• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.547-549
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• 2021

In Artificial Intelligence analysis, the process of creating a model and verifying it is a task that requires computational processing time because it is Batch Processing performed with already generated data. We need to model, validate, and predict real-time data, such as stocks and defense information, with data generated directly in front of us. As a solution to this, we solve it by applying techniques to segment the data required for artificial intelligence modeling tasks in order of time processing and distribute the data across multiple processes.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1037-1044
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• 2020

Since radar wind profiler (RWP) provides wind field data with high time and space resolution in all weather conditions, their verification of the accuracy and quality is essential. The simultaneous wind measurement from rawinsonde is commonly used to evaluate wind vectors from RWP. In this study, the simulation algorithm which produces the spectrum and raw time series (I/Q) data from the average values of moments is presented as a step-by-step verification method for the signal processing algorithm. The possibility of the simulation algorithm was also confirmed through comparison with the raw data of LAP-3000. The Doppler power spectrum was generated by assuming the density function of the skew-normal distribution and by using the moment values as the parameter. The simulated spectrum was generated through random numbers. In addition, the coherent averaged I/Q data was generated by random phase and inverse discrete Fourier transform, and raw I/Q data was generated through the Dirichlet distribution.

• The korean journal of orthodontics
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• v.49 no.1
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• pp.41-48
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• 2019

Objective: This in-vivo study aimed to compare landmark identification errors in anteroposterior (AP) and posteroanterior (PA) cephalograms generated from cone-beam computed tomography (CBCT) scan data in order to examine the feasibility of using AP cephalograms in clinical settings. Methods: AP and PA cephalograms were generated from CBCT scans obtained from 25 adults. Four experienced and four inexperienced examiners were selected depending on their experience levels in analyzing frontal cephalograms. They identified six cephalometric landmarks on AP and PA cephalograms. The errors incurred in positioning the cephalometric landmarks on the AP and PA cephalograms were calculated by using the straight-line distance and the horizontal and vertical components as parameters. Results: Comparison of the landmark identification errors in CBCT-generated frontal cephalograms revealed that landmark-dependent differences were greater than experienceor projection-dependent differences. Comparisons of landmark identification errors in the horizontal and vertical directions revealed larger errors in identification of the crista galli and anterior nasal spine in the vertical direction and the menton in the horizontal direction, in comparison with the other landmarks. Comparison of landmark identification errors between the AP and PA projections in CBCT-generated images revealed a slightly higher error rate in the AP projections, with no inter-examiner differences. Statistical testing of the differences in landmark identification errors between AP and PA cephalograms showed no statistically significant differences for all landmarks. Conclusions: The reproducibility of CBCT-generated AP cephalograms is comparable to that of PA cephalograms; therefore, AP cephalograms can be generated reliably from CBCT scan data in clinical settings.

• Journal of information and communication convergence engineering
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• v.1 no.3
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• pp.150-156
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• 2003

This paper explored the use of an algorithm for the data mining and method in handling missing data which had generated enhanced association patterns observed using the data illustrated here. The evaluations showed that more association patterns are generated in the second analysis which suggests more meaningful rules than in the first situation. It showed that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, strategies could be efficiently planned out and implemented to improve marketing schemes. This investigation gives rise to a number of interesting issues that could be explored further like the effect of outliers and missing data for detecting fraud and devious database entries.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.218-225
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• 2003

A diagnostic system has been developed which reports modeling errors generated when exchanging CAD data using IGES (Initial Graphics Exchange Specification) format. The system determines whether the CAD data contains errors. It also helps to define the criteria for determining the integrity and interoperability of CAD data with downstream applications of another CAD/CAM/CAE/PDM systems. The methodology of our algorithms is to analyze IGES model data by identifying errors and anomalies with respect to the diagnosis of geometry and topology. The GUI (Graphic User Interface) of the developed system helps users to input values and to visualize diagnostic results at real time.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.163-173
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• 2004

The objective of this study is to develop a method to generate micro-relief digital elevation model (DEM) data of the tidal mudflats using multi-temporal Landsat Thematic Mapper (TM) data. Field spectroscopy measurements showed that reflectance of the exposed mudflat, shallow turbid water, and normal coastal water varied by TM band wavelength. Two sets of DEM data of the inter-tidal mudflat area were generated by interpolating several waterlines extracted from multi-temporal TM data acquired at different sea levels. The waterline appearing in the near-infrared band was different from the one in the middle-infrared band. It was found that the waterline in TM band 4 image was the boundary between the shallow turbid water and normal coastal water and used as a second contour line having 50cm water depth in the study area. DEM data generated by using both TM bands 4 and 5 rendered more detailed topographic relief as compared to the one made by using TM band 5 alone.

• Design & Manufacturing
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• v.17 no.1
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.105-112
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• 1996

Softwares including random number generation are abundant in modern informative society. But it's hard to get directly multivariate multinomial random numbers from existing softwares. Multivariate multinomial random numbers are greatly used in social and medical sciences. In this paper, we show that desired multivariate multinomial random numbers can be easily generated by the aids of existing random number generating software. Some characteristics of multivariate multinomial distribution are surveyd. Measures of association for the generated random numbers were computed and compared with population ones via simulation study.

• Journal of the Korean Statistical Society
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• v.14 no.1
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• pp.7-17
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• 1985

Data set generated by teh bivariate dichotomous choice made by individuals often occurs in practice. This paper presents general model of how such data set is generated as well as methods of estimation. The M.L.E. is examined and found to be computationally burdensome. A simpler estimator, the bivariate dichotomous two-stage estimator, is suggested as an alternative. The two-stage estimator is found to be as efficient as the M.L.E.

• Journal of the Society of Disaster Information
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• v.4 no.1
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• pp.86-100
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• 2008

It is needed to classify the kinds of construction and demolition(c&d) debris to 6 catagories of waste concrete, waste asphalt concrete, waste wood, scraps, combustible waste and incombustible waste in order to properly do a separate discharge and to estimate unit generation rate in construction site. Also, in this case, the unit treating cost for mixed wastes should be applied with the unit treating cost for combustible waste. The construction standard materials estimation data is used for basic data for estimating unit generation rate. The mixed wastes in this data should be classified to waste wood, combustible waste and incombustible waste, and their ratio is obtained by using the unit generation rate of Asia Pacific Environment and Management Institute and Seoul Metropolitan Development Institute. The waste amounts generated from newly-built construction can be obtained from multiplying the loss rate by the amount of materials used from construction standard estimation data. Also, those from dismantling construction can be obtained by subtracting waste amount generated during newly-built construction from total input amount of materials in newly-built construction. Those in two cases can be used in construction site. It can be used for estimating the amount generated and establishing the treating plan in the case of setting up the policy of waste management and doing the environment impact assessment.