• Agribusiness and Information Management
• /
• v.3 no.1
• /
• pp.11-22
• /
• 2011

This study describes the development of a web-based system that collects all data generated in the research conducted to set pre-harvest residue limits (PHRLs) for agricultural product safety control. These data, including concentrations of pesticide residues, limit of detection, limit of quantitation, recoveries, weather charts, and growth rates, are incorporated into a database, a regression analysis of the data is performed using statistical techniques, and the PHRL for an agricultural product is automatically computed. The development and establishment of this system increased the efficiency and improved the reliability of the research in this area by standardizing the data and maintaining its accuracy without temporal or spatial limitations. The system permits automatic computation of the PHRL and a quick review of the goodness of fit of the regression model. By building and analyzing a database, it also allows data accumulated over the last 10 years to be utilized.

• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.325-335
• /
• 2006

This paper evaluates and maps the quality of life in the Atlanta, Georgia metropolitan area in 2000. Three environmental variables from Landsat TM data, four socioeconomic variables from census data, and a hazard-related variable from toxic release inventory (TRI) database were integrated into a geographic information system (GIS) environment for the quality of life assessment. To solve the incompatibility problem in areal units among different data, the four socioeconomic variables aggregated by zonal units were spatially disaggregated into individual pixels. Principal components analysis (PCA) was employed to integrate and transform environmental, socioeconomic, and hazard-related variables into a resultant quality of life score for each pixel. Results indicate that the highest quality of life score was found around Sandy Springs, Roswell, Alphretta, and the northern parts of Fulton County along Georgia 400 whereas the lowest quality of life score was clustered around Smyma of Cobb County, the inner city of Atlanta, and Hartsfield-Jackson International Airport. The results also reveals that normalized difference vegetation index (NDVI) and relative risk from TRI facilities are two versatile indicators of environmental and socioeconomic quality of an urban area in the United States.

• IE interfaces
• /
• v.24 no.2
• /
• pp.128-138
• /
• 2011

This research proposes a resource allocation model of Data QC (Quality Control) activities using COQ (Cost of Quality). The model has been developed based on a series of research efforts such as COQ classifications, weight determination of Data QC activities, and an aggregation approach between COQ and Data QC activities. In the first stage of this research, COQ was divided into the four typical classifications (prevention costs, appraisal costs, internal failure costs and external failure costs) through the opinions from five professionals in Data QC. In the second stage, the weights of Data QC activities were elicited from the field professionals. An aggregation model between COQ and Data QC activities has been then proposed to help the practitioners make a resource allocation strategy. DEA (Data Envelopment Analysis) was utilized for locating efficient decision points. The proposed resource allocation model has been validated using the case of Korea national defense information system. This research is unique in that it applies the concept of COQ to the data management for the first time and that it demonstrates a possible contribution to a real world case for budget allocation of national defense information.

• Korean Journal of Computational Design and Engineering
• /
• v.10 no.4
• /
• pp.274-283
• /
• 2005

Product data quality (PDQ) is a real and significant issue in today's manufacturing environment. In the Korean automotive industry, much of the design work takes place with the support of software tools, such as CAD systems. Although many designers frequently encounter quality problems regarding product data, there is no investigation into the present state of CAD usage and PDQ activities before. The Korean automotive industry is responsible for about 11.1 percent of the total value of manufactured goods in Korea and 7.9 percent of employment in manufacturing. A study performed by the Research Triangle Institute showed that imperfect interoperability imposes at least $\$1$ billion per year on the members of the U.S. automotive supply chain. The trends toward concurrent engineering and out-sourcing have elevated the importance of high-quality product data and efficient product data exchange. This paper shows the results from a survey of PDQ conducted on seven 1st tiers among members of the Korean automotive supply chain.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.3
• /
• pp.372-381
• /
• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.12
• /
• pp.377-389
• /
• 2021

This study aims at investigating the effects of big accounting data on the sustainable business growth of listed firms in Thailand. In addition, it examines the mediating effects of accounting information quality and decision-making effectiveness and the moderating effects of digital innovation on the research relationships. The study's useful samples are the 289 listed Thai companies. To examine the research relationships, the structural equation model and multiple regression analysis are used in this study. According to the results of this study, big accounting data has a significant effect on accounting information quality, decision-making effectiveness, and sustainable business growth. Next, accounting information quality significantly affects decision-making effectiveness and sustainable business growth. Similarly, decision-making effectiveness significantly affects sustainable business growth. Both accounting information quality and decision-making effectiveness mediate the big accounting data-sustainable business growth relationships. Lastly, digital innovation moderates the effects of accounting information quality and decision-making effectiveness on sustainable business growth. Accordingly, In conclusion, big accounting data has emerged as a key source of sustainable competitive advantage. As a result, to succeed in competitive environments, businesses must have a thorough understanding of big accounting data.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.2
• /
• pp.133-137
• /
• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• Journal of Information Technology Applications and Management
• /
• v.11 no.4
• /
• pp.209-225
• /
• 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.

• Journal of Korean Society for Quality Management
• /
• v.29 no.1
• /
• pp.128-139
• /
• 2001

For small sized companies in Korea, it is crucial to have a systematic way of collecting and storing quality data, and extracting relevant information when necessary. This paper Is concerned with developing a quality information system which is mainly focused on processing quality inspection data, converting the data into meaningful quality information, and illustrating a case study of a small sized company in Gyeongnam province. First, we review the current quality Inspection system and reorganize a quality system which is more efficient than the current one. Second, incoming material, process, and product inspection modules are developed. Finally, a statistical process control module is integrated to see the manufacturing process behavior over time.

• Journal of Environmental Impact Assessment
• /
• v.15 no.1
• /
• pp.45-52
• /
• 2006

The purpose of this study is to assess spatiotemporal variation of coastal water quality according to time and location changes. For this we developed numerical marine trophic index base on four water quality components (chlorophyll, suspended solids, dissolved inorganic nitrogen and phosphorus) and applied this index to the water quality data measured in the korean coastal zone for the 7-years period from 1997 to 2003. Water quality data are obtained only at selected sites even though they are potentially available at any location. Therefore, in order to estimate spatial variation of coastal water quality, it is necessary to estimate the unknown values at unsampled locations based on observation data. In this study, we used IDW (Inverse Distance Weighted) method to predict water quality components at unmeasured locations and applied marine trophic index to predicted values obtained by IDW interpolation. The results of this study indicate that marine trophic index and spatial interpolation are useful for understanding spatiotemporal characteristics of coastal water quality.