• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.127-132
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• 2004

기업에서 올바른 품질관리를 수행하기 위해서는 1차적으로 품질수준을 결정하는 각각의 요인들에 대한 정확한 데이터의 확보가 필요하며, 이를 위한 측정(measurement) 수단 또한 매우 중요하다고 할 수 있다. 올바른 측정을 위해서는 양호한 측정기기 뿐만 아니라, 이를 효과적으로 다룬 수 있는 측정인력의 존재 통은 필수적 요건이라 할 수 있다. 따라서 본 연구에서는 제품 생산의 불량률에 대한 추정방정식으로 많이 사용되는 로짓(logit) 모형을 이용하여 기업의 측정표준 관련 투자가 제품의 품질에 미치는 효과를 분석하였는데, 그 결과 측정 및 풀질관리 인력, 측정설비 구입비 및 표준실 운영비 통의 측정 관련 투자, 인증표준물질의 사용이 제품생산의 불량률 감소를 가져와 제품의 풀질에 효과를 미치는 것으로 나타났다. 또한 기업의 측정표준 투자 결정요인을 분석한 결과, 기업의 연구개발 투자가 많을수록, 시장에서의 독점력이 높을수록, 기업규모가 클수록, 피리고 재무구조가 탄탄할수록 측정표준 투자가 큰 것으로 나타났다.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.951-962
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• 2019

Producing consistent, accurate soil moisture data to be utilized as a reference dataset for researches related to hydrological cycle and natural disaster is being critical, but such techniques (e.g. quality control) are still limited to improve reliability of soil moisture data. In this study, analyses of soil moisture's behavior and quality control based on International Soil Moisture Network's (ISMN's) criteria were carried out in Yongdam study watershed, which is UNESCO-IHP' representative examination area in South Korea, to suggest a direction to improve the quality of soil moisture data. The results of the behavior analysis showed normal increasing/decreasing patterns following precipitation events in all stations except two (i.e. Bugui, Ancheon). As a result of applying quality flagging technique, there were no observation recordings in abnormal range, and freezing of soil moisture occurred within general range (~20%). Soil moisture rise without prior rainfall appeared about 4% and there were less than 0.01% for spike and 5% for plateau. Producing more reliable reference data will be possible if site-specific criteria for quality control are considered enough in the future.

• The Journal of Bigdata
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• v.6 no.1
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• pp.145-151
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• 2021

In recent years, digital data has been generated in all areas of human activity, and there are many attempts to safely store and process the data to develop useful services. A data lake refers to a data repository that is independent of the source of the data and the analytical framework that leverages the data. In this paper, we designed a tool to safely store various big data generated by smart cities in a data lake and ETL it so that it can be used in services, and a web-based tool necessary to use it effectively. Implement. A series of processes (ETLs) that quality-check and refine source data, store it safely in a data lake, and manage it according to data life cycle policies are often significant for costly infrastructure and development and maintenance. It is a labor-intensive technology. The mounting technology makes it possible to set and execute ETL work monitoring and data life cycle management visually and efficiently without specialized knowledge in the IT field. Separately, a data quality checklist guide is needed to store and use reliable data in the data lake. In addition, it is necessary to set and reserve data migration and deletion cycles using the data life cycle management tool to reduce data management costs.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.350-354
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• 2010

