Earticle

As renewable energy penetration continues to increase, the output variability and forecasting uncertainty of photovoltaic generation have emerged as major operational risks in power systems. This study establishes a sensor-based data quality control procedure to ensure the reliability of meteorological data collected at a PV plant. For temperature, humidity, and wind speed, a four stage QC process physical range check, persistence check, step change check, and median filtering was applied. Solar radiation, which exhibits strong temporal and distributional characteristics, was processed using a three-stage QC procedure consisting of physical range, step change, and frequency distribution checks. Using the quality-controlled meteorological data, PV generation forecasting was performed with SVM and XGBoost models. As a result, the MAPE values improved to 6.32% for SVM and 6.08% for XGBoost after QC application. The findings confirm that meteorological data quality control significantly enhances PV forecasting accuracy and can support future strategies for distributed energy resource management, curtailment mitigation, and power system risk reduction.

With the rapid expansion of renewable energy deployment, power systems are increasingly exposed to issues such as higher output variability. Photovoltaic generation, as the most widely installed variable renewable energy source both domestically and internationally, exhibits significant fluctuations due to weather conditions. These characteristics lead to operational challenges including increased curtailment, higher reserve requirements, and even risks of large-scale outages. This study aimed to improve the accuracy of photovoltaic power generation forecasting by developing a data quality control procedure for meteorological data collected at a PV plant. The quality-controlled data were used as inputs to SVM and XGBoost, resulting in improved forecasting accuracy, with MAPE decreasing from 7–10% to 6.32% and 6.08%, respectively. The results demonstrate that meteorological data quality control significantly enhances PV forecasting performance and can contribute to distributed energy resource operation and curtailment mitigation strategies.

High-throughput genotyping and sequencing has revolutionized animal breeding by providing access to vast amounts of genomic data to facilitate precise selection for desirable traits. This shift from traditional methods to genomic selection provides dense marker information for predicting genetic variants. However, the success of genomic selection heavily depends on the accuracy and quality of the genomic data. Inaccurate or low-quality data can lead to flawed predictions, compromising breeding programs and reducing genetic gains. Therefore, stringent quality control (QC) measures are essential at every stage of data processing. QC in genomic data involves managing single nucleotide polymorphism (SNP) quality, assessing call rates, and filtering based on minor allele frequency (MAF) and Hardy-Weinberg equilibrium (HWE). High-quality SNP data is crucial because genotyping errors can bias the estimates of breeding values. Cost-effective low-density genotyping platforms often require imputation to deduce missing genotypes. QC is vital for genomic selection, genome-wide association studies (GWAS), and population genetics analyses because it ensures data accuracy and reliability. This paper reviews QC strategies for genomic data and emphasizes their applications in animal breeding programs. By examining various QC tools and methods, this review highlights the importance of data integrity in achieving successful outcomes in genomic selection, GWAS, and population analyses. Furthermore, this review covers the critical role of robust QC measures in enhancing the reliability of genomic predictions and advancing animal breeding practices.

Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.

The competence of enterprise for managing information is evaluated not by the amount of information but by the quality of information such as response time, data consistency, and data correctness. The degradation of data quality is usually caused by the inappropriate process of managing the structure and value of stored data. According to the recent survey on the actual condition of data quality management, the correctness and consistency of data appeared to be the most problematic area among the six criteria of data quality management such as correctness, consistency, availability, timeliness, accessibility, and security. Moreover, the problem was more serious in case of small and medium-sized companies than large enterprises. In this paper, therefore, we attempt to propose a new data quality control methodology for small and medium-sized companies that can improve the correctness and consistency of data without consuming too much time and cost. To adopt the proposed methodology to real application immediately, we provided some scripts for as-is analysis and devised automation tools for managing naming rules of vocabulary, terminology, and data code. Additionally, we performed case study on the distribution service of a small-sized company to estimate the applicability of our tool and methodology.

The paper reviews the methodologies of confirmatory data analysis(CDA) and exploratory data analysis(EDA) in statistical quality control(SQC), design of experiment(DOE) and reliability engineering(RE). The study discusses the properties of flexibility, openness, resistance and reexpression for EDA.

