• Journal of Radiation Industry
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• v.18 no.3
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• pp.209-215
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• 2024

Radiation work always carries the risk of radiation exposure, so regulatory agencies manage it through licensing when high exposure is expected. However, due to passive management methods using TLD, etc., there are cases where risk management is done after an incident occurs or the incident is covered up. In this study, we developed a system to manage the location of radiation work and the risk of workers in real time through a safety management platform and a location-based personal dosimeter. The safety platform server receives data from the developed personal dosimeter in real time and manages risks in three steps for each worker using location and dose rate, and can predict risks and generate alarms in real time. The personal dosimeter transmits the location and dose rate of the worker in real time using GPS and LTE communication. The developed safety management platform and personal dosimeter were verified through a field test to receive real-time data of the location and dose rate data of the worker, and the risk management function according to the individual dose rate was verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.161-171
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• 2009

In this paper, we propose an automatic method for setting data constraints of a data cleansing service, which is for managing the quality of data exchanged between composite services based on SOA(Service-Oriented Architecture) and enables to minimize human intervention during the process. Because it is impossible to deal with all kinds of real-world data, we focus on business data (i.e. costumer order, order processing) which are frequently used in services such as CRM(Customer Relationship Management) and ERP(Enterprise Resource Planning). We first generate an extended-element vector by extending semantics of data exchanged between composite services and then build a rule-based system for setting data constraints automatically using the decision tree learning algorithm. We applied this rule-based system into the data cleansing service and showed the automation rate over 41% by learning data from multiple registered services in the field of business.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.97-112
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• 2013

The quality of the data is the most basic prerequisite for effective use of data. Problems and the resulting loss due to error data has emerged using case studies and a number of, to a national, quality certification system of the data has been enforced, you must manage to generate data study on the method for selecting the point of view of an organization's data CTQ is a very unsatisfactory state of affairs. Selected CTQ main data is subject to quality control in the organization, to develop criteria for CTQ data side of the business and IT so that it can be managed in a systematic manner, the proposed model, to filter the data accordingly presented in detail how to manage enterprise-wide CTQ data that can be quantified Te. By utilizing SPSS, factor analyzes, for which I used the AHP method for quantification. In particular, we present a framework of management measures along the maturity of the data in the organization due to the enforcement of authentic quality certification system of DB, utilizing the CTQ-DSMM model readily applicable to practice.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.109-119
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• 2008

The number of card users has grown steadily after the adaption of smart card. Considering the diverse information from smart card data, the increase of card usage rate leads to various useful implications meaning in travel pattern analysis and transportation policy. One of the most important implications is the possibility that the data enables us to generate transit O/D tables easily. In the case of generating transit O/D tables from smart card data, it is necessary to filter data error and/or data missing. Also, the correction of data missing is an important procedure. In this study, it is examined to compute the level of data error and data missing, and to correct data missing for transit O/D generation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.945-956
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• 2020

The publication of data is essential in order to receive high quality services from many applications. However, if the original data is published as it is, there is a risk that sensitive information (political tendency, disease, ets.) may reveal. Therefore, many research have been proposed, not the original data but the synthetic data generating and publishing to privacy preserve. but, there is a risk of privacy leakage still even if simply generate and publish the synthetic data by various attacks (linkage attack, inference attack, etc.). In this paper, we propose a synthetic data generation algorithm in which privacy preserved by applying differential privacy the latest privacy protection technique to GAN, which is drawing attention as a synthetic data generative model in order to prevent the leakage of such sensitive information. The generative model used CGAN for efficient learning of labeled data, and applied Rényi differential privacy, which is relaxation of differential privacy, considering the utility aspects of the data. And validation of the utility of the generated data is conducted and compared through various classifiers.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.99-107
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• 2024

The recent digital transformation in many industries has led to an explosion of data, which has exponentially increased the amount of data that companies need to generate and process. As a result, enterprises are leveraging ERP systems to manage and analyze large amounts of data in real time. However, due to cost and time issues in processing large amounts of data in existing ERP systems, it is essential to apply data archiving technology that can compress and store data in real time in existing systems. Therefore, this paper aims to identify the trends of the target technology by utilizing patent data on ERP-based real-time data archiving technology, analyze the core patents, and create new core patents based on them.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.219-223
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• 2012

For an off-site consequence analysis at nuclear power plant, MELCOR Accident Consequence Code System(MACCS) II code is widely used as a software tool. In this study, the algorithm of web-based off-site consequence analysis program(OSCAP) using the MACCS II code was developed for an Integrated Leak Rate Test (ILRT) interval extension and Level 3 probabilistic safety assessment(PSA), and verification and validation(V&V) of the program was performed. The main input data for the MACCS II code are meteorological, population distribution and source term information. However, it requires lots of time and efforts to generate the main input data for an off-site consequence analysis using the MACCS II code. For example, the meteorological data are collected from each nuclear power site in real time, but the formats of the raw data collected are different from each site. To reduce the efforts and time for risk assessments, the web-based OSCAP has an automatic processing module which converts the format of the raw data collected from each site to the input data format of the MACCS II code. The program also provides an automatic function of converting the latest population data from Statistics Korea, the National Statistical Office, to the population distribution input data format of the MACCS II code. For the source term data, the program includes the release fraction of each source term category resulting from modular accident analysis program(MAAP) code analysis and the core inventory data from ORIGEN. These analysis results of each plant in Korea are stored in a database module of the web-based OSCAP, so the user can select the defaulted source term data of each plant without handling source term input data.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.495-506
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• 2001

XML makes it possible for data to be exchanged regradless of the data model in which it is represented or the platform on which it is stored, serving as a standard for data exchange format on the internet. Especially, it is natural to send XML data without DTD on the internet when XML is data-centric. Therefore it is needed to extract relational schema to store XML data efficiently, to optimize queries, and to publish data which have been stored in the relational database in the XML format. In this paper, we proposed a method to generate relational database in the XML documents without DTD and store XML data using upper/lower bound schema extraction technique for semistructured data. In extracting a lower bound schema, we especially show an efficient technique for creating relational schema by using simulation with is more advanced than the datalog method.