전 세계적인 지구 온난화 현상으로 해수면 상승과 생태계 변화가 발생하여 기후변화에 대한 사회적관심이 증가하고 있다. 이와 더불어 기후변화와 지구환경시스템의 대기, 수권, 생물권, 지표면 동 각 권역간의 상호작용과 피드백을 고려한 연구가 증가하고 있는 실정이다. 기후와 환경을 통합적으로 분석하여 기후변화에 따른 지구환경시스템의 변화특성을 이해하고 이러한 피드백 과정을 파악하기 위해서는 분석 자료의 원활한 공유와 연계를 위한 통합 데이터베이스 구축이 필요하다. 이를 위해서는 먼저 다양한 기후/환경 연구 분야의 자료를 관리하기 위한 데이터의 의미, 명칭, 정의 등에 대한 원칙의 수립이 요구된다. 따라서 본 연구에서는 기후/환경 변화예측 연구 자료의 원활한 공유와 관리를 위한 데이터 표준화 연구를 수행하였다. 기후/환경 변화예측 연구 분야의 자료 현황을 조사 및 분석하였고 그에 따른 자료 관리 방안을 마련하였다. 그 결과 관리할 오브젝트를 기준으로 기후/환경 연구 분야의 데이터 표준화를 수행하였고 표준단어, 표준도메인, 표준용어를 정의하였다. 데이터 표준화 결과는 기후/환경 변화예측 자료를 관리하고 공유하는데 있어 데이터의 의미를 효율적으로 파악하고, 데이터베이스 설계과정에서 데이터의 품질과 생산성을 향상 시킬 수 있다. 향후 연구에서는 데이터베이스 개념적 엔티티의 속성설계 단계부터 데이터 표준을 적용한 통합 데이터베이스 구축이 필요하다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.288-289
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• 2015

EMS(Energy Management System: 에너지 관리 시스템)에서 SCADA(Supervisory Control and Data Acquisition: 집중원격감시 시스템)은 RTU(Remote Terminal Unit), RCC(Remote Control Centre) 등을 통해 데이터를 취득한다. 이렇게 취득된 데이터는 토폴로지(Topology)를 구성하고, 계통을 감시하고 해석하는데 사용된다. EMS에서 계통해석 기술은 발전, 송전, 변전, 배전 계통이 유기적으로 결합되어 원활한 전력의 공급과 높은 신뢰도를 제공할 수 있어야 하는데 이를 위해서는 항상 신뢰할 수 있는 데이터를 사용해야 한다. 하지만 SCADA 취득 데이터는 통신 선로 상의 지연 등 통신 오류를 포함하고 있기 때문에 항상 신뢰할 수는 없으며, 오차를 포함한 측정 데이터를 사용하여 계통 해석을 할 경우 계통 해석 결과 전체를 신뢰할 수 없게 된다는 문제점이 있다. 본 논문에서는 원격 장치 신호 품질 불량 시 계통해석 데이터를 보다 신뢰할 수 있도록 취득, 사용하는 방안에 대해 제안한다.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.1
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• pp.57-68
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• 2009

In automotive industries, reusability of product information is increasing through database construction of previous product data. The product data is stored by data quality management in product information systems. For naval ships, have the functional similarity by the ships of the same classification and class, that are built by series. Information of hull structures as well as embarked equipments are similar. So it would be effective to use database systems that are considered product information quality of previous ships in design and production processes. In this paper we discuss product information quality in database construction of naval ship product models. For this, we propose a basic concept and reference model for data quality verification. Based on this concept, A verification guideline is defined and it is applied for the case study of the digital naval ship which was built to the naval ship product model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.165-170
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• 2024

This study aims to verify the accuracy of the air quality management system in Yangju City using an artificial intelligence (AI) evaluation model. The consistency and reliability of fine dust data were assessed by comparing public data from the Ministry of Environment with data from Yangju City's air quality management system. To this end, we analyzed the completeness, uniqueness, validity, consistency, accuracy, and integrity of the data. Exploratory statistical analysis was employed to compare data consistency. The results of the AI-based data quality index evaluation revealed no statistically significant differences between the two datasets. Among AI-based algorithms, the random forest model demonstrated the highest predictive accuracy, with its performance evaluated through ROC curves and AUC. Notably, the random forest model was identified as a valuable tool for optimizing the air quality management system. This study confirms that the reliability and suitability of fine dust data can be effectively assessed using AI-based model performance evaluation, contributing to the advancement of air quality management strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.319-322
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• 2019

There are many techniques for locating users in an indoor spot. Among them, WiFi fingerprinting technique which is widely used is phased into a data collection step and a positioning step. In the data collection step, all surrounding Wi-Fi signals are collected and managed as a list. The more data collected, the better the accuracy of the indoor position based on Wi-Fi fingerprint. Existing high-quality data collection and management methods are time consuming and costly, and many operations are required to extract and generate data necessary for machine learning. Therefore, we research how to collect and manage large amount of data in limited resources. This paper presents efficient data collection methods and data generation for learning.