• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.98-102
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• 2023

This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

• ETRI Journal
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• v.26 no.2
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• pp.149-160
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• 2004

In this paper, we present an interactive data broadcasting system compliant with the Advanced Television Systems Committee (ATSC) standards. The proposed system provides users not only with various data broadcasting services but also remote interactive services. For various data broadcasting services, we have adopted a synchronized data injector that calculates the transmission time of synchronized data accurately and multiplexes synchronized data with the data of an MPEG-2 audio-visual program according to the calculated transmission time. To support remote interactive services, we designed and implemented a return channel server connected on a bi-directional interaction channel. Test results show that the proposed system provides both an asynchronous and synchronized data broadcasting service and remote interactive service appropriately.

• Applied Microscopy
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• v.50
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• pp.2.1-2.7
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• 2020

Electron energy loss spectroscopy (EELS) is an analytical technique that can provide the structural, physical and chemical information of materials. The EELS spectra can be obtained by combining with TEM at sub-nanometer spatial resolution. However, EELS spectral information can't be obtained easily because in order to interpret EELS spectra, we need to refer to and/or compare many reference data with each other. And in addition to that, we should consider the different experimental variables used to produce each data. Therefore, reliable and easily interpretable EELS standard reference data are needed. Our Electron Energy Loss Data Center (EELDC) has been designated as National Standard Electron Energy Loss Data Center No. 34 to develop EELS standard reference (SR) data and to play a role in dissemination and diffusion of the SR data to users. EELDC has developed and collected EEL SR data for the materials required by major industries and has a total of 82 EEL SR data. Also, we have created an online platform that provides a one-stop-place to help users interpret quickly EELS spectra and get various spectral information. In this paper, we introduce EEL SR data, the homepage of EELDC and how to use them.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.609-626
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• 2013

Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.137-162
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• 2021

This study aimed to examine the perception and experience of researchers in the field of mechanical engineering on research data creation and management, and suggest implications for research data management and services in the field. Research data management and services of domestic and foreign research institutes were investigated, and in-depth interviews were conducted with researchers belonging to domestic mechanical engineering research institutes to analyze the perception and conduction of research data creation and management according to four major categories: "research data, accountable conducting of research and compliance with research ethics, utility and effectiveness of research data management, and the value of sharing research data." To ensure effective research data management and services in mechanical engineering, it is necessary to conduct a data investigation on the process, type, and form of production to collect explicit metadata and implicit contextual information. It is also necessary to propose a plan to recognize research results using the publication of data journals and to prepare infrastructure such as a cloud-based system that supports safe data management and communication between researchers. In addition, it suggests that it is important for various officials in the research field to allocate roles and responsibilities for research data management and services at the organizational level.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.317-344
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• 2019

Increasing the value of data, paradigm shifts in research methods, and specific manifestations of open science indicate that research is no longer text-centric, but data-driven. In this study, we analyzed the services for DCC, ICPSR, ANDS and DataONE to derive key elements and functional requirements for research data management and services that are still insufficient in domestic research. Key factors derived include DMP writing support, data description, data storage, data sharing and access, data citations, and data management training. In addition, by presenting functional requirements to the derived key elements, this study can be applied to construct and operate RDM service in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.117-124
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• 2021

In this paper, we propose CANVAS (Creative ANalytics enVironment And System), an analytics system of the National Research Data Platform (DataON). CANVAS is a personalized analytics cloud service for researchers who need computing resources and tools for research data analysis. CANVAS is designed in consideration of scalability based on micro-services architecture and was built on top of open-source software such as eGovernment Standard framework (Spring framework), Kubernetes, and JupyterLab. The built system provides personalized analytics environments to multiple users, enabling high-speed and large-capacity analysis by utilizing high-performance cloud infrastructure (CPU/GPU). More specifically, modeling and processing data is possible in JupyterLab or GUI workflow environment. Since CANVAS shares data with DataON, the research data registered by users or downloaded data can be directly processed in the CANVAS. As a result, CANVAS enhances the convenience of data analysis for users in DataON and contributes to the sharing and utilization of research data.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.595-600
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• 2012

This study is to assess the application of SWAT-CUP(Soil and Water Assessment Tool-Calibration Uncertainty Programs) and to extend daily flow data from 8-day interval flow data which has been measured by Korean Ministry of Environment(MOE). Model sensitivity analysis and calibration were performed with sequential uncertainty fitting(SUIF-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP. The most sensitive parameters were SOL_K.sol, CH_N2.rte, CN2.mgt, SOL_BD.sol, ALPHA_BF.gw, ALPHA_BNK.rte, SOL_AWC.sol, CH_K2.rte, SFTMP.bsn, GW_DELAY.gw. Following the sensitivity analysis, SWAT-CUP calibration was carried out using 8-day interval flow data from January 2008 to December 2010. The results were then assessed based on the visual agreement and simulated flow plots and the performance statistics generated $R^2$ and NSE which are 0.71 and 0.61 respectively. Results of these statistics indicated that there was a good agreement between the observed and simulated flow. To extend daily flow data from 8-day interval flow data, parameters, which were estimated by SWAT-CUP, re-entered for SWAT model. As a result, the observed flow data were found to reflect the trend of simulated flow data. From these results, it is thought that this method could be used to provide daily flow data using 8-day interval flow data.

• Journal of Computing Science and Engineering
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• v.2 no.3
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• pp.221-248
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• 2008

Data provenance is the background knowledge that enables a piece of data to be interpreted and used correctly within context. The importance of tracking provenance is widely recognized, as witnessed by significant research in various areas including e-science, homeland security, and data warehousing and business intelligence. In order to further advance the research on data provenance, however, one must first understand the research that has been conducted to date and identify specific topics that merit further investigation. In this work, we develop a framework based on semiotics theory to assist in analyzing and comparing existing provenance research at the conceptual level. We provide a detailed review of data provenance research and compare and contrast the research based on d semiotics framework. We conclude with an identification of challenges that will drive future research in this field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.430-432
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• 2022

With the recent increase in interest in the open science movement in science and technology fields such as open access, open data, and open source, the movement to share and utilize publicly funded research products is materializing and revitalizing. In line with this trend, many efforts are being made to establish and revitalize a system for sharing and utilizing research data, which is a key resource for research in Korea. These efforts are mainly focused on collecting research data by field and institution, and linking it with DataON, a national research data platform, to search and utilize it. However, developed countries are building a system that can share and utilize not only such research data but also various types of R&D-related computing resources such as IaaS, PaaS, SaaS, and MLaaS. EOSC (European Open Science Cloud), ARDC (Australian Research Data Commons), and CSTCloud (China S&T Cloud) are representative examples. In Korea, the Korea Research Data Commons (KRDC) is designed and a core framework is being developed to facilitate the sharing of these computing resources. In this study, the necessity, concept, composition, and future plans of KRDC are introduced.