• Asian-Australasian Journal of Animal Sciences
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• v.26 no.9
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• pp.1237-1246
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• 2013

The objective of this study was to estimate variance components and genetic parameters for lactation milk yield (LY), lactation length (LL), average milk yield per day (YD), initial milk yield (IY), peak milk yield (PY), days to peak (DP) and parameters (ln(a) and c) of the modified incomplete gamma function (MIG) in an Ethiopian multibreed dairy cattle population. The dataset was composed of 5,507 lactation records collected from 1,639 cows in three locations (Bako, Debre Zeit and Holetta) in Ethiopia from 1977 to 2010. Parameters for MIG were obtained from regression analysis of monthly test-day milk data on days in milk. The cows were purebred (Bos indicus) Boran (B) and Horro (H) and their crosses with different fractions of Friesian (F), Jersey (J) and Simmental (S). There were 23 breed groups (B, H, and their crossbreds with F, J, and S) in the population. Fixed and mixed models were used to analyse the data. The fixed model considered herd-year-season, parity and breed group as fixed effects, and residual as random. The single and two-traits mixed animal repeatability models, considered the fixed effects of herd-year-season and parity subclasses, breed as a function of cow H, F, J, and S breed fractions and general heterosis as a function of heterozygosity, and the random additive animal, permanent environment, and residual effects. For the analysis of LY, LL was added as a fixed covariate to all models. Variance components and genetic parameters were estimated using average information restricted maximum likelihood procedures. The results indicated that all traits were affected (p<0.001) by the considered fixed effects. High grade $B{\times}F$ cows (3/16B 13/16F) had the highest least squares means (LSM) for LY ($2,490{\pm}178.9kg$), IY ($10.5{\pm}0.8kg$), PY ($12.7{\pm}0.9kg$), YD ($7.6{\pm}0.55kg$) and LL ($361.4{\pm}31.2d$), while B cows had the lowest LSM values for these traits. The LSM of LY, IY, YD, and PY tended to increase from the first to the fifth parity. Single-trait analyses yielded low heritability ($0.03{\pm}0.03$ and $0.08{\pm}0.02$) and repeatability ($0.14{\pm}0.01$ to $0.24{\pm}0.02$) estimates for LL, DP and parameter c. Medium heritability ($0.21{\pm}0.03$ to $0.33{\pm}0.04$) and repeatability ($0.27{\pm}0.02$ to $0.53{\pm}0.01$) estimates were obtained for LY, IY, PY, YD and ln(a). Genetic correlations between LY, IY, PY, YD, ln(a), and LL ranged from 0.59 to 0.99. Spearman's rank correlations between sire estimated breeding values for LY, LL, IY, PY, YD, ln(a) and c were positive (0.67 to 0.99, p<0.001). These results suggested that selection for IY, PY, YD, or LY would genetically improve lactation milk yield in this Ethiopian dairy cattle population.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.3
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• pp.91-100
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• 1999

Tow constitutive models for weathered granite soil, Yasufuku's constitutive model with a single yield surface and Lade's constitutive model with two intersectiong yield surface compared in terms of their capabilities to accurately capture the observed behavior of compacted weathered grainite soil for various stress-paths. Both the single surface and the double surface models capture the experimentally observed behavior at a variety of stress-paths with good accuracy. The double surface model may model the observed compacted weathered granite soil behavior with better accuracy for proportational loading with increasing stress, but the single surface model may model dilatancy property with better accuracy for p-constant loading with increasing stress ratio.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.393-398
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• 2000

Densification behavior of mixed copper and tool steel powder under cold compaction was investigated. By mixing the yield functions originally proposed by Fleck-Gurson for pure powder, a new mixed yield functions In terms of volume fractions and contact numbers of Cu powder were employed in the constitutive models. The constitutive equations were implemented into a finite element program (ABAQUS) to compare with experimental data. and with calculated results from the model of Kim et at. for densification of mixed powder under cold isostatic pressing and cold die compaction. Finite element calculations by using the yield functions mixed by contact numbers of Cu powder agreed better with experimental data than those by volume fractions of Cu powder.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• Korea-Australia Rheology Journal
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• v.13 no.4
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• pp.197-203
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• 2001

We reported on the eletrorheological (ER) properties of several semiconducting polymers including poly (p-phenylene) (PPP), poly (acene quinone) radicals (PAQRs), microencapsulated polyaniline (MPANI) and polyaniline (PANI) those we synthesized. The yield stress dependence on electric field strength for the ER fluids using these semiconducting polymers was mainly examined. The yield stress, which is an important design parameter for ER fluids, was observed to satisfy a universal scaling function, allowing that yield stress data for all the ER fluids examined in this study collapse onto a single curve for a broad range of electric field strengths. The proposed scaling function incorporates both the polarization and conductivity models.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.207-209
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• 2003

Quantifying crop production is one of the most important applications of remote sensing in which the temporal and up-to-date data can play very important role in avoiding any immediate insufficiency in agricultural production. A combination of climatic data and biophysical parameters derived from Landsat7 ETM+ was used to develop a mathematical model for wheat yield forecast in different geographically wide Wheat growing districts in Egypt. Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) with temperature were used in the modeling. The model includes three sub-models representing the correlation between the reported yield and each individual variable. Simulation results using district statistics showed high accuracy of the derived correlations to estimate wheat production with a percentage standard error (%S.E.) of 1.5% in El- Qualyobia district and average (%S.E.) of 7% for the whole wheat areas.

• Structural Engineering and Mechanics
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• v.42 no.2
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• pp.153-174
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• 2012

Micromechanical facture models can be used to predict ductile fracture in steel structures. In order to calibrate the parameters in the micromechanical models for the largely used Q345 steel in China, uniaxial tensile tests, smooth notched tensile tests, cyclic notched bar tests, scanning electron microscope tests and finite element analyses were conducted in this paper. The test specimens were made from base metal, deposit metal and heat affected zone of Q345 steel to investigate crack initiation in welded steel connections. The calibrated parameters for the three different locations of Q345 steel were compared with that of the other seven varieties of structural steels. It indicates that the toughness index parameters in the stress modified critical strain (SMCS) model and the void growth model (VGM) are connected with ductility of the material but have no correlation with the yield strength, ultimate strength or the ratio of ultimate strength to yield strength. While the damage degraded parameters in the degraded significant plastic strain (DSPS) model and the cyclic void growth model (CVGM) and the characteristic length parameter are irrelevant with any properties of the material. The results of this paper can be applied to predict ductile fracture in welded steel connections.