Since 1990s, construction firms adopted process management to improve internal and external business structure based on process. It however has a few improvement functions. In addition, business support systems for business process efficiency improvement were developed, but separation between business process and IT system occurred because it focused on function-based individual construction and data input/output. To solve this problem, this study proposed business process and ITintegrationmodelforsupportingusertousebusinessinformationandsystembasedonvisualizedbusinessproce ss and improving use of previous business support system.

본 논문은 온도 등 7 가지의 IoT 센서에서 수집된 기상데이터의 각 기상요소에 대하여 품질관리(Quality Control; QC)를 하였다. 또한, 우리는 측정된 값에 오류가 있는 데이터를 기계학습으로 의미있게 추정하는 방법을 제안한다. 수집된 기상데이터를 기본 QC 결과를 바탕으로 오류 데이터를 선형 보간하여 기계학습 QC를 진행하였으며, 기계학습 기법으로는 대표적인 서포트벡터회귀, 의사결정테이블, 다층퍼셉트론을 사용했다. 기본 QC의 적용 유무에 따라 비교해 보았을 때, 우리 는 기본 QC를 거쳐 보간한 기계학습 모델들의 평균절대오차(MAE)가 21% 낮은 것을 확인할 수 있었다. 또한, 기계학습 기법에 따라 비교하여 서포트벡터회귀 모델을 적용하였을 때가, 모든 기상 요소에 대하여 MAE가 평균적으로 다층신경망 은 24%, 의사결정테이블은 58% 낮은 것을 알 수 있었다.

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

해양 탄성파탐사 자료취득시스템은 항측시스템, 기록시스템, 스트리머 케이블, 음원 등으로 구성된다. 항측시스템은 계획된 측선에 따라 탐사선을 계산된 위치로 이동시키며, 탐사선과 수중에서 예인되는 탐사장비의위치를 측정 및 계산하는 중요한 역할을 수행한다. 본 기술보고에서는 동해 울릉분지에서 심해 유가스전 탐사기술 개발을 위해 석유물리탐사선 탐해2호로 수행된 항측 자료취득 및 품질관리 등의 과정과 항측 자료취득및 품질관리의 중요성에 대해서 소개하고자 한다.

Marine seismic data acquisition system consists of navigation system, recording system, streamer cable and seismic source. Navigation system performs the important role steering the vessel along the pre-plot survey line and measuring and calculating the position between survey vessel and deployed equipments. To develop the deepwater hydrocarbon exploration technology in the Ulleung Basin, in this technical report, we introduce the processes of navigation data acquisition and quality control conducted by navigation system of R/V Tamhae II and report those importance.

Agricultural reservoirs are important facilities for storing or managing water for the purpose of securing agricultural water, creating and expanding agricultural production bases, and using them to increase agricultural production. In particular, the Korea Rural Community Corporation (KRC) manages agricultural reservoirs scattered across the country, and officially recognizes and distributes hydrological data to increase their public utilization and aims to improve the value of water resources. Data on the water level of agricultural reservoirs are important. However, errors such as missing values and outliners limit utilization of the data in various fields of research and industry. Therefore, water quality data measures should be devised to increase reliability. this study categorized different error types and looked at automatic correction methods to enhance the reliability of the vast hydrological data. In addition, the water level data corrected from errors were compared to the reference hydrologic data through expert judgment in accordance with the quality control procedure, and the most appropriate measures were verified. As KRC manages more agricultural reservoirs than any other institution, the proposed method of efficient and automatic water level data correction in this study is expected to increase the availability and reliability of the hydrological data.

수자원 설계 및 홍수 예 경보 등을 위한 수문분석 시, 강우자료는 필수요소이다. 현재 수문분석 시 비교적 장기간의 자료를 보유하고 있는 기상청, 국토교통부 등의 지상기상관측지점(SSS, Surface Synoptic Stations)에 시 강우자료를 이용하고 있으나, 집중호우가 빈번히 발생하는 현실정과 집중호우의 발생빈도가 증가할 것으로 예상되는 향후에는 더욱 조밀한 관측망을 구성하고 있는 방재기상관측지점(AWS, Automatic Weather Stations)의 분 단위 강우자료를 이용한 분석이 필요하다. 그러나 방재기상관측지점의 분 단위 강우자료는 자동으로 관측되고 있어, 자료품질에 대한 문제점이 매번 지적되고 있다. 본 연구에서는 서울지역을 중심으로 기상청 방재기상관측지점의 분 단위 강우자료의 품질개선 방안에 관한 연구를 실시하였다. 분 단위 강우자료의 품질관리방안은 크게 3단계로 결측치 품질관리, 이상치 품질관리 그리고 강우 보완 품질관리로 구분하여 품질관리 방안을 제시하고 분석을 수행하였다. 마지막으로 서울지점의 분 단위 강우자료와 시 단위 강우자료의 비교분석을 통해 강우 품질관리에 대한 평가를 실시하였다. 이는 향후 분 단위 강우자료를 이용한 수문분석 시, 강우자료 품질관리 방안으로 활용될 것으로 판단된다.

