• Journal of Biosystems Engineering
• /
• v.35 no.2
• /
• pp.91-99
• /
• 2010

A Model of small scale total mixed rations(TMR) plant which can be utilized round bales was developed, tested and analyzed in this study. This study consist of two parts. One is development of a small scale TMR plant model which was already reported at the previous paper. This is the second part of the study. For the study, a series of tests of the model plant were performed and its costs was analyzed. Also, the break-even point of the model plant by comparing with market price of commercial TMR feed was determined. Results of the research are summarized as follows ; As the results of mixing test, the average coefficient of variation(CV) value for mixing of the feed was 13.0 % at the gate of the mixer. The production cost was estimated as 8,298 won/head for dairy cattle farm and 2,495 won/head for beef cattle farm, when producing 8 batch a day. Also, it is recommended to utilize the model plant when farm size is over 79 heads for dairy cattle farm and 113 heads for beef cattle farm. As an overall conclusion, the model plant designed for farm size TMR feed mill will be very useful model for both beef cattle and dairy farms in Korea. Also it is expected that the capital investment for the model plant can be recovered with 8 months compare with purchasing commercial TMR feed if the model plant feeds 1,000 beef cattle approximately.

• Journal of Soil and Groundwater Environment
• /
• v.15 no.6
• /
• pp.91-98
• /
• 2010

The effective thermal conductivity of porous materials is usually determined by porosity, water content, and the conductivity of the matrix. In addition, it is also affected by the internal structure of the materials such as the size, arrangement, and connectivity of the matrix-forming grains. Based on the structural models for multi-phase materials, thermal conductivities of soils and sands measured with varying the water content were analyzed. Thermal conductivities of dry samples were likely to fall in the region between the Maxwell-Eucken model with air as the continuous phase and the matrix as the dispersed phase ($ME_{air}$) and the co-continuous (CC) model. However, water-saturated samples moved down to the region between the $ME_{wat}$ model and the series model. The predictive inconsistency of the structural models for dry and water-saturated samples may be caused by the increase of porosity for water-saturated samples, which leads to decrease of connectivity among the grains of matrix. In cases of variably saturated samples with a uniform grain size, the thermal conductivity showed progressive changes of the structural models from the $ME_{air}$ model to the $ME_{wat}$ model depending on the water content. Especially, an abrupt increase found in 0-20% of the water content, showing transition from the $ME_{air}$ model to the CC model, can be attributed to change of water from the dispersed to continuous phase. On the contrary, the undisturbed soil samples with various sizes of grains showed a gradual increase of conductivity during the transition from the $ME_{air}$ model to the CC model.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.2
• /
• pp.289-299
• /
• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.

• Journal of the Society of Naval Architects of Korea
• /
• v.52 no.2
• /
• pp.104-109
• /
• 2015

The Korea Research Institute of Ships & Ocean Engineering (KRISO) has an ice tank to make a test environment similar to the real ice in the polar sea in order to carry out model tests. One of the most important task of the ice tank is to generate the model ice to have similar material properties as sea ice. The primary properties of sea ice which influence the ice performance of ice breakers and ice-strengthened vessels traveling in the polar sea are ice thickness, flexural strength, density, modulus of elasticity and crystal structure etc. Among them, since the density of model ice influences the buoyance resistance of ice for the ship model, the accurate measurement of ice density should be used to obtain the accurate analysis results from the model test. In this paper, some existing methods to measure the density of model ice are reviewed and a new one is proposed to measure it accurately and easily as possible. In this study, the measuring system including an UTM and several measuring devices was established to obtain the model ice density. Polyethylene and ice specimens are used for a series of repeatable measurement tests. From the results, it was recognized that both of the displacement method and the weight/weight methods gave the stable and favorable tendency.

