• 대한간호학회지
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• 제28권2호
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• pp.344-357
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• 1998

This study was designed to construct a model that predicts the quality of life of family caregivers with a chronically ill patient. The hypothetical model was developed based on the findings from past studies on quality of life and on the family with a chronically ill patient. Data were collected by self-reported questionnaires from 200 family caregivers in Seoul & Kyung Gi-Do, from May 1 to July 21, 1997. Data were analyzed using descriptive statistics and correlation analysis. The Linear Structural Relationship(LISREL) modeling process was used to find the best fit model which predicts causal relationships among variables. The results are as follows : 1. The overall fit of the hypothetical model to the data was moderate [X$^2$=31.54(df=23, p=.11), GFI=.96, AGFI=.91, RMR=.04]. 2. Paths of the model were modified by considering both its theoretical implication and the statistical significance of the parameter estimates. Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data expect chi-square value(GFI=.95, AGFI=.91, RMR=.04). 3. Some of predictive factors, especially economic status, physical ability to perform daily-life activity, period after disease-onset, social support and fatigue revealed indirect effects on the quality of life of family caregivers with a chronically ill patient. 4. The factors, burden and role satisfaction revealed significant direct effects on the quality of life of family caregivers with a chronically ill patient. 5. All predictive variables of quality of life of family caregivers with a chronically ill patient, especially economic status, physical ability to perform daily-life activity, period after disease-onset, social support, fatigue, burden and role satisfaction explained 38.0% of the total variance in the model. In conclusion, the derived model in this study is considered appropriate in explaining and predicting quality of life of family caregivers with a chronically ill patient. Therefore it can effectively be used as a reference model for further studies and suggests direction in nursing practice.

• 한국IT서비스학회지
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• 제14권2호
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• pp.51-68
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• 2015

As interests in the quality of data in database systems are growing recently, analysis and improvement of data quality in databases have been an important issue. However, there has yet to be a clear agreement on how to reasonably calculate the total cost of such project. In this paper, based on real project data and budget statistics, we develop a model to estimate the cost for quality analysis and improvement project of a database. We first conduct statistical analysis to build our basic model. Throughout this analysis, we have identified factors that determine the scale of works required to conduct the project and eventually determine the cost. In addition, we have identified factors that determine the complexity of the project. These factors can adjusts the cost determined by the scale of works. Our model is verified and improved by surveys on experts. We apply our model to newly conducted projects and observe that our model estimates the cost of each project reasonably well.

• 정보화정책
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• 제24권1호
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• pp.30-47
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• 2017

정부는 공공데이터의 품질관리 수준을 평가하기 위해 국제표준을 기반으로 공공데이터 품질관리 조직 성숙도 모델을 개발하였다. 그러나 현장에 적용하기에는 평가항목이 너무 많다는 지적에 따라 평가지표 수를 축소한 새로운 모델을 보완 개발하였다. 이를 위하여 프로세스를 통합 및 조정하여 프로세스 수를 축소하였으며 프로세스능력수준이 아닌 새로운 활동능력수준 기반의 평가 방식을 제안하였다. 또한, 공공데이터 품질관리 성숙수준을 다섯 개의 레벨로 표현하는 방식과 1~5 사이의 실수로 표현하는 방식을 제안하였다. 그리고 새로 제안한 모델의 특성을 기존의 조직 성숙도 모델과 비교 분석하였다.

• 상하수도학회지
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• 제19권6호
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• 한국항공우주학회지
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• 제41권9호
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• pp.689-699
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• 2013

전리층 폭풍 시 발생할 수 있는 극심한 전리층 이상현상은 GNSS 보강시스템 사용자의 안전을 위협하는 대표적인 요인이므로 전리층 위협모델을 기반으로 한 지상 모니터링을 통해 적시에 감지 및 경보가 이루어 져야한다. GNSS 관측 데이터를 기반으로 전리층 분석을 수행하고 그 결과로 위협모델을 개발하기 때문에 각 관측소의 데이터 품질은 시스템 성능에 큰 영향을 미칠 수 있다. 전 세계적으로 GNSS 상시관측소 수가 많이 증가함에 따라 품질이 떨어지는 데이터를 산출하는 관측소 또한 증가하였다. 본 연구에서는 GNSS 데이터 품질평가 기법 이용하여 국내 GPS 상시관측소 데이터의 품질을 비교하고 품질이 떨어지는 데이터가 전리층 지연오차 및 기울기 추정치에 미치는 영향을 분석하였다. 품질평가 결과 국내 상시관측소간 데이터 품질에 큰 차이를 보였고 이 품질은 일정기간 유지된다는 것을 확인하였다. 본 연구에서 분석한 결과를 바탕으로 전리층 위협모델 개발을 위한 GNSS 데이터 품질 기준을 제시할 수 있다.

• 한국기록관리학회지
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• 제11권1호
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• pp.231-252
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• 2011

이 논문은 문화유산기관 아카이브 포탈의 대중화와 지속적인 이용을 위해 퀄리티 포탈에 필요한 요소들을 살펴보았다. 이용자에 대한 이해가 문화유산기관 아카이브 포탈의 성공에 결정적임에도 불구하고 많은 연구가 되어오지 않은 점을 감안하여, 타 포탈들의 퀄리티 분석에 적용된 이용자 중심 평가모델들을 정리하였다. Intrinsic bias를 피하기 위해 triangulation 방법론을 선택하였으며, 이에 서비스 품질(Service Quality), 데이타 품질(Data Quality), 그리고 기술수용모델(Technology Acceptance Model: TAM)을 분석 정리하여 문화유산기관의 아카이브 포탈에 적합한 평가모델을 제시하였다. 제시된 평가모델을 검증하기 위한 99 항목 설문도 제안하였다. 후속 연구에서는 설문의 타당성과 평가모델의 적합성이 실증적으로 검증될 계획이다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.63-69
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• 1999

The purpose of this study is to develop Water Quality Management System(WQMS), which performs calculating pollutant discharge and forecasting water quality with water pollution model. Operational water quality management requires not only controlling pollutants but acquiring and managing exact information. A GIS software, ArcView was used to enter or edit geographic data and attribute data, and MapObject was used to customize the user interface. PCI, a remote sensing software, was used for deriving land cover classification from 20 m resolution SPOT data by image processing. WQMS has two subsystems, Database Subsystem and Modelling subsystem. Database subsystem consisted of watershed data from digital map, remote sensing data, government reports, census data and so on. Modelling subsystem consisted of NSPLM(NonStorm Pollutant Load Model)-SPLM(Storm Pollutant Load Model). It calculates the amount of pollutant and predicts water quality. This two subsystem was connected through graphic display module. This system has been calibrated and verified by applying to Mokhyun stream watershed.

• Water Engineering Research
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• 제3권2호
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• 상하수도학회지
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• 제38권1호
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• Journal of Information Technology Applications and Management
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• 제11권4호
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• pp.209-225
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• 2004

This paper presents case studies on database Quality uSing an extended database Quality model developed by Korea Database Promotion Center, which shall be called KDPC2003. The model is applied to evaluate two kinds of databases ; one is an operational database, the other is an information service database. The purpose of this research is two-folded. One is to evaluate database quality and assess current status of database quality management; the other is to assess usefulness of KDPC2003 and to propose ideas for its augmentation. The findings in this study will provide basic facts to test database quality evaluation models.