Rainfall data is necessary component for water resources design and flood warning system. Most analysis are used long-term hourly data of surface synoptic stations from the Meteorological Administration, Ministry of land, Infrastructure and Transport and others. However, It will be used minutely data of more high density automatic weather stations than surface synoptic stations expecting to increase the frequency of heavy precipitation. But minutely data has a problem about quality of rainfall data by auto observation. This study analyzed about quality control method using automatic weather station's minutely rainfall data of meteorological administration. It was performed assessment of the quality control that was classified quality control of miss Data, outlier data and rainfall interpolation. This method will be utilized when hydrological analysis uses minute rainfall data.

과학기술데이터는 엄격한 평가기준에 의해 신뢰성과 정확성이 보장된 참조표준(standard reference data) 값을 지닐 수 있어야 한다. 참조표준은 측정표준을 바탕으로 구하여진 결과 값을 표준화시키는 작업으로서 데이터의 생산조건, 실험환경, 측정방법, 데이터 처리 등이 검토되어 참조데이터(reference data)와 구분된다. 참조표준은 “공인된 수치데이터”로 표현되고 이는 측정결과의 신뢰도를 정량적으로 나타내는 불확도(uncertainty)로 표기된다. 본 연구에서는 참조표준에 대한 의미를 이해하고, 평가되지 않은 과학기술 데이터가 어떤 평가 과정을 거쳐 참조표준으로 분류되는지를 알아보기 위해 소재물성분야를 예로 살펴보았다. 그리고 국가 참조표준을 본격적으로 운영하기 위한 방안으로 국가참조표준센터, 데이터센터, 위원회의 역할과 임무, 이를 뒷받침하기 위한 법적, 제도적 방안을 제시하였다.

Data produced during scientific and technical activities usually have value as standard reference data (SRD) which is a well-documented numeric value assessed for reliability and accuracy. The main criteria in the evaluation for SRD is how well their production is documented including a sample preparation, measurement method, data processing and so on, other than reference data. SRD is represented as a certified numeric value with uncertainty. In this study, the SRD in the area of material properties was introduced to provide understanding of SRD and its evaluation method. Also the national SRD system was studied. The national SRD system is composed of the center for the SRD, the data centers, and the committee. The role and task of those components were studied. The legislational and systematic supports for the system were proposed in this study.

본 연구에서는 기상청에서 수행하는 기존의 기상 관측에 대한 품질관리 절차 이외에 향후 스마트시티 등에서 활용될 수 있는 머신러닝 기반의 Internet of Things (IoT) 도시기상 관측 자료에 대한 품질검사 기준을 제안한다. 현재 기상청에서 종관기상관측(Automated Synoptic Observing System, ASOS)과 방재기상관측(Automatic Weather System, AWS) 기반으로 설정한 기준이 도시기상에 적합한지 확인하기 위하여 서울시에 설치된 SKT AWS 자료를 기반으로 사용성을 검증하였고, IoT 자체의 데이터가 가지는 특성을 고려하여 최종적으로 머신러닝 기반의 품질검사 알고리즘을 제안하였다. 품질검사 방법으로는 IoT 기기 자체에 대한 결측값 검사, 값 패턴 검사, 충분 데이터 검사, 통계적 범위 이상 검사, 시간값 이상 검사, 공간값 이상 검사를 먼저 수행하고, 기상청에서 제시하고 있는 기상 관측에 대한 품질검사인 물리한계검사, 단계검사, 지속성 검사, 기후범위 검사, 내적 일치성 검사를 5가지 기상요소에 대하여 각각 수행하였다. 제안한 알고리즘의 검증을 위하여 인천광역시 송도에 위치한 관측소에 실제 IoT 도시기상관측 데이터에 이를 적용하였다. 이를 통해 기존의 기상청 QC로는 확인할 수 없었던 IoT 기기가 가질 수 있는 결함을 확인할 수 있고, 알고리즘에 대한 검증을 진행하여 향후 스마트시티에 설치될 IoT 기상관측기기에 대한 품질검사 방법을 제안한다.

