• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.499-510
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• 2015

Purpose: This paper treats the improvement of the quality and reliability of military weapon systems based on the maintenance data. Methods: The proposed method of the data integration and refinement are used to obtain the component reliability information and to find the frequently failed components based on the Pareto analysis. Based on the reliability information and the number of failed component frequencies, the target components of quality improvement are determined and improved by multiple methods such as engineering changes, special meetings, additional training and revising maintenance manuals. Results: Based on the proposed process, we find some components which need to be improved in order to enhance the quality and reliability. Conclusion: A process is developed for improving the quality and reliability of weapon systems. This process will be adopted by various weapon systems to enhance the quality and reliability, as well as reduce military spending.

• 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.

• 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.

• 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.

• 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.

• 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.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.89-95
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• 2008

In this paper, we propose an integrated techniques for managing data quality in traffic data warehousing environments. We describe how to collect and construct the traffic data warehouses from the operational databases, such as FTMS and ARTIS. We explain how to configure the traffic data warehouses efficiently. Also, we propose a quality management techniques to provide high quality traffic data for various analytical transactions. Proposed techniques can contribute in providing high quality traffic data to the traffic related users and researcher, thus reducing data preprocessing and evaluation cost.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.439-447
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• 2020

Public data refers to all data or information created by public institutions, and public information that leads to communication and cooperation among all people. Public data is an important method to lead the next generation of new industries such as artificial intelligence and smart cities, Korea is continuously ranked high in the international evaluation related to public data. However, despite the continuous efforts, the use of public data or industrial influence is insufficient. Quality issues are continuously discussed in the use of public data, but the criteria for quantitatively evaluating data are insufficient. This paper reviews indicators for public data quality evaluation and performs quantitative evaluation on selected public data. In particular, the quality of open standard data constructed and opened based on public data management guidelines is examined to determine whether government guidelines are appropriate. The data quality assessment includes the metadata and data values of open standard data, and is reviewed based on completeness and accuracy indicators. Based on the data analysis results, this paper proposes policy and technical measures for quality improvement.

• 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.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.582-587
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• 2007

ERP system is a good one because it provides required data to the Board of Directors at the right time, but needs to collect many data in this system. Nevertheless, increase in data leads to the system's quality deterioration which makes companies to carry out quality improvement. In order to solve quality deterioration problem, a company's quality improvement director must execute under acknowledgement of the relationships between sectors to be improved, which are DBMS, Application, System, Data Management, Archiving, and Reorganization. But in many cases, these relationships are ignored due to massive size of each of the sectors, resulting fragmental quality improvement operation. This case paper proposes a solution to effectively solve quality deterioration problem created by the massive data produced while operating ERP System(constructed by SAP package and web). First, it defines the sectors where quality improvements are vital, and lists out things to be considered. Then, by analysing the working process of these sectors, proposes the most efficient order of the improvement process. This case will eventually help the company's quality improvement director to execute quality improvement most effectively without trials and errors, which is this paper's ultimate goal.