processes are well-supported by technical guidelines and standards, project managers often face challenges related to the project culture, team, resources, and business practices that make up the project environment within which an EVMS is being used. A comprehensive literature review revealed a lack of a data-driven and consistent assessment frameworks that can gauge the environment surrounding EVMS implementation. This paper will discuss the EVMS environment of construction and environmental projects, and examine its impact on cost performance. The authors used a multi-method approach to identify 27 environment factors that make up the EVMS environment, assessing them on 18 construction and environmental projects worth over $2 billion of total cost. Research methods employed include: (1) a literature review of more than 300 references; (2) a survey of 294 respondents; and (3) remote research charrettes with more than 60 participating expert practitioners. Culture (one of the identified environment categories) was found to be relatively more important in terms of its impact on the EVMS environment, followed by people, practices, and resources. These exploratory results show statistically significant differences in cost performance between completed projects with either a good or poor environment, for the sample projects. Key environment factors are outlined, and guidance is provided to practitioners around how to set up an effective EVMS environment in a construction or environmental project to inform decision-making and support achieving the project cost objectives successfully.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.