• Title/Summary/Keyword: Quality of Data

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A Relative Atomspheric Correction Methods for Water Quality Factors Extraction from Landsat TM data (Landsat TM data로부터 수질인자 추출을 위한 상대적 대기 보정 방법)

  • Yang, In-Tae;Kim, Eung-Nam;Choi, Youn-Kwan
    • Journal of Industrial Technology
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    • v.18
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    • pp.17-25
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    • 1998
  • Recently, there are a lot of studies to use a satellite image data in order to investigate a simultaneous change of a wide range area as a lake. However, many cases of a water quality research occur as problem when we try to extract the water quality factors from the satellite image data, because of the atmosphere scattering exert as bad influence on a result of analysis. In this study, and attempt was made to select the relative atmospheric correction method for the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors extraction from the satellite image data. And also, the time-series analysis of the water quality factors was performed by using the multi-temporal image data.

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Factors Affecting LOSA Data Quality (LOSA Data 품질(Quality)에 영향을 미치는 요소)

  • Kyoung Ho Lee;Jang Ryong Lee
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.31 no.2
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    • pp.72-80
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    • 2023
  • Line Operations Safety Audit (LOSA) is a well known preventive aviation safety program for Threat and Error management (TEM). High quality LOSA data suitable for safety management is obtained when a flight crew flies at the same level of attention as ordinary flight. Factors contributing to LOSA data quality may include flight crew's understanding on LOSA purpose, observer's career, and characteristics of the organization responsible for LOSA operations. This study explored purposes of TEM and LOSA, as well as their relationship. Previous studies mentioned quality of LOSA data can be influenced by heuristic judgment, hawthorne effect, and priming effect. This study recognized the importance of LOSA data quality to be effectively used for preventive safety management. It was confirmed that the level of understanding on LOSA concept, experience of the observer, and the characteristics of the department in charge of LOSA operation could affect the quality of LOSA data.

Real Time Water Quality Forecasting at Dalchun Using Nonlinear Stochastic Model (추계학적 비선형 모형을 이용한 달천의 실시간 수질예측)

  • Yeon, In-sung;Cho, Yong-jin;Kim, Geon-heung
    • Journal of Korean Society of Water and Wastewater
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    • v.19 no.6
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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.

The Data Quality Management Framework and it's Business Scenario (데이터 품질관리 프레임워크와 비즈니스 시나리오)

  • Lee, Chang-Soo;Kim, Sun-Ho
    • The Journal of Society for e-Business Studies
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    • v.15 no.4
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    • pp.79-99
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    • 2010
  • As data exchange between business partners in e-business becomes more active, obtaining and managing reliable data is emerging as a pressing issue for corporations and organizations. For the resolution of data quality, this paper proposes a framework for data quality management with its scenario. The data quality management framework consists of three phases: data quality monitoring, data quality improvement and data application, each of which has three processes. In each process, necessity, functions, roles, and relationships among processes are specified. In order for users to directly apply the framework to the business field, a business scenario is given with examples of product identification and classification code systems widely used in e-business.

Review and Discussion on Development of Soil Quality Indicators (토양의 질 지표 개발 동향과 논의)

  • Yoon, Jung-Hui
    • Korean Journal of Soil Science and Fertilizer
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    • v.37 no.3
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    • pp.192-198
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    • 2004
  • The heavy dependence of modern science-based agriculture on chemicals such as fertilizers and pesticides, and heavy machinery gave rise to questions about long-term sustainability of agriculture in relation to degradation of soil quality. The research achievements and trends in developing soil quality indicators were introduced and discussed in this report. Organization for Economic Cooperation and Development (OECD) established 13 agri-environment indicators including soil quality indicator in 1994, collected the soil data and methodologies for development of soil quality indicators in OECD member countries responded to OECD questionnaires and published the OECD reports, Environmental Indicators for Agriculture Volume 1, 2, and 3. Leading countries such as USA, Canada and New Zealand collected national data and evaluated the data in aspect of soil quality. They developed the various methods for selecting a minimum data set (MDS), scoring the soil properties and calculating soil quality index integrating the score of each soil property.

A study on the Development of BIM-based Quality Pre-checking System in Architecture Design Phase

  • Shin, Jihye;Choi, Jungsik;Kim, Inhan
    • International conference on construction engineering and project management
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    • 2015.10a
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    • pp.284-288
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    • 2015
  • Recently, the mandate on utilizing BIM implemented by public institutions of many countries has great impact on the significantly increasing practices of BIM. The improvement of work efficiency and productivity, which is occurred by BIM adoption, depends on the consistency and accuracy of data. To maximize the benefit of BIM, the interests in BIM data quality have been enlarging all over the world. The BIM data quality pre-check, which is conducted by designer in the design phase, offers opportunities for quality improvement by continuously assessing BIM data. However, BIM quality pre-check is being conducted under arbitrary interpretation of users because of the absence of specific review factors and assessment methods for checking BIM quality. The purpose of this study is to establish an automated BIM quality pre-checking system to improve BIM design quality effectively and efficiently. It could be expected to meet the owner's requirements and to minimize the cost and time occurred additionally from revising and reproducing data by constructing consistency and accuracy of it.

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DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK MODELS SUPPORTING RESERVOIR OPERATION FOR THE CONTROL OF DOWNSTREAM WATER QUALITY

  • Chung, Se-Woong;Kim, Ju-Hwan
    • Water Engineering Research
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    • v.3 no.2
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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.

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A Review on the Quality Control of Marine Fish Data (해양어류 자료의 정도관리에 대한 고찰)

  • LEE, HWAHYUN;SOHN, DONGWHA;KIM, SUAM
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.26 no.3
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    • pp.277-289
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    • 2021
  • Among various data types obtained from the ocean, the quality controls for abiotic data collected from chemical, physical, and geological field surveys haves already been partially established. Due to the difficulties in standardization of the data collections and basic analyses, however, the quality controls of biotic data are in its early stage. For marine fish, the necessity of quality control is more demanded due to the wide range of data usage, but there are currently no consistent quality control guidelines because of the diversity and scope of data types derived from species-specific and age-specific information throughout various habitats. In this paper, we provide examples of marine fish data utilization and also show methods of the marine fish data collection, limitations of the data collection methods, and suggestions for improving the marine fish data quality. We hope this paper will help to establish the direction of quality control for marine fish data from both fishery-dependent and fishery-independent surveys in Korea in the near future.

Analysis of Healthcare Quality Indicators using Data Mining and Development of a Decision Support System (데이터마이닝을 이용한 의료의 질 측정지표 분석 및 의사결정지원시스템 개발)

  • Kim, Hye Sook;Chae, Young-Moon;Tark, Kwan-Chul;Park, Hyun-Ju;Ho, Seung-Hee
    • Quality Improvement in Health Care
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    • v.8 no.2
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    • pp.186-207
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    • 2001
  • Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

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