• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.157-164
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future communication satellite, called 'Moogunghwa", which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical gravitation of the earth, gravitation of the sun and moon, radiation of sun, drag of the atmosphere was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using Extended Kalman Filter which is suitable for real-time orbit determination. The result shows that the chacteristics of the satellite orbit has east-west and south-north drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the positon and velocity error, and state error standard deviation is reasonable.able.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.304-309
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• 2006

Near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of fatty acid composition in sesame (Sesamum indicum L.) seed oil. A total of ninety-three samples of intact seeds were scanned in the reflectance mode of a scanning monochromator, and reference values for fatty acid composition were measured by gas-liquid chromatography. Calibration equations were developed using modified partial least square regression with internal cross validation (n=63). The equations obtained had low standard errors of cross-validation and moderate $R^2$ (coefficient of determination in calibration). Prediction of an external validation set (n=30) showed significant correlation between reference values and NIRS estimated values based on the SEP (standard error of prediction), $r^2$ (coefficient of determination in prediction) and the ratio of standard deviation (SD) of reference data to SEP. The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for oleic and linoleic acid, having good correlation between reference and NIRS estimate. The results indicated that NIRS, a nondestructive screening method could be used to rapidly determine fatty acid composition in sesame seeds in the breeding programs for high quality sesame oil.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.692-697
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future communication satellite, called "Moogunghwa", which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical geopotential term, lunar and solar gravity, drag force of the atmosphere and solar radiation pressure was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using EKF which is suitable for real-time orbit determination. The result shows that the characteristics of the satellite orbit has drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the position and velocity error , and state error standard deviation is reasonable.easonable.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.200-205
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future connumication satellite, called "Moogunghwa" , which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical term, lunar and solar gravity, drag force of the atmospher, and solar radiation pressure was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using Extended Kalman Filter which is suitable for real-time orbit determination. The result shows that the chacteristics of the satellite orbit has east-west and south-north drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the positon and velocity error, and state error standard deviation is reasonable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1356-1376
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• 2023

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

• KIEAE Journal
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• v.10 no.6
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• pp.97-103
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• 2010

Sky simulator is a effective daylighting design tool that can evaluate three dimensional performance of lighting. Especially, the dome type sky simulator offer reliable and reproducible daylighting performance with different standard sky models. Recently, K university has developed the dome type sky simulator(sky dome) with the diameter of 6.5m and the height of 3.7m. The sky dome consists of a group of 145 large steel panels with 72 halogen lamps which are arranged in a circular array. The luminance distribution of the sky dome can be calibrated by changing the angle and the brightness of the lamps respectively. To allow more reliable prediction and evaluation of daylighting through the sky dome, It is essential to validate the sky luminance distribution of the sky dome. This study consider the validation of the comparisons between the measured and the calculated luminance values for the CIE standard overcast sky. Also, the error rate between the measured and the calculated luminance values were compared to the previous studies. The results indicated that the K university sky dome can reproduce reliable CIE standard overcast sky with the average relative error rate of 4.4% and root-mean-square error(RMSE) of 5.4%.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.369-378
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• 2018

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.12
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• pp.1736-1740
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• 2004

Since the application of relatively high levels of heavy metals in the compost poses a potential hazard to plants and animals, the content of heavy metals in the compost with animal manure is important to know if it is as a fertilizer. Measurement of heavy metals content in the compost by chemical methods usually requires numerous reagents, skilled labor and expensive analytical equipment. The objective of this study, therefore, was to explore the application of near-infrared reflectance spectroscopy (NIRS), a nondestructive, cost-effective and rapid method, for the prediction of heavy metals contents in compost. One hundred and seventy two diverse compost samples were collected from forty-seven compost facilities located along the Han river in Korea, and were analyzed for Cr, As, Cd, Cu, Zn and Pb levels using inductively coupled plasma spectrometry. The samples were scanned using a Foss NIRSystem Model 6500 scanning monochromator from 400 to 2,500 nm at 2 nm intervals. The modified partial least squares (MPLS), the partial least squares (PLS) and the principal component regression (PCR) analysis were applied to develop the most reliable calibration model, between the NIR spectral data and the sample sets for calibration. The best fit calibration model for measurement of heavy metals content in compost, MPLS, was used to validate calibration equations with a similar sample set (n=30). Coefficient of simple correlation (r) and standard error of prediction (SEP) were Cr (0.82, 3.13 ppm), As (0.71, 3.74 ppm), Cd (0.76, 0.26 ppm), Cu (0.88, 26.47 ppm), Zn (0.84, 52.84 ppm) and Pb (0.60, 2.85 ppm), respectively. This study showed that NIRS is a feasible analytical method for prediction of heavy metals contents in compost.