• Asian-Australasian Journal of Animal Sciences
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• 제33권12호
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• pp.2039-2049
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• 2020

Objective: This study was conducted to provide models to accurately predict nitrogen (N) and phosphorus (P) excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data. Methods: The generic model in this study is based on the principles of the Lucas equation, describing the relation between dry matter intake (DMI) and faecal N excretion to predict the quantity of faecal N (Q_FN). Excretion of urinary N and faecal P were calculated based on National Research Council recommendations for dairy cows. A farm survey was conducted to collect input parameters for the models. The data set was used to calibrate the model to predict Q_FN for the specific case. The model was validated by comparing the predicted quantity of faecal N with the actual quantity of faecal N (Q_FNACT) based on measurements, and the calibrated model was compared to the Lucas equation. The models were used to predict N and P excretion of all 144 dairy cows in the data set. Results: Our estimate of true N digestibility equalled the standard value of 92% in the original Lucas equation, whereas our estimate of metabolic faecal N was -0.60 g/100 g DMI, with the standard value being -0.61 g/100 g DMI. Results of the model validation showed that the R² was 0.63, the MAE was 15 g/animal/d (17% from Q_FNACT), and the RMSE was 20 g/animal/d (22% from Q_FNACT). We predicted that the total N excretion of dairy cows in Indonesia was on average 197 g/animal/d, whereas P excretion was on average 56 g/animal/d. Conclusion: The proposed models can be used with reasonable accuracy to predict N and P excretion of dairy cattle on smallholder farms in Indonesia, which can contribute to improving manure management and reduce environmental issues related to nutrient losses.

• 대한기계학회논문집B
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• 제32권7호
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• pp.549-557
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• 2008

Despite of the laminar-turbulent transition region co-exist with fully turbulence region around the leading edge of an airfoil, still lots of researchers apply to fully turbulence models to predict aerodynamic characteristics. It is well known that fully turbulent model such as standard k-model couldn't predict the complex stall and the separation behavior on an airfoil accurately, it usually leads to over prediction of the aerodynamic characteristics such as lift and drag forces. So, we apply correlation based transition model to predict aerodynamic performance of the NREL (National Renewable Energy Laboratory) Phase IV wind turbine. And also, compare the computed results from transition model with experimental measurement and fully turbulence results. Results are presented for a range of wind speed, for a NREL Phase IV wind turbine rotor. Low speed shaft torque, power, root bending moment, aerodynamic coefficients of 2D airfoil and several flow field figures results included in this study. As a result, the low speed shaft torque predicted by transitional turbulence model is very good agree with the experimental measurement in whole operating conditions but fully turbulent model(${\kappa}-\;{\varepsilon}$) over predict the shaft torque after 7m/s. Root bending moment is also good agreement between the prediction and experiments for most of the operating conditions, especially with the transition model.

• 통합자연과학논문집
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• 제14권3호
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• pp.99-106
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• 2021

A mixture of alcohol and water is commonly used as antifreeze, liquor, and the fundamental solvents for the manufacture of cosmetics, pharmaceuticals, and inks in our daily life. Since various properties of alcohol water mixtures such as density, boiling or melting point, viscosity, and dielectric constant are determined by their mixing ratio, it is very important to know the mixing ratio to predict their properties. One of simple method to find the mixing ratio is measuring the density of the mixtures. However, it is not easy to predict the mixing ratio from the density of the mixtures because the relationship between mixing ratio and density has not been established well. The relationship is dependent on the relative sizes of solute and solvent molecules, and their interactions. Recently, an empirical model to predict the density of glycerol water mixture from their mixing ratio has been introduced. The suggested model is simple but quite accurate for glycerol water mixture. In this article, we investigated the applicability of this model to different alcohol water mixtures. Densities for six different alcohol water mixtures containing various alcohols (e.g., ethylene glycol, 1,3-propane diol, propylene glycol, methanol, ethanol, and 1-propanol) were simulated and compared to experimentally measured ones to investigate the applicability of the model proposed for glycerol water mixtures to other alcohol water mixtures. The model predicted the actual density of all alcohol water mixtures tested in this article with high accuracy at various ratios. This model can probably be used to predict the mixing ratio of other alcohol water mixtures from their densities beyond 6 alcohols tested in this article from their densities.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계 학술대회논문집(수송기계편)
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• pp.140-143
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• 2005

The hybrid CAE/CAT methods are widely applied to product development in various fields because this method can predict the response of the whole system when a part of the system is changed. Especially, the hybrid CAE/CAT method is very useful to predict tile vehicle NVH characteristics after changing some parts of the vehicle. Target parts can be established on the basis of test models and FE models of the prototype constructed in the planning stage of car development. In this study, the topic was focused on the proper test-based FBS application process to predict vehicle NVH characteristic. First, the test-based FBS method was apply to vehicle substructure and car-body. And then the test-based model was replaced with FE model to apply hybrid CAE/CAT method. The replaced FE model was modified through the optimization process. The interior noise in vehicle during the drive was predicted with Modified FE model, then the predicted results were verified by experimenting with actual modified model.

• 산업경영시스템학회지
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• 제38권2호
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• pp.1-7
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• 2015

As a measure of health, the percentage of body fat has been utilized for many ergonomist, physician, athletic trainers, and work physiologists. Underwater weighing procedure for measuring the percentage of body fat is popular and accurate. However, it is relatively expensive, difficult to perform and requires large space. Anthropometric techniques can be utilized to predict the percentage of body fat in the field setting because they are easy to implement and require little space. In this concern, the purpose of this study was to find a regression model to easily predict the percentage of body fat using the anthropometric circumference measurements as predictor variables. In this study, the data for 10 anthropometric circumference measurements for 252 men were analyzed. A full model with ten predictor variables was constructed based on subjective knowledge and literature. The linear regression modeling consists of variable selection and various assumptions regarding the anticipated model. All possible regression models and the assumptions are evaluated using various statistical methods. Based on the evaluation, a reduced model was selected with five predictor variables to predict the percentage of body fat. The model is : % Body Fat = 2.704-0.601 (Neck Circumference) + 0.974 (Abdominal Circumference) -0.332 (Hip Circumference) + 0.409 (Arm Circumference) - 1.618 (Wrist Circumference) + $\epsilon$. This model can be used to estimate the percentage of body fat using only a tape measure.

• 시스템엔지니어링학술지
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• 제16권2호
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• 한국지진공학회논문집
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• 제28권2호
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• 대한교통학회지
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• 제5권2호
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• pp.73-80
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• 1987

Various procedures for evaluation of traffic noise annoyance have been proposed. However, most of the studies of this type are restricted for improving traffic flow. In this paper, a method to predict the road traffic noise is proposed in terms of equivalent continuous A-Weighted sound pressure level (Leq), based on a probability model. First, distribution of the road traffic noise level are investigated. second, the weibull distribution parameters are estimated by using the quantification theory. Finally, a prediction model of the road traffic noise is proposed based on the weibull distribution model The predicted values of the Leq are closely matched the measured data.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.739-742
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• 1999

A reinforcement corrosion prediction model was proposed using the results from accelerated testing and mathematical equation from the Fick's 2nd law for chloride-induced corrosion of reinforcement in concrete. The input data included the chloride concentration, mix characteristics of concrete, and environmental conditions. This model can be used to predict the chloride concentration pertaining to corrosion time and loading age for marine concrete structures. This model can also be used to predict the service life.

• 대한안전경영과학회지
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• 제13권1호
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• pp.91-98
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• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.