• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.333-339
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• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.520-538
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• 2019

In this study, it was proposed that a method of setting the target water quality for TOC using the watershed model and the load duration curves to manage non-biodegradable organics in the total water load management system. To simulate runoff and water quality of the watershed, the HSPF model is used which is appropriate for urban and rural areas. Additionally, the load duration curve is used to reflect the variable water quality correlated with various river flow rates in preparing the TMDL plans in the U.S. First, the model was constructed by inputting the loads calculated from the pollutant sources in 2015. After the calibration and verification process, the water quality by flow conditions was analyzed from the BOD and TOC simulation results. When the BOD achieved the target water quality by inputting the target year loads for 2020, the median and average values of TOC were proposed for the target water quality. The provisional method of TOC target water quality for the management of non-biodegradable organics, which is one of the challenges of the total water load management system, was considered. In the future, it is expected to be used as basic data for the conversion of BOD into TOC in the total water load management system.

• Journal of Korean Society for Quality Management
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• v.39 no.4
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• pp.464-479
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• 2011

The purpose of this study is that analyzing the causal relationship between Infra, Process and Performance of companies which are executing the Supply Chain Quality Management(SCQM) with their subcontractors and partners. Korean Standards Association(KSA) provides the Supply Chain Quality Management Model and Quality Collaboration Index for 4 years, but a few study has investigated the critical variables and their causal relationship to organizational performance. Therefore we examine the SCQM model and related index and choose the quality, human resource and risk management processes for identifying the path to organizational performance. In addition, exploratory factor analysis is conducted for figuring out the major factors among the 3 processes. Structural Equation Model are successively used for determining which characteristics of the infra and processes are the most critical variables to performance. The data was collected from KSA and composed of 52 companies and 346 their partners. The result shows that risk management process has no significant effect on the organizational performance and pre-production process collaboration.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.563-567
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• 2004

During drought season, the self-purification capacities of the four major rivers in Korea are significantly controlled by environmental maintenance flows supplied from the mid- or upstream large dams. Therefore, it is obviously important to operate the dams considering not only water quantity aspects but also conservation of downstream water quality and aquatic ecosystems. Mathematical water quality models can be efficiently used to serve as a decision support tool for evaluating the effects of operational alternatives of upstream dams on the downstream aquatic environment. In this study, an unsteady one-dimensional water quality model, KORIV1-WIN was developed based on the theoretical and numerical algorithms for hydrodynamics and water quality simulations of CE-QUAL-RIV1. It consists of hydrodynamic(KORIV1H) and water quality(KORIV1Q) modules, and pre- and post-processors for input data preparations and output displays. The model can be used to predict one-dimensional hydraulic and water quality variations in rivers with highly unsteady flows such as dam outflow change, rainfall-runoff, and chemical spill events.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1168-1176
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• 2024

For reusable Temporary Structure Elements such as scaffolding and temporary supports, quality control tasks are currently carried out through visual inspections by quality management workers and subjective judgments. Regarding quality tests based on the KOSHA(Korea Occupational Safety & Health Agency) system, only three pieces are sampled regardless of the quantity received at the site. On the other hand, although there is ongoing technological research on an automatic quality inspection of temporary structure elements, relevant stakeholders' introduction of such systems is hindered by issues such as cost. Therefore, this study aims to review a business model for introducing a quantitative and automated quality inspection system for reusable temporary structure elements. The study intends to propose application methods for each component according to a template and establish the business model by conducting interviews and collecting basic data for each template component. The results of this research are expected to serve as a foundation for implementation and expanding the adoption of quality management for temporary structure elements using smart technologies in the future.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1997-2004
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• 1994

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.157-168
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• 2024

The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.135-150
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• 2018

The biological resources research data around the world are not only very critical themselves but should be shared and utilized. Up to now, the biological resources have been compiled and managed individually depending on the purpose and characteristics of the study without any clear standard. So, in this study, the data reference model would be suggested which is applicable in the phase ranging from the start of the construction of the information system and which can be commonly used. For this purpose, the data model of the related information system would be expanded based on the domestic and foreign standards and data control policy so that the data reference model which can be commonly applicable to individual information system would be developed and its application procedure would be suggested. In addition, for the purpose of proving the excellence of the suggested data reference model, the quality level would be verified by applying the Korgstie's data model evaluation model and its level of data sharing with the domestic and foreign standards would be compared. The test results of this model showed that this model is better than the conventional data model in classifying the data into 4 levels of resources, target, activities and performances and that it has higher quality and sharing level of data in the data reference model which defines the derivation and relation of entity.