• Fire Science and Engineering
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• v.25 no.5
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• pp.93-99
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• 2011

In the present study, a numerical simulation was conducted to estimate the prediction performance of FDS on the carbon monoxide production in the under-ventilated compartment fires. Methane and heptane fires located in the a 2/5 scale compartment based on the ISO-9705 standard room was simulated using FDS Ver. 5.5. Through the comparison between the computed results and the earlier published experimental data, the performance of FDS was estimated on the predictions of the combustion gases concentration in the hot upper layer of the compartment and the effects of CO yield rate on the estimation of CO production at local points were analyzed. From the results, it was known that FDS Ver. 5.5, in which the two-step reaction mixture fraction model implemented, was more effective on the prediction of CO concentration compared to the previous FDS version. In addition, controlling CO yield rate made the predicted CO concentration get closer to the experimental data for the fires of the under-ventilated condition.

• Food Science of Animal Resources
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• v.30 no.6
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• pp.905-909
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• 2010

Two sets of data on carcass traits and beef cut parameters were used to investigate the relationships between carcass and beef cut measurements, which can be used to make predictions of retail cut percentages. One set had a total of 1,141 measurements of Hanwoo cattle of three different sex origins, which were slaughtered in an abattoir located at the National Institute of Animal Science, RDA, Korea from 1996 to 2008. To develop prediction models for retail cut percentage with higher accuracies than the current model, another set consisting of a total of 13,389 records of carcass and beef cut traits were collected from 30 abattoirs and butcheries in Korea from 2008 to 2009. Bulls yielded heavier and leaner carcasses than steers. High correlation coefficients were estimated between amount of body fat and percent retail cut (-0.82) as well as between back fat thickness (BF) and percent retail cut (-0.62). The amount of retail cut, however, was highly correlated with body weight before slaughter (BW, 0.95) or with cold carcass weight (CWT, 0.94). Relationships between percent retail cut and measurable beef yield traits, BF, loin eye area (LEA) or CWT varied by sex class, which must be considered for development of a prediction model with high accuracy. Models of data for all breeds and sexes fit the effects of breed, sex, and interaction of abattoir by butchers, whereas models of data for each breed and sex fit the effect of interaction of abattoir by butcher only. Due to possible future changes in back fat control, we performed a log transformation of BF. Our new models fit better than the currently used model.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• Journal of Welding and Joining