• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.1
• /
• pp.61-70
• /
• 2020

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.17 no.3
• /
• pp.293-300
• /
• 1999

The study aims to develope techniques for generating digital elevation model(DEM) from IRS-1C PAN stereo image data. The bundle adjustment technique was used to determine the satellite exterior orientation parameters as a function of along-track lines. The first degree of polynomial was selected as a function of satellite attitude and position for each scan line. To evaluate the DEM and orthoimage generated, the resulted three dimensional coordinates of the 16 elevation points were computed with the map coordinates. The elevation test showed that root mean square errors of the DEM elevation was about $\pm{16.66m}$ meters.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.1
• /
• pp.27-34
• /
• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• Journal of Preventive Veterinary Medicine
• /
• v.42 no.4
• /
• pp.143-147
• /
• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.67.2-67.2
• /
• 2020

Not only the magnitude of the mirror surface error, the pattern matters as it produces certain aberrations. In particular, the surface error of the freeform mirrors, which are optimized to eliminate specific aberrations, might show much higher sensitivity in optical performance. Therefore, we analyze the mirror surface error with Zernike polynomials with the goal of generating a realistic error surface. We investigate the surface error of the freeform mirror fabricated by diamond turning machine to analyze the realistic tendency of the error. The surface error with 0.22 ㎛ root-mean-square value is fitted to the Zernike terms using the incremental fitting method, which increases the number of the fitting coefficients through steps. Furthermore, optical performance via surface error pattern based on Zernike terms is studied to see the influences of each term. With this study, realistic error surface generation may allow higher accuracy not only for the feasibility test but also for all tests and predictions using optical simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.22 no.4
• /
• pp.383-390
• /
• 2004

IGS provides so accute a final precise ephmerides which is offered in the 13rd, and it also offers a rapid precise ephmerides for more prompt application and an ultra-rapid precise ephmerides for real-time application. The purpose of this study is to analyze the accuracy of a rapid precise ephemerides and an ultra-rapid precise ephemerides based on a final precise ephmerides and determine the degree of the Lagrange Interpolation which needs to decide the location of a satellite. As the result of this study, the root mean square error of x,y,z coordinates of a rapid precise ephemerides was $\pm$0.0l6m or so, and the root mean square error of an observed ultra-rapid precise ephemerides was approximately $\pm$0.024m. The root mean square error of an ultra-rapid precise ephemerides predicted for 24 hours was $\pm$0.07m or so and the one of an ultra-rapid precise ephemerides predicted for 6 hours was $\pm$0.04m or so. Therefore, I could figure out that it had higher accuracy than a broadcast ephemerides. Also, in case that the location of a satellite was calculated with the method of the Lagrange Interpolation, it was confirmed that using the 9th order polynomial was efficient.

• Journal of Food Hygiene and Safety
• /
• v.32 no.3
• /
• pp.206-210
• /
• 2017

This study was performed to develope mathematical models for predicting growth kinetics of Staphylococcus aureus in the processed meat product, pyeonyuk. Growth patterns of S. aureus in pyeonyuk were determined at the storage temperatures of 4, 10, 20, and $37^{\circ}C$ respectively. The number of S. aureus in pyeonyuk increased at all the storage temperatures. The maximum specific growth rate (${\mu}_{max}$) and lag phase duration (LPD) values were calculated by Baranyi model. The ${\mu}_{max}$ values went up, while the LPD values decreased as the storage temperature increased from $4^{\circ}C$ to $37^{\circ}C$. Square root model and polynomial model were used to develop the secondary models for ${\mu}_{max}$ and LPD, respectively. Root Mean Square Error (RMSE) was used to evaluate the developed model and the fitness was determind to be 0.42. Therefore the developed predictive model was useful to predict the growth of S. aureus in pyeonyuk and it will help to prevent food-born disease by expanding for microbial sanitary management guide.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1329-1350
• /
• 2018

There have been many applications to observe Earth using synthetic aperture radar (SAR) since it could acquire Earth observation data without reference to weathers or local times. However researches about digital map generation using SAR have hardly been performed due to complex raw data processing. In this study, we suggested feasibility of producing digital map using SAR stereo images. We collected two sets, which include an ascending and a descending orbit acquisitions respectively, of KOMPSAT-5 stereo dataset. In order to suggest the feasibility of digital map generation from SAR stereo images, we performed 1) rational polynomial coefficient transformation from radar geometry, 2) digital resititution using KOMPSAT-5 stereo images, and 3) validation using digital-map-derived reference points and check points. As the results of two models, root mean squared errors of XY and Z direction were less than 1m for each model. We discussed that KOMPSAT-5 stereo image could generated 1:25,000 digital map which meets a standard of the digital map. The proposed results would contribute to generate and update digital maps for inaccessible areas and wherever weather conditions are unstable such as North Korea or Polar region.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.148-159
• /
• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Investigative Magnetic Resonance Imaging
• /
• v.22 no.1
• /
• pp.37-49
• /
• 2018

Purpose: The effect of global inhomogeneity on quantitative susceptibility mapping (QSM) was investigated. A technique referred to as Simultaneous Unwrapping Phase with Error Recovery from inhomogeneity (SUPER) is suggested as a preprocessing to QSM to remove global field inhomogeneity-induced phase by polynomial fitting. Materials and Methods: The effect of global inhomogeneity on QSM was investigated by numerical simulations. Three types of global inhomogeneity were added to the tissue susceptibility phase, and the root mean square error (RMSE) in the susceptibility map was evaluated. In-vivo QSM imaging with volunteers was carried out for 3.0T and 7.0T MRI systems to demonstrate the efficacy of the proposed method. Results: The SUPER technique removed harmonic and non-harmonic global phases. Previously only the harmonic phase was removed by the background phase removal method. The global phase contained a non-harmonic phase due to various experimental and physiological causes, which degraded a susceptibility map. The RMSE in the susceptibility map increased under the influence of global inhomogeneity; while the error was consistent, irrespective of the global inhomogeneity, if the inhomogeneity was corrected by the SUPER technique. In-vivo QSM imaging with volunteers at 3.0T and 7.0T MRI systems showed better definition in small vascular structures and reduced fluctuation and non-uniformity in the frontal lobes, where field inhomogeneity was more severe. Conclusion: Correcting global inhomogeneity using the SUPER technique is an effective way to obtain an accurate susceptibility map on QSM method. Since the susceptibility variations are small quantities in the brain tissue, correction of the inhomogeneity is an essential element for obtaining an accurate QSM.