• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.7
• /
• pp.39-46
• /
• 2008

High precision machining technology has become one of the important parts in the development of a precision machine. Such a machine requires high speed on a large workspace as well as high precision positioning. For machining systems having a long stroke with ultra precision, a dual-stage system including a global stage (coarse stage) and a micro stage (fine stage) is designed in this paper. Though linear motors have a long stroke and high precision feed drivers, they have some limitations for submicron positioning. Piezo-actuators with high precision also have severe disadvantage for the travel range, and the stroke is limited to a few microns. In the milling experiments, the positional accuracy has been readily achieved within 0.2 micron over the typical 20 mm stroke, and the path error over 2 micron was reduced within 0.2 micron. Therefore, this technique can be applied to develop high precision positioning and machining in the micro manufacturing and machining system.

• Structural Engineering and Mechanics
• /
• v.71 no.5
• /
• pp.545-552
• /
• 2019

The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.

• Bulletin of the Korean Chemical Society
• /
• v.26 no.12
• /
• pp.2007-2016
• /
• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Analytical Science and Technology
• /
• v.12 no.6
• /
• pp.528-533
• /
• 1999

The total iodine is determined in serum, fresh milk and feed additive by ICP-MS. The preparation involves only a 25-fold dilution with a diluent by direct nebulization in the plasma. The diluent composition is $NH_4OH$(0.5% v/v)+$CH_3OH$(5% v/v) and the abundance of iodine is measured at m/z=127. The calibration curve was linear over the ranges $0-100{\mu}g/L$ with $R^2=0.99$ for iodine. The detection limit of this method is $0.084{\mu}g/L$ for iodine. The relative error range of milk powder SRM in optimum condition is 2.30%-4.73%. The concentration ranges of iodine in the serum, fresh milk, feed additive were $12.4-40.2{\mu}g/L$, < 0.01-3.11 mg/L, < $10^{-7}-2.60g/kg$ respectively.

• Asian-Australasian Journal of Animal Sciences
• /
• v.27 no.10
• /
• pp.1426-1435
• /
• 2014

Saponins have been considered as promising natural substances for mitigating methane emissions from ruminants. However, studies reported that addition of saponin-rich sources often arrived at contrasting results, i.e. either it decreased methane or it did not. The aim of the present study was to assess ruminal methane emissions through a meta-analytical approach of integrating related studies from published papers which described various levels of different saponin-rich sources being added to ruminant feed. A database was constructed from published literature reporting the addition of saponin-rich sources at various levels and then monitoring ruminal methane emissions in vitro. Accordingly, levels of saponin-rich source additions as well as different saponin sources were specified in the database. Apart from methane, other related rumen fermentation parameters were also included in the database, i.e. organic matter digestibility, gas production, pH, ammonia concentration, short-chain fatty acid profiles and protozoal count. A total of 23 studies comprised of 89 data points met the inclusion criteria. The data obtained were subsequently subjected to a statistical meta-analysis based on mixed model methodology. Accordingly, different studies were treated as random effects whereas levels of saponin-rich source additions or different saponin sources were considered as fixed effects. Model statistics used were p-value and root mean square error. Results showed that an addition of increasing levels of a saponin-rich source decreased methane emission per unit of substrate incubated as well as per unit of total gas produced (p<0.05). There was a decrease in acetate proportion (linear pattern; p<0.001) and an increase in propionate proportion (linear pattern; p<0.001) with increasing levels of saponin. Log protozoal count decreased (p<0.05) at higher saponin levels. Comparing between different saponin-rich sources, all saponin sources, i.e. quillaja, tea and yucca saponins produced less methane per unit of total gas than that of control (p<0.05). Although numerically the order of effectiveness of saponin-rich sources in mitigating methane was yucca>tea>quillaja, statistically they did not differ each other. It can be concluded that methane mitigating properties of saponins in the rumen are level- and source-dependent.

• Journal of Welding and Joining
• /
• v.14 no.6
• /
• pp.131-139
• /
• 1996

The response of the arc sensor using the welding current and/or welding voltage as its outputs has been obtained by the analysis and/or experiments of the static characteristics of arc sensor. But in order to improve the reliability of arc sensor, it is necessary to know its dynamic characteristics. So in this paper, it is presented the dynamic model of arc sensor including the power source, arc voltage, electrode burnoff rate, and wire feed rate. A numerical simulation of the dynamic model of arc sensor was implemented, computing the welding current with input of CTWD. The results of computer simulations and experiments of $CO_2$arc welding showed that a linear relationship between weaving center - weld line distance and current area difference was established. Additionally, a real-time weld seam tracking system interfaced with industrial welding robot was constructed, the result of the weld seam tracking experiment for weld line with an initial offset error of 5$^{\circ}$was good.

• International journal of advanced smart convergence
• /
• v.4 no.1
• /
• pp.45-53
• /
• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.2
• /
• pp.72-79
• /
• 2003

A numerical calculation was conducted to estimate and to elucidate the role of the radiative heat flux from metal particles(Al, $Al_2O_3$) on the dynamic extinction of solid propellant rocket where the rapid depressurization took place. Anon-linear flame modeling implemented by the residence time modeling for metalized propellant was adopted to evaluate conductive heat flux to the propellant surface. The radiative heat feed back was calculated with the aid of a modified comvustion-flow model as well. The calculation results with the propellant of AP:Al:CTPB=76:10:14 had revealed that the radiative heat flux is approximately 5~6% of total flux at the critical depressurization rate regardless of chamber geometry (open or confined chamber). It was also found that the dynamic extinction in open geometry could be predicted at the depressurization rate about 45% larger with radiative heat feedback than without radiation. Thus, it should be claimed that even a small amount of radiative flux 5~6% could produce a big error in predicting the critical depressurization rate of the metalized propellant combustion.

• Journal of Bio-Environment Control
• /
• v.30 no.4
• /
• pp.393-400
• /
• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.