• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.904-913
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• 2020

As the data volumes and complexity rapidly increase, data-intensive science handling large-scale scientific data needs to investigate new techniques for intelligent storage and data distribution over networks. Recently, Named Data Networking (NDN) and data-intensive science communities have inspired innovative changes in distribution and management for large-scale experimental data. In this article, analysis on NDN testbeds for large-scale scientific data such as climate science data and High Energy Physics (HEP) data is presented. This article is the first attempt to analyze existing NDN testbeds for large-scale scientific data. NDN testbeds for large-scale scientific data are described and discussed in terms of status, NDN-based application, and features, which are NDN testbed instance for climate science, NDN testbed instance for both climate science and HEP, and the NDN testbed in SANDIE project. Finally various issues to prevent pitfalls in NDN testbed establishment for large-scale scientific data are analyzed and discussed, which are drawn from the descriptions of NDN testbeds and features on them.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.1-9
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• 2014

This study deals with development of Integrated Transportation Analysis System for Large-scale event. Based on case studies, the requirements of the system were defined and the direction of development was established. The large-scale events that require fast and accurate transportation policy were selected. The data warehouse and data mart were developed by integrating the large-scale event data and the traffic data. Business intelligence system was designed and developed users to allow timely decisions.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.503-508
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• 2003

In this study, we peformed ahead a field geological investigation, boring investigation for slope stability analysis in large scale slope failure area. But the geological stratum was not clearly grasped, because ground was very disturbed by large scale Granite intrusion. Furthermore, the existing test data was not pertinent to the large scale Granite intrusion site like here. Therefore, various kind of field test were performed to grasp clearly for geological stratum. And the results of back analysis, various kind tests used to slope stability analysis.

• Journal of Ship and Ocean Technology
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• v.5 no.4
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• pp.44-50
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• 2001

Large container carriers are suffering from lack of knowledge on reliable correlation allowances between model tests and full-scale trials, especially at fully loaded condition, Careful full-scale sea trial with a full loading of containers both in holds and on decks was carried out to clarify it. Model test results were analyzed by different methods but with the same measuring data to figure out appropriated correlations factors for each analysis methods, Even if it is no doubt that model test technique is one of the most reliable tool to predict full scale powering performance, its assumptions and simplifications which have been applied on the course of data manipulation and analysis need a feedback from sea trial data for a fine tuning, so called correlation factor. It can be stated that the best correlation allowances at fully loaded condition for both 2-dimensional and 3-dimensional analysis methods are fecund through the careful sea trial results and relevant study on the large size container carriers.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.268-273
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• 2021

In this paper, we propose a sentiment analysis model that improves performance on small-scale data. A sentiment analysis model for small-scale data is proposed and verified through experiments. To this end, we propose Bagging-Bi-GRU, which combines Bi-GRU, which learns GRU, which is a variant of LSTM (Long Short-Term Memory) with excellent performance on sequential data, in both directions and the bagging technique, which is one of the ensembles learning methods. In order to verify the performance of the proposed model, it is applied to small-scale data and large-scale data. And by comparing and analyzing it with the existing machine learning algorithm, Bi-GRU, it shows that the performance of the proposed model is improved not only for small data but also for large data.

• Ocean and Polar Research
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• v.39 no.1
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• pp.61-71
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• 2017

This study estimates the cost function through offshore fishery cost data and analyzed the economies of scale of Korea's offshore fishery. For the estimation of the cost function, translog cost function was used, and the analysis implemented the panel analysis of the panel data. Also, annual economies of scale of the offshore fishery and economies of scale of 14 offshore fisheries in 2015 were analyzed using translog cost function coefficient estimation. The analysis result of economies of scale of Korea's offshore fishery showed that with the exception of 2003, economies of scale exist in all periods of time. However, as it almost reaches the minimum efficient scale, it was revealed that further scale expansion will bring inefficiency. Thus, according to the analysis result, Korea's offshore fishery requires a scale reduction policy rather than scale expansion policy, and this seems to coincide with the current government's fishery reconstruction policy and its practice such as the fishing vessel buyback program. The analysis result of economies of scale of each offshore fishery in 2015 showed that economies of scale of each offshore fishery exists with the exception of five trawl fisheries such as large pair-trawl and large otter trawl and large purse seines. This strongly suggests that the five fisheries and Large Purse Seines with non performing economies of scale need urgent scale reduction and should be the first target for the government's fishery reconstruction policy.

• Journal of Distribution Science
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• v.19 no.3
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• pp.49-60
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• 2021

Purpose: This study aims to utilize geographic data to analyze how various retail formats of large-scale stores around the traditional market affect the performance of the traditional market in Seoul, Korea. Research design, data, and methodology: The two types of catchment areas were demarcated (circle of 1km radius and Thiessen polygon) for each traditional market, and the large-scale stores located within each catchment area were identified for 153 traditional markets in Seoul, Korea. Additionally, multiple regression analysis was utilized. Results: The results revealed that the influence on the performance of the traditional markets were different depending on the retail format of the large-scale stores. Large discount stores were found to have a negative effect on the sales and the visitors of traditional markets, whereas complex shopping malls and department stores had a positive effect on the traditional markets. Conclusions: As a result of the differences in the retail format such as product categories and leisure functions, the impact of some large-scale stores on the traditional market may have a greater agglomeration effect than the consumer churn effect. Therefore, it is suggested that in the regulation of these large-scale stores, the differences in retail format should be considered for the future.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.534-547
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• 2014

Large-scale failures resulting from natural disasters or intentional attacks are now causing serious concerns for communication network infrastructure, as the impact of large-scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we propose a new framework for the analysis and prevention of network service disruptions in large-scale failure scenarios. We build dynamic deterministic and probabilistic models to capture the impact of regional failures as they evolve with time. A probabilistic failure model is proposed based on wave energy behaviour. Then, we develop a novel approach for preventive protection of the network in such probabilistic large-scale failure scenarios. We show that our method significantly improves uninterrupted delivery of data in the network and reduces service disruption times in large-scale regional failure scenarios.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.3
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• pp.385-396
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• 1999

In the process of structural design for large-scale structures with several thousands of degrees of freedom, a plethora of structural calculations with large amount of data storage are required to obtain the forces and displacements of the members. However, current computational environment with single microprocessor such as a personal computer or a workstation is not capable of generating a high-level of efficiency in structural analysis and design process for large-scale structures. In this paper, a high-performance parallel computing system interconnected by a network of personal computers is proposed for an efficient structural analysis. Two distributed structural analysis algorithms are developed in the form of distributed or parallel preconditioned conjugate gradient (DPCG) method. To enhance the performance of the developed distributed structural analysis algorithms, the number of communications and the size of data to be communicated are minimized. These algorithms are applied to the structural analyses of three large space structures as well as a 144-story tube-in-tube framed structure.