• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1247-1247
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• 2001

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.5
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• pp.423-430
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• 2014

Noise and vibration, which occur in a pipe, are usually caused by the interaction between the turbulent flow and nearby wall. Although it can be estimated by a simple case of expanded pipes having complex turbulent flow, the radiated noise is highly dependent upon the size, shape, and thickness of the given model. In addition, the radiated noise propagates and has serious interference and destabilization effects on the surrounding systems, which can lead to fatigue fracture and failure. This study took advantage of the variety of commercial programs, such as FLUENT (flow solver), NASTRAN (dynamic motion solver of complex structures) and VIRTUAL LAB (radiated noise solver) based on the boundary element method (BEM), to understand the underlying physics of flow noise. The expanded pipe has separation and a high pressure drop because of the abrupt change in the cross-section. Based on the radiated noise calculations, the noise level was reduced to around 20 dB in the range of 100-500 Hz.

• Journal of The Korean Astronomical Society
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• v.46 no.3
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• pp.133-140
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• 2013

The empirical mass discrepancy-acceleration (MDA) relation of disk galaxies provides a key test for models of galactic dynamics. In terms of modified laws of gravity and/or inertia, the MDA relation quantifies the transition from Newtonian to modified dynamics at low centripetal accelerations $a_c{\lesssim}10^{-10}ms^{-2}$. As yet, neither dynamical models based on dark matter nor proposed modifications of the laws of gravity/inertia have predicted the functional form of the MDA relation. In this work, I revisit the MDA data and compare them to four different theoretical scaling laws. Three of these scaling laws are entirely empirical; the fourth one - the "simple ${\mu}$" function of Modified Newtonian Dynamics - derives from a toy model of gravity based on massive gravitons (the "graviton picture"). All theoretical MDA relations comprise one free parameter of the dimension of an acceleration, Milgrom's constant aM. I find that the "simple ${\mu}$" function provides a good fit to the data free of notable systematic residuals and provides the best fit among the four scaling laws tested. The best-fit value of Milgrom's constant is $a_M=(1.06{\pm}0.05){\times}10^{-10}ms^{-2}$. Given the successful prediction of the functional form of the MDA relation, plus an overall agreement with the observed kinematics of stellar systems spanning eight orders of magnitude in size and 14 orders of magnitude in mass, I conclude that the "graviton picture" is sufficient (albeit probably not a necessary nor unique approach) to describe galactic dynamics on all scales well beyond the scale of the solar system. This suggests that, at least on galactic scales, gravity behaves as if it was mediated by massive particles.

• Korean Journal of Optics and Photonics
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• v.20 no.4
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• pp.223-229
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• 2009

This paper describes the development of a simulator which can numerically calculate an image to be acquired in a machine vision system for automated optical inspection. The simulator is based on a ray tracing technique and composed of three modules which are an illuminating system, a specimen and an imaging system. Kinds of model parameters for modules and their values are carefully chosen from the direct measurement and the observation of related phenomena. Finally, the validity of the simulator is evaluated by logical analysis and by comparison with measured images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.770-776
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• 2021

Positioning systems have been actively researched and developed over the past few years and have been used in many applications. This paper presents a method to determine a location in a large space using a sensor system consisting of an accelerometer and a single-axis gyroscope. In particular, to consider usability, a sensor device was loosely worn on the waist so that the experimental data could be used in practical applications. Based on the experimental results of circular tracks with radiuses of 1m and 3m, in this paper, an algorithm using the threshold of rotation angle was proposed and applied to the experimental results. A tracking experiment was performed on the grid-pattern track model. For raw sensor data, the average deviation between the final tracking point and the target point was approximately 15.2 m, which could be reduced to approximately 4.0 m using an algorithm applying the rotation angle threshold.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.13-23
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• 2021

In this paper, we proposed a framework that generates the trajectory of a single rigid body based on its COM configuration and contact pose. Because we use a smaller input dimension than when we use a full body state, we can improve the learning time for reinforcement learning. Even with a 68% reduction in learning time (approximately two hours), the character trained by our network is more robust to external perturbations tolerating an external force of 1500 N which is about 7.5 times larger than the maximum magnitude from a previous approach. For this framework, we use centroidal dynamics to calculate the next configuration of the COM, and use reinforcement learning for obtaining a policy that gives us parameters for controlling the contact positions and forces.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.452-459
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• 2023

The core power control is an important issue for the study of dynamic characteristics in China initiative accelerator driven subcritical system (CiADS), which has direct impact on the control strategy and safety analysis process. The CiADS is an experimental facility that is only controlled by the proton beam intensity without considering the control rods in the current engineering design stage. In order to get the optimized operation scheme with the stable and reliable features, the variation of beam intensity using the continuous and periodic control approaches has been adopted, and the change of collimator and the adjusting of duty ratio have been proposed in the power control process. Considering the neutronics and the thermal-hydraulics characteristics in CiADS, the physical model for the core power control has been established by means of the point reactor kinetics method and the lumped parameter method. Moreover, the multi-inputs single-output (MISO) logical structure for the power control process has been constructed using proportional integral derivative (PID) controller, and the meta-heuristic algorithm has been employed to obtain the global optimized parameters for the stable running mode without producing large perturbations. Finally, the verification and validation of the control method have been tested based on the reference scenarios in considering the disturbances of spallation neutron source and inlet temperature respectively, where all the numerical results reveal that the optimization method has satisfactory performance in the CiADS core power control scenarios.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2112-2124
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• 2023

With the rise of economic and safety standards for nuclear reactors, new concepts of Gen-IV reactors and modular reactors showed more complex designs that challenge current tools for reactor physics analysis. A Monte Carlo (MC) two-step method was proposed in this work. This calculation scheme uses the continuous-energy MC method to generate multi-group cross-sections from heterogeneous models. The multi-group MC method, which can adapt locally-heterogeneous models, is used in the core calculation step. This calculation scheme is verified using a Gen-IV modular lead-based fast reactor (LFR) benchmark case. The influence of homogenized patterns, scatter approximations, flux separable approximation, and local heterogeneity in core calculation on simulation results are investigated. Results showed that the cross-sections generated using the 3D assembly model with a locally heterogeneous representation of control rods lead to an accurate estimation with less than 270 pcm bias in core reactivity, 0.5% bias in control rod worth, and 1.5% bias on power distribution. The study verified the applicability of multi-group cross-sections generated with the MC method for LFR analysis. The study also proved the feasibility of multi-group MC in core calculation with local heterogeneity, which saves 85% time compared to the continuous-energy MC.

• Journal of the Korean Chemical Society
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• v.65 no.2
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• pp.151-165
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• 2021

The study applied chemistry classes based on Collaborative Problem-Solving for Competency(CoProC) Instruction Model to 86 high school seniors and recognized the resulting changes in Character Competence through character competence test, analysis of argumentation, and analysis of reflective writing. In addition, the analysis of the correlation between the assessment results showed what each evaluation method meant to assess Character Competence. The study found that the average score of Character Competence rose in all three assessments, but differences were found in the detailed factors. Character competency test is meaningful in helping students participate with a sense of mastery of learning as a self-evaluation, and the analysis of the argumentation process seems to be an appropriate evaluation method to evaluate the Character Competence shown by students' words and actions through course evaluations. An analysis of reflective writing showed the advantage of being able to analyze Character competencies that are difficult to show in action by looking back on the entire course of learning. In addition, given that the correlation between the three assessment results is very low, it is expected that the three Character competency assessment methods will be conducted together to obtain detailed and comprehensive information on students' character competencies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.