• Journal of Navigation and Port Research
• /
• v.45 no.6
• /
• pp.284-297
• /
• 2021

A simple kinematic model for the prediction of ship manoeuvres based on trial data is proposed in this study. The model consists of first order differential equations in surge, sway, and yaw directions which simulate the time series of each velocity component. Actually instead of sea trial data, dynamic model simulations are conducted with randomly varied control inputs such as propeller revolution rates and rudder angles. Based on learning of control inputs and velocity outputs of dynamic model simulations in sufficient time, kinematic model coefficients are optimized so that the kinematic model can be approximately reproduce the velocity outputs of dynamic model simulations with arbitrary control inputs. The resultant kinematic model is verified with new dynamic simulation sets.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.2
• /
• pp.274-281
• /
• 2019

Objective: The objective of this study was to estimate the risk of Campylobacter jejuni (C. jejuni) infection from various jerky products in Korea. Methods: For the exposure assessment, the prevalence and predictive models of C. jejuni in the jerky and the temperature and time of the distribution and storage were investigated. In addition, the consumption amounts and frequencies of the products were also investigated. The data for C. jejuni for the prevalence, distribution temperature, distribution time, consumption amount, and consumption frequency were fitted with the @RISK fitting program to obtain appropriate probabilistic distributions. Subsequently, the dose-response models for Campylobacter were researched in the literature. Eventually, the distributions, predictive model, and dose-response model were used to make a simulation model with @RISK to estimate the risk of C. jejuni foodborne illness from the intake of jerky. Results: Among 275 jerky samples, there were no C. jejuni positive samples, and thus, the initial contamination level was statistically predicted with the RiskUniform distribution [RiskUniform (-2, 0.48)]. To describe the changes in the C. jejuni cell counts during distribution and storage, the developed predictive models with the Weibull model (primary model) and polynomial model (secondary model) were utilized. The appropriate probabilistic distribution was the BetaGeneral distribution, and it showed that the average jerky consumption was 51.83 g/d with a frequency of 0.61%. The developed simulation model from this data series and the dose-response model (Beta Poisson model) showed that the risk of C. jejuni foodborne illness per day per person from jerky consumption was $1.56{\times}10^{-12}$. Conclusion: This result suggests that the risk of C. jejuni in jerky could be considered low in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2A
• /
• pp.285-292
• /
• 2006

In this study, a method determining the local bond-slip model from pure shear test results of CFRP sheet-concrete adhesive joints is proposed and local bond-slip models are presented. Adhesive joints with a specific bond-slip model, which is assumed as multi-linear curve in order to represent arbitary function, are solved numerically. The difference between the solution and test results are minimized for finding the bond-slip model. The model with bilinear curve is also optimized to verify the improvement of multi-linear model. The selected test results are ultimate load-adhesive length curves from a series of adhesive joints and load-displacement curves for each joint. The optimization problem is formulated by physical programming, and the optimized bond-slip model is found using genetic algorithm.

• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.1
• /
• pp.111-115
• /
• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.55 no.3
• /
• pp.141-145
• /
• 2006

The line current problem(2-dimensional space : point source) is not easy to analyze the magnetic field using the standard finite element method(FEM), such as overhead trolley line or transmission line. To supplement such a defect this paper is proposed the coupling scheme of analytical solution and FEM. In analysis of the magnetic field using the standard FEM. If the current region is a relatively small compared to the whole region. Therefore the current region must be finely divided using a large number of elements. And the large number of elements increase the number of unknown variables and the use of computer memories. In this paper, an analytical solution is suggested to supplement this weak points. When source is line current and the part of interest is far from line current, the analytical solution can be coupling with FEM at the boundary. Analytical solution can be described by the multiplication of two functions. One is power function of radius, the other is a trigonometric function of angle in the cylindrical coordinate system. There are integral constants of two types which can be established by fourier series expansion. Also fourier series is represented as the factor to apply the continuity of the magnetic vector potential and magnetic field intensity with tangential component at the boundary. To verify the proposed algorithm, we chose simplified model existing magnetic material in FE region. The results are compared with standard FE solution. And it is good agreed by increasing harmonic order.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1511-1523
• /
• 2016

The increasing trend of the five violent crimes (murder, robbery, rape, violence, theft) in Korea is not independent of social and economic factors. Several social science research have discussed about this issue but most of them do not properly reflect the nature of the time-series data. Based on several time series models, we studied about the endogenous factors (time, seasonal and cycle factors) and exogenous factors (economical, social change and crime control factors) on violent crime occur in Korea. Autocorrelation were also taken into account. Through this study, we want to help to make preventive policy by explaining the cause of violent crime and predicting the future incidence of it.