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

본 연구는 X세대의 일반적특성과 건강행태가 삶의 질에 미치는 영향을 파악하고, 연령 내 이질성을 고려 한 맞춤형 건강증진 전략의 필요성을 제시하고자 하였다. 2024년 지역사회 건강조사를 활용하여 46-54세 34,103 명과 55-61세 32,294명 총 66,307명을 대상으로 삶의 질은 EQ-5D로 측정하였으며, SPSS 25.0을 활용해 복합표본 설계를 반영한 다중회귀분석을 실시하였다. 연구 결과, 두 집단에서 모두 가구소득, 교육수준, 경제활동 여부, 음 주 빈도, 주관적 스트레스 수준, PHQ-9, 고혈압 및 당뇨병 진단 유무가 삶의 질에 유의한 영향을 미쳤으며, 46-54세 집단에서는 남성이, 55-61세 집단에서는 배우자 유, 고강도 신체활동 실천, BMI(비만전단계)가 유의미한 요인으로 나타났다. 본 연구를 토대로 예비 노년층인 X세대 내 연령별 특성을 반영한 차별화된 건강 정책 수립 의 중요성을 강조하며, 향후 생애주기별 맞춤형 건강증진 전략 개발의 기초자료로 활용될 수 있다.

This study examines the effects of general characteristics and health behaviors on the quality of life among Generation X, and highlights the need for tailored health promotion strategies that consider intra-age-group heterogeneity. Using data from the 2024 Korea Community Health Survey, a total of 66,307 individuals aged 46–54 (n=34,103) and 55–61 (n=32,294) were analyzed. Quality of life was measured using the EQ-5D index, and complex sample multiple regression analysis was conducted using SPSS 25.0. The results showed that, in both age groups, household income, education level, economic activity, drinking frequency, perceived stress, PHQ-9 scores, and diagnoses of hypertension and diabetes significantly affected quality of life. Additionally, being male was a significant predicting factor in the 46–54 age group, while having a spouse, engaging in vigorous physical activity, and being in the pre-obese BMI range were significant predicting factors in the 55–61 age group. These findings underscore the importance of developing differentiated health policies that reflect the characteristics of pre-elderly Generation X, and suggest that this study may serve as a foundational reference for future life cycle–based health promotion strategies.

Since solar energy can be harvested periodically, using solar power in wireless sensor networks (WSNs) requires a different approach to energy consumption from typical battery-based WSNs. Meanwhile, it is also challenging to supply enough energy required for heavy operations such as video data encoding and transferring in battery-based WSNs. Therefore, we address the problem of determining the quality of encoding sensory data on the solar-powered sensor node. Based on a simple energy model of the solar-powered node, proposed scheme controls the quality of encoding data adaptively in the way of using the harvested energy maximally. Experimental results demonstrate the efficiency of our scheme.

본 연구에서는 현장에서 수행된 콘크리트 품질관리 시험 데이터를 이용하여 압축강도의 통계적 특성과 품질 신뢰성을 평가하였다. 분석 데이터는 총 172개의 압축강도 시험 결과로 구성되며, 21 MPa, 24 MPa, 27 MPa, 30 MPa 및 35 MPa의 호칭강도 등급을 포함한다. 분석 결과, 압축강도는 전반적으로 정규분포에 가까운 분포를 나타냈으며, 평균 압축강도는 호칭강도보다 높은 수준으로 나타났다. 강도비 분석 결과 평균값은 약 1.163으로 나타났으며, 대부분의 데이터는 1.05~1.25 범위에 분포하였다. 또한 강도비가 1.0 미만으로 나타날 확률은 약 1.7 % 수준으로 평가되었다. 특히 강도 등급이 증가할수록 강도비는 감소하는 경향을 나타내어, 고강도 콘크리트일수록 설계 목표에 근접한 수준으로 관리되는 경향을 보였다. 따라서 본 연구에서 적용한 통계적 분석 절차는 현장 품질관리 데이터를 기반으로 압축강도의 변동 특성, 강도 여유 및 강도 미달 가능성을 정량적으로 평가할 수 있다는 점에서 의의가 있으며, 향후 현장 콘크리트 품질 평가 및 신뢰성 기반 관리 기준 검토를 위한 기초자료로 활용될 수 있을 것으로 기대된다.

