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

• Journal of Korean Society for Quality Management
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• v.35 no.3
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• pp.21-36
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• 2007

This article is to analyze the service efficiency of public sector using Data Envelopment Analysis(DEA). We tried to measure the public service quality and overall satisfaction by using several DEA models, degree of combination and top2box which is a little bit different methodology from traditional ones. We used CCR, Super-efficiency and Slack based measure(SBM) model in DEA to measure public service efficiency of the 16 public institutions(7 City Halls and 9 Provincial offices). Since the traditional method based on the measurement model(CCR, BCC) has the fundamental problems without taking account of the existence of slacks inputs and outputs in several efficient units, we suggest to measure the exact amount of efficiency and inefficiency of the public sector and rank analysis many efficient units exactly through the slacks-based measure model(SBM)

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.312-320
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• 2024

Healthcare services converged with ICT technology are improving quality of life and satisfaction through various customized services. In ICT-based medical services, data interchange between medical services is important, and HL7 FHIR, a medical data standard, enables efficient medical data interchange. FHIR-based medical information services using wireless data broadcasting can efficiently support massive clients. This paper proposes a function point model to evaluate the implementation cost of FHIR-based health information services using wireless data broadcasting. The proposed cost evaluation model can effectively evaluate the development cost by applying the complexity of converting medical data into FHIR format and the complexity of organizing indexes to efficiently support massive clients. The comparison of the proposed feature point evaluation model with simple feature points shows the efficiency and suitability of the proposed cost evaluation model.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.274-283
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• 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
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• v.4 no.3
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• pp.372-381
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• 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
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• v.8 no.12
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• pp.377-389
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• 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
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• v.21 no.2
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• pp.133-137
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• 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 The Korean Association of Information Education
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• v.2 no.1
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• pp.35-51
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• 1998

Software reliability growth model is one of the evaluation methods, software quality which quantitatively calculates the software reliability based on the number of errors detected. For correct and precise evaluation of reliability of certain software, the reliability model, which is considered to fit dose to real data should be selected as well. In this paper, the optimal model for specific test data was selected one of among five software reliability growth models based on NHPP(Non Homogeneous Poission Process), and in result reliability estimating scales(total expected number of errors, error detection rate, expected number of errors remaining in the software, reliability etc) could obtained. According to reliability estimating scales obtained, Software development and predicting optimal release point and finally in conducting systematic project management.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.59-78
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• 1994

In some observational studies, we have often random censoring model. However, the data available may be partially observable censored data consisting of the observed failure times and only those nonfailure times which are subject to follow up. In this paper, we present an extension of the problem of partially parametric estimation of the survival function to such partially observable censored data. The proposed estimator treats the observed failure times nonparametrically and uses a parametric model only for those nonfailure times which are subject to follow-up. We discuss the motivation and construction of the proposed estimator and investigate the limiting properties of the proposed estimator such as asymptotic normality. Also, when the assumed parametric model is exponential, the asymptotic variance of the estimator is obtained. Furthermore, an example is given to compare the proposed estimator with the modified Kaplan Meier(MKM) estimator. From the results, it is shown that the relative efficiency of the proposed estimator is higher than that of the MKM estimator in the follow-up study with increasing time.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.