• Food Science and Preservation
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• v.12 no.2
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• pp.141-145
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• 2005

To investigate the relationship between the milling degree and color of milled rice, an empirical whiteness model was developed according to the milling degree from $0\%\;to\;20\%$ using paddy of three different varieties of Chuchung, Nampyong and Odae. The values of determination coefficient and the root mean square error between measured and predicted whiteness were 0.990, 0.877, respectively, and the whiteness model was proved to be quite applicable. The relationships between whiteness values and color factors in several color systems were tested to select useful color factors for development of convenient whiteness meter. The whiteness value of milled rice according to degree of milling could be converted into b and Hunter whiteness in Lab color system. B in RGB color system at high values of determination coefficient were 0.990, 0.985, and 0.989, respectively.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.655-661
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• 2017

Sparse view CT has been widely used to reduce radiation dose to patient in radiation therapy. In this work, we performed sinogram restoration from sparse sampling data by using inpainting method for simulation and experiment. Sinogram restoration was performed in accordance with sampling angle and restoration method, and their results were validated with root mean square error (RMSE) and image profiles. Simulation and experiment are designed to fan beam scan for various projection angles. Sparse data in sinogram were restored by using linear interpolation and inpainting method. Then, the restored sinogram was reconstructed with filtered backprojection (FBP) algorithm. The results showed that RMSE and image profiles were depended on the projection angles and restoration method. Based on the simulation and experiment, we found that inpainting method could be improved for sinogram restoration in comparison to linear interpolation method for estimating RMSE and image profiles.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.277-288
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• 2020

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

• Food Science of Animal Resources
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• v.35 no.1
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• pp.108-113
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• 2015

The purpose of this study was to develop predictive models for the growth of Listeria monocytogenes in pork Bulgogi at various storage temperatures. A two-strain mixture of L. monocytogenes (ATCC 15313 and isolated from pork Bulgogi) was inoculated on pork Bulgogi at 3 Log CFU/g. L. monocytogenes strains were enumerated using general plating method on Listeria selective medium. The inoculated samples were stored at 5, 15, and $25^{\circ}C$ for primary models. Primary models were developed using the Baranyi model equations, and the maximum specific growth rate was shown to be dependent on storage temperature. A secondary model of growth rate as a function of storage temperature was also developed. As the storage temperature increased, the lag time (LT) values decreased dramatically and the specific growth rate of L. monocytogenes increased. The mathematically predicted growth parameters were evaluated based on the modified bias factor ($B_f$), accuracy factor ($A_f$), root mean square error (RMSE), coefficient of determination ($R^2$), and relative errors (RE). These values indicated that the developed models were reliably able to predict the growth of L. monocytogenes in pork Bulgogi. Hence, the predictive models may be used to assess microbiological hygiene in the meat supply chain as a function of storage temperature.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.4
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• pp.32-42
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• 2007

The substrate resistance is modeled to estimate the performance degradation of analog circuits by substrate noise in a $0.35{\mu}m$ twin-well non-epitaxial CMOS process. The substrate resistance model equations are applied to the P+ guard-ring isolation structure and a good match was achieved between measurements and models. The substrate resistance is divided into four types and a semi-empirical model equation is obtained for each type of substrate resistance. The rms(root-mean-square) error of the substrate resistance model is below 10% compared with the measured resistance. To apply this substrate resistance model to the P+ guard ring structure, ADS(Advanced Design System) circuit simulation results are compared with the measurement results using Network Analyzer, and relatively good agreements are obtained between measurements and simulations.

• Journal of Environmental Science International
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• v.22 no.5
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• pp.555-562
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• 2013

Jeju Island, the heaviest raining area in Korea, is a volcanic Island located at the southernmost of Korea, but most streams are of the dry due to its hydrological/geological characteristics different from those of inland areas. Therefore, there are limitations in applying the results from the mainland to the studies on stream run-off characteristics analysis and water resource analysis of Jeju Island. In this study, the SWAT(soil & water assessment tool) model is used for the Hwabuk stream watershed located east of the downtown to calculate the long-term stream run-off rate, and WMS(watershed modeling system) and HEC-HMS(hydrologic modeling system) models are used to figure out the stream run-off characteristics due to short-term heavy rainfall. As the result of SWAT modelling for the long-term rainfall-runoff model for Hwabuk stream watershed in 2008, 5.66% of the average precipitation of the entire basin was run off, with 3.47% in 2009, 8.12% in 2010, and root mean square error(RMSE) and determination coefficient($R^2$) was 496.9 and 0.87, respectively, with model efficient(ME) of 0.72. From the results of WMS and HEC-HMS models which are short-term rainfall-runoff models, unless there was a preceding rainfall, the runoff occurred only for rainfall of 40mm or greater, and the run-off duration averaged 10~14 hours.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.203-209
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• 2009