This study evaluates the statistical characteristics and quality reliability of concrete compressive strength using field quality control data obtained from construction sites. A total of 172 compressive strength test results were analyzed, covering nominal strength grades of 21, 24, 27, 30, and 35 MPa. The results indicate that the compressive strength approximately follows a normal distribution, with the average strength exceeding the nominal strength. The strength ratio showed a mean value of approximately 1.163, and most values were distributed within the range of 1.05~1.25. The probability of the strength ratio falling below 1.0 was estimated to be approximately 1.7%. In addition, the strength ratio tends to decrease as the strength grade increases, indicating that higher-strength concrete is managed closer to the target strength level. Therefore, the statistical evaluation procedure applied in this study is meaningful in that it quantitatively assesses strength variability, strength margin, and the probability of strength deficiency based on field quality control data. The results of this study are expected to provide fundamental information for field concrete quality evaluation and the development of reliability-based quality management criteria.

기후 자료를 정확히 분석하고 미래 환경을 예측하기 위해서는 이들 자료에 대한 검증과 품질 관리 과정이 필요하다. 본 연구에서는 우리나라에서 구축한 기후 자료의 품질을 효과적으로 관리하기 위하여, 기후 자료의 시각화 도구를 개발하고, 이를 이용한 품질 관리 방안을 제시하였다. 본 연구에서 개발한 HTML5 기반의 시각화 도구를 이용하여, 인터넷 환경에서 다수의 전문가들이 효과적으로 기후 자료를 관리할 수 있게 되었다. 기후자료의 시각화 도구를 활용하여 기후자료의 품질 관리 업무에 적용한 결과, 다양한 종류의 품질 관리 검사를 효과적으로 수행할 수 있었다. 본 연구의 결과는 향후 기상청의 기후 자료 품질 관리 업무에 활용될 수 있을 것으로 예상된다.

It is required to investigate climate data and control their quality for the accurate analysis of the data and the prediction of future climate environment. Therefore, this research suggested the efficient quality assurance/quality control (QA/QC) approach of climate data through the development of geovisualization tools based on HTML5. The proposed QA/QC method was found to allow multiple experts to examine the quality of climate data in an Internet environment. In addition, the various types of distinct QA/QCs could be achieved by applying the geovisualization tools for the management tasks of climate data. It is anticipated that the proposed approach of the research would be potentially utilized by the Korea Meteorological Administration in controlling the quality of climate data.

OBJECTIVES: The aim of effective data quality control and management is to minimize the impact of errors on study results by identifying and correcting them. This study presents the results of a data quality control system for the Korea HIV/AIDS Cohort Study that took into account the characteristics of the data. METHODS: The HIV/AIDS Cohort Study in Korea conducts repeated measurements every 6 months using an electronic survey administered to voluntarily consenting participants and collects data from 21 hospitals. In total, 5,795 sets of data from 1,442 participants were collected from the first investigation in 2006 to 2016. The data refining results of 2015 and 2019 were converted into the data refining rate and compared. RESULTS: The quality control system involved 3 steps at different points in the process, and each step contributed to data quality management and results. By improving data quality control in the pre-phase and the data collection phase, the estimated error value in 2019 was 1,803, reflecting a 53.9% reduction from 2015. Due to improvements in the stage after data collection, the data refining rate was 92.7% in 2019, a 24.21%p increase from 2015. CONCLUSIONS: Despite this quality management strategy, errors may still exist at each stage. Logically possible errors for the post-review refining of downloaded data should be actively identified with appropriate consideration of the purpose and epidemiological characteristics of the study data. To improve data quality and reliability, data management strategies should be systematically implemented.