• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.928-941
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• 2016

Energy consumption of today's wired data networks is driven by access networks. Today, green networking has become a issue to reduce energy wastes and $CO_2$ emission by adding energy managing mechanism to wired data networks. However, energy consumption and environmental impacts of wired access networks are largely unknown. In addition, there is a lack of general and quantitative valuation basis of energy use of large-scale access networks and $CO_2$ emissions from them. This paper compared and analyzed limits of existing models estimating energy consumption of access networks and it proposed a model to estimate energy consumption of large-scale access networks by top-down approach. In addition, this work presented models that assess environmental(anti-greening) impacts of access networks using results from our models. The performance evaluation of the proposed models are achieved by comparing with previous models based on existing investigated materials and actual measured values in accordance with real cases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.97-100
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• 2016

Using the CNN(Convolutinal Neural Network), Deep Learning for variety of fields are being developed and these are showing significantly high level of performance at image recognition field. In this paper, we show the test accuracy which is learned by large-scale training data, over 5,000,000 of Korean characters. The architecture of CNN used in this paper is KCR(Korean Character Recognition)-AlexNet newly created based on AlexNet. KCR-AlexNet finally showed over 98% of test accuracy. The experimental data used in this paper is large-scale Korean character database PHD08 which has 2,187 samples for each Korean character and there are 2,350 Korean characters that makes total 5,139,450 sample data. Through this study, we show the excellence of architecture of KCR-AlexNet for learning PHD08.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.37-43
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• 2022

Drones, one of the 4th industrial technologies that are expanding from military use to industrial use, are being actively used in the search missions of the National Police Agency and finding missing persons, thereby reducing interest in a wide area and the input of large-scale search personnel. However, legal review of police drone operation is continuously required, and the importance of advanced system for related operations and analysis of captured images in connection with search techniques is increasing at the same time. In this study, in order to facilitate recording, preservation, and monitoring in the concept of precise search and monitoring, it is possible to achieve high efficiency and secure golden time when precise search is performed by constructing spatial information based on photo rather than image data-based search. Therefore, we intend to propose a spatial information construction technique that reduces the resulting data volume by adjusting the unnecessary spatial information completion rate according to the size of the subject. Through this, the scope of use of drone search missions for large-scale areas is advanced and it is intended to be used as basic data for building a drone operation manual for police searches.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.390-397
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• 2021

As digital twin technology is applied to various industrial fields, technologies to effectively process large amounts of data are required. In this paper, we discuss a customized service method for fast search and effective delivery of large-scale data for underground facility for public utilities management. The proposed schemes are divided into two ways: a fast search data generation method and a customized information service segmentation method to efficiently search and abbreviate vast amounts of data. In the high-speed search data generation, we discuss the configuration of the synchronization process for the time series analysis of the sensors collected in the underground facility and the additional information method according to the data reduction. In the user-customized service method, we define the types of users in normal and disaster situations, and discuss how to service them accordingly. Through this study, it is expected to be able to develop a systematic data generation and service model for the management of underground utilities that can effectively search and receive large-scale data in a disaster situation.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.49-56
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• 2014

In this paper, we propose a method for identifying hidden principal sentiments among large scale texts from documents, social data, internet and blogs by analyzing standard language, slangs, argots, abbreviations and emoticons in those words. The IRLBA(Implicitly Restarted Lanczos Bidiagonalization Algorithm) is used for principal component analysis with large scale sparse matrix. The proposed system consists of data acquisition, message analysis, sentiment evaluation, sentiment analysis and integration and result visualization modules. The suggested approaches would help to improve the accuracy and expand the application scope of sentiment analysis in social data.

• Journal of the Korean Institute of Educational Facilities
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• v.23 no.4
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• pp.3-10
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• 2016

This paper presents the investigation results on the classification of the university campuses. For the classification, we selected the spatial index as the evaluation indicator since the environmental factors and maintenance methods vary from university campus to university campus. For the study, we used eight spatial indices of the 30 national universities. This paper provides the spatial characteristics of different campus types, presents campus classification analysis as a future research approach to campus maintenance, and provides the data for the future study of comparison among universities. The results are as follows. 1) The classification investigation categorized the university campuses into three groups. Type 1 is a large-scale type, located near downtown. Type 2 is a medium-scale type, located at a remote site from downtown. Type 3 is a small-scale type, which is located comparatively near downtown. 2) Type 1 is a large-scale mixed area type, and 13 universities belong to this group. Type 2 is a medium-scale suburban area type, and six universities are in this group. Finally, Type 3 is a small-scale downtown area type, and 11 universities belong to this group.

• Information Systems Review
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• v.14 no.1
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• pp.1-19
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• 2012

A large-scale IS development project takes a long time, thus its project manager needs to be more careful on risk management. In particular, appropriate cutover decision making is critical in large-scale IS development projects because the opening of the large-scale IS significantly impacts the organization. Regardless of its importance, cutover decision making in conventional IS development projects has been done in a quite simple way. Conventional cutover decisions have been made by considering just whether the new IS operates or not from the system, application, and data implementation perspectives. However, this approach may lead to unsatisfactory performance or system failure in complex large-scale IS development. Under this background, we propose a new framework for cutover decision making on large-scale IS projects. To validate the applicability, we applied the framework to a core banking system development case. The case study shows that our framework is effective in proper cutover decision making.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.56.2-56.2
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• 2013

We study the three-dimensional genus topology of large-scale structure using the CMASS Data Release 11 sample of the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). The CMASS sample yields a genus curve that is characteristic of one produced by Gaussian random-phase initial conditions. The data thus supports the standard model of inflation where random quantum fluctuations in the early universe produced Gaussian random-phase initial conditions. Modest deviations in the observed genus from random phase are as expected from the nonlinear evolution of structure. We construct mock SDSS CMASS surveys along the past light cone from the Horizon Run 3 (HR3) N-body simulations, where gravitationally bound dark matter subhalos are identified as the sites of galaxy formation. We study the genus topology of the HR3 mock surveys with the same geometry and sampling density as the observational sample, and the observed genus topology to be consistent with LCDM as simulated by the HR3 mock samples.