The purpose of this study is to develop a minimally constraint joint angle measurement system for the feedback control of FES (functional electrical stimulation) locomotion. Feedback control is desirable for the efficient FES locomotion, however, the simple on-off control schemes are mainly used in clinic because the currently available angle measurement systems are heavily constraint or cosmetically poor. We designed a new angle measurement system consisting of a magnet and magnetic sensors located below and above the ankle joint, respectively, in the rear side of ipsilateral leg. Two magnetic sensors are arranged so that the sensing axes are perpendicular each other. Multiple positions of sensors attachment on the shank part of the ankle joint model and also human ankle joint were selected and the accuracy of the measured angle at each position was investigated. The reference ankle joint angle was measured by potentiometer and motion capture system. The ankle joint angle was determined from the fitting curve of the reference angle and magnetic flux density relationship. The errors of the measured angle were calculated at each sensor position for the ankle range of motion (ROM) $-20{\sim}15$ degrees (dorsiflexion as positive) which covers the ankle ROM of both stroke patients and normal subjects during locomotion. The error was the smallest with the sensor at the position 1 which was the nearest position to the ankle joint. In case of human experiment, the RMS (root mean square) errors were $0.51{\pm}1.78(0.31{\sim}0.64)$ degrees and the maximum errors were $1.19{\pm}0.46(0.68{\sim}1.58)$ degrees. The proposed system is less constraint and cosmetically better than the existing angle measurement system because the wires are not needed.

• Atmosphere
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• v.24 no.4
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• pp.523-532
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• 2014

In this study, thermal tropopause height defined from WMO (World Meteorological Organization) using temperature profile derived from Advance Microwave Sounding Unit-A (AMSU-A; hereafter named AMSU) onboard EOS (Earth Observing System) Aqua satellite is retrieved. The temperature profile of AMSU was validated by comparison with the radiosonde data observed at Osan weather station. The validation in the upper atmosphere from 500 to 100 hPa pressure level showed that correlation coefficients were in the range of 0.85~0.97 and the bias was less than 1 K with Root Mean Square Error (RMSE) of ~3 K. Thermal tropopause height was retrieved by using AMSU temperature profile. The bias and RMSE were found to be -5~ -37 hPa and 45~67 hPa, respectively. Correlation coefficients were in the range of 0.5 to 0.7. We also analyzed the change of tropopause height and temperature in middle troposphere in the extreme heavy rain event (23 October, 2003) associated with tropopause folding. As a result, the distinct descent of tropopause height and temperature decrease of ~8 K at 500 hPa altitude were observed at the hour that maximum precipitation and maximum wind speed occurred. These results were consistent with ERA (ECMWF Reanalysis)-Interim data (potential vorticity, temperature) in time and space.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.3
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• pp.380-393
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• 2015

This study presents how we can evaluate stereo camera systems for the structural deformation monitoring. A stereo camera system, consisting of a set of stereo cameras and reflective markers attached on the structure, is introduced for the measurement and the stereo pattern recognition (SPR) method is utilized for the full-field structural deformation estimation. Performance of this measurement system depends on many parameters including types and specifications of the cameras, locations and orientations of them, and sizes and positions of markers; it is difficult to experimentally identify the effects of each parameter on the measurement performance. In this study, a simulation framework for evaluating performance of the stereo camera systems with various parameters has been developed. The maximum normalized root-mean-square (RMS) error is defined as a representative index of stereo camera system performance. A plate structure is chosen for an introductory example. Its several modal harmonic vibrations are generated and estimated in the simulation framework. Two cases of simulations are conducted to see the effects of camera locations and the resolutions of the cameras. An experimental validation is carried out for a few selected cases from the simulations. Using the simultaneous laser displacement sensor (LDS) measurements as the reference, the measurement errors are obtained and compared with the simulations.