• Journal of the Korean Society for Library and Information Science
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• v.34 no.2
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• pp.211-232
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• 2000

The acquisition of foreign government documents by exchange or gift is problematic in the electronic government era because the pattern of distributing system is changed. The alternatives for the collection development are developing the meta-site and establishing the subject gateways for the foreign government information as the resource discovery tools. The anchor and link functions of the AltaVista are used to evaluate a meta-site. The Scout Project Isaac is suggested as the model of interoperating resource discovery services for the geographically distributed metadata databases.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.63-72
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• 2009

Korean Land Spatialization Program (KLSP) is an R&D program of the National GIS Project for developing ubiquitous GIS technologies under the control of the M inistry of Land, Transport and Maritime Affairs (M LTM). The first program of the KLSP, which lasts from 2006 to 2012, initiated with $132 million of national funds and $42 million of private matching funds. Aiming to develop the 'Innovation of GIS technology for ubiquitous Korean land', the KLSP consists of five core research projects and one research coordination project to practically utilize and commercialize the results of core research projects. The Korean Land Spatialization Group (KLSG) is planning the KLSP Test-Bed for testing, integrating, and exhibiting the KLSP's outcomes. About 40% of the outcomes are related products to geo-sensor and wireless sensor network (W SN). Thus, interoperable, scalable and web accessible frameworks like an OGC SWE (Open Geospatial Consortium Sensor Web Enablement) are mandatory because some of the products must be connected to each other in a KLSG Test-Bed. The main objective of this paper is to introduce the KLSP Test-Bed and the SWE SOS (Sensor Observation Service) prototype, which is developed for interoperable geo-sensor networks of the KLSP.

• KEPCO Journal on Electric Power and Energy
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• v.3 no.1
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• pp.17-21
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• 2017

Recently, the Korea Enhanced Power system Simulator (KEPS) was upgraded with new processor cards and various peripheral devices. The purpose of the upgrade is for the large-scale AC/DC simulation studies for different purposes such as FACTS, PMU applications, and SPS. The need for such study is now growing in KEPCO network. Frequently, traditional ways of performing the necessary study had been found to be less adequate for guiding the key decisions in the company. Therefore, the growing needs, as well as the revealed inadequacy of the traditional system studies, had been attributing to the momentum for the upgrade project. This paper explains the details of the upgrade project. As an example of presenting the effectiveness of the upgraded RTDS system in KEPRI-KEPCO, a large-scale AC/DC real time simulation case which includes the entire Korean network and a planned MMC-HVDC system is introduced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1145-1149
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• 2008

This paper presents KEPCO research project for developing wide area DAS by reengineering conventional DAS and its functional upgrade. Through the research project system design requirements were prepared based on the local operation status and oversea examples. Application softwares were also developed supporting integrated operation of several area. The system implementation was performed on Choongnam regional head office for pilot operation. The developed wide area DAS will be fully installed on 41 DCC nation wide and expected to contribute on innovation of our distribution network operation.

• Water Engineering Research
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• v.5 no.3
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• pp.147-156
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• 2004

Recently, the population growth, industrial and agricultural development are rapidly undergoing in the Lower Rio Grande Valley (LRGV) in Texas. The Lower Rio Grande Valley (LRGV) composed of the 4 counties and three of them are interesting for Non-point and point source pollutant modeling: Starr, Cameron, and Hidalgo. Especially, the LRGV is an intensively irrigation region, and Texas A&M University Agriculture Program and the New Mexico State University College of Agriculture applied irrigation district program (Guy Fipps and Craig Pope, 1998), projects in GIS and Hydrology based agricultural water management systems and assessment of prioritized protecting stream network, water quality and rehabilitation based on water saving potential in Rio Grande River. In the LRGV region, where point and non-point sources of pollution may be a big concern, because increasing fertilizers and pesticides use and population cause. This project objective seeks to determine the accumulation of non-point and point source and discuss the main impacts of agriculture and environmental concern with water quality related to pesticides, fertilizer, and nutrients within LRGV region. The GIS technique is widely used and developed for the assessment of non-point source pollution in LRGV region. This project shows the losses in kg/$km^2$/year of BOD (Biological Oxygen Demand), TN (total Nitrogen) and TP (total phosphorus) in the runoff from the surface of LRGV.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2265-2271
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• 2011

This paper presents an u-health Care system based on context-awareness information. Extended project induced an education for creative engineering design. It could occur one of the issue for life care or wellness in recently life. Especially u-health Care system help on achieving this object as an efficient care-training with user blood pressure or with echocardiogram, electromyogram, electroencephalogram, or Electrocardiography. In this paper, development project show an u-health care system based on this parameters of the context-awareness with network service and analysis provide with working memory(DB) for user data base applications.

• Reproductive and Developmental Biology
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• v.36 no.1
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• pp.39-47
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• 2012

Ovarian cancer is the most lethal gynecological malignancy, and specific biomarkers are important needed to improve diagnosis, prognosis, and to forecast and monitor treatment efficiency. There are a lot of pathological factors, including reactive oxygen species (ROS), involved in the process of cancer initiation and progression. The oxidative modification of proteins by ROS is implicated in the etiology or progression of disorders and diseases. In this study, a labeling experiment with the thiol-modifying reagent biotinylated iodoacetamide (BIAM) revealed that a variety of proteins were differentially oxidized between normal and tumor tissues of ovarian cancer patients. To identify cysteine oxidation-sensitive proteins in ovarian cancer patients, we performed comparative analysis by nano-UPLC-$MS^E$ shotgun proteomics. We found oxidation-sensitive 22 proteins from 41 peptides containing cysteine oxidation. Using Ingenuity program, these proteins identified were established with canonical network related to cytoskeletal network, cellular organization and maintenance, and metabolism. Among oxidation-sensitive proteins, the modification pattern of Collagen alpha-1(VI) chain (COL6A1) was firstly confirmed between normal and tumor tissues of patients by 2-DE western blotting. This result suggested that COL6A1 might have cysteine oxidative modification in tumor tissue of ovarian cancer patients.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.26-31
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• 2015

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1501-1510
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• 2014

Low voltage direct current (LVDC) distribution system is a suitable techno-economic candidate which can create an innovative solution for distribution network development with respect to rural electrification. This research focuses on the use of LVDC distribution system to replace some of KEPCO's existing traditional medium voltage alternating current (MVAC) distribution network for rural electrification in South Korea. Considering the technical and economic risks and benefits involved in such project, a comparative techno-economic analysis on the LVDC and the MVAC distribution networks is conducted using economic assessment method such as the net present value (NPV) on a discounted cash flow (DCF) basis as well as the sensitivity analysis technique. Each would play a role in an economic performance indicator and a measure of uncertainty and risk involved in the project. In this work, a simulation model and a computational tool are concurrently developed and employed to aid the techno-economic analysis, evaluation, and estimation of the various systems efficiency and/or performance.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.277-292
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• 2018

This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.