• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.869-876
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• 2013

In order to estimate the fatigue life of mufflers at the early stage of researches and designs, the new prediction process was developed by the artificial neural network, which has the algorism of weldment properties. Bending fatigue test was carried out for defining the characteristics of muffler weldment fatigue life and damage. For considering and predicting mechanical and fatigue properties of the muffler, the maximum stress of weldment was adapted as the variable of artificial neural network training. Also, it was compared with the fatigue life predicting results using fatigue notch factors, for proving the newly developed process of the artificial neural network.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.69-70
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• 2020

Accidents in the construction industry are very high compared to other industries, and the number is also increasing steeply every year. Relevant studies were limited for solving the problems. The purpose of this study is to develop a comprehensive risk prediction process for personnel deployed at construction sites on safety management. First of all, the variables were divided into fixed, real-time and working types variables, and the relevant comprehensive data were collected. Second, the probability of a disaster was derived based on the collected data, and weights for each variable were calculated using the dummy regression analysis method using statistical methodology. Lastly, the resulting weighting and disaster probability equation was constructed, and The Final Risk Calculation Formula was developed. The Final Risk Calculation Formula presented in this study is expected to have a significant impact on the establishment of effective safety management measures to prevent possible safety accidents at construction sites

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.30-34
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• 2019

We analyzed how the features in plasma information based virtual metrology (PI-VM) for SiO₂ etching depth with variation of 5% contribute to the prediction accuracy, which is previously developed by Jang. As a single feature, the explanatory power to the process results is in the order of plasma information about electron energy distribution function (PI_EEDF), equipment, and optical emission spectroscopy (OES) features. In the procedure of stepwise variable selection (SVS), OES features are selected after PI_EEDF. Informative vector for developed PI-VM also shows relatively high correlation between OES features and etching depth. This is because the reaction rate of each chemical species that governs the etching depth can be sensitively monitored when OES features are used with PI_EEDF. Securing PI_EEDF is important for the development of virtual metrology (VM) for prediction of process results. The role of PI_EEDF as an independent feature and the ability to monitor variation of plasma thermal state can make other features in the procedure of SVS more sensitive to the process results. It is expected that fault detection and classification (FDC) can be effectively developed by using the PI-VM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.100-103
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• 2008

In this paper, we propose H.264 Encoder integrating Intra Prediction, Deblocking filter, Context-Based Adaptive Variable Length Coding, and Motion Estimation encoder module. This designed module can be operated in 440 cycle for one-macroblock. To verify the Encoder architecture, we developed the reference C from JM 9.4 and verified the our developed hardware using test vector generated by reference C. The designed circuit can be operated in 166MHz clock system, and has 1800k gate counts using Charterd 0.18um process including SRAM memory. Manufactured chip has the size of $6{\times}6mm$ and 208 pins package.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.8-14
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• 2022

In this study reports experiments were conducted to determine the quality of weld beads of different materials, Al and Cu. Among the lasers used to make battery cells for electric vehicles, non-destructive testing was performed using deep learning to determine the quality of beads welded with the ARM laser. Deep learning was performed using AlexNet algorithm with a convolutional neural network structure. The results of quality identification were divided into good and bad, and the result value was derived that all the results were in agreement with 94% or more. Overall, the best welding quality was obtained in the experiment for the fixed ring beam output/variable center beam output, in the case of the fixed beam (ring beam) 500W and variable beam (center beam) 1,050W; weld bead failure was seldom observed. The tensile force test to confirm the reliability of welding reported an average tensile force of 2.5kgf/mm or more in all sections.

• The Journal of Information Systems
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• v.16 no.3
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• Environmental Analysis Health and Toxicology
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• v.30
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• pp.10.1-10.14
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• 2015

Recent cohort studies have relied on exposure prediction models to estimate individual-level air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.7 no.1
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• pp.1-12
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• 1995

In order to seek various relationships among many design parameters employed in the design of the axial-flow fans the program which generates acoustic spectrum has been developed and its validity verified. Outputs of the program, with other outputs from a formerly developed performance prediction program, have been used to form a multi-objective function, for which an optimal design process was carried out. The present analysis shows that overall noise level and efficiency has contrasting trends, and the chord length turns out to be the most critical design variable. In the chosen design case of requirements $Q=2000m^2/min$, ${\Delta}P_s=67mmAq$, D=1.4m, the chord length of 0.2059m minimizes the overall noise level, while chord length of 0.1254m maximizes the efficiency. The resulting chord length in the balanced optimization is 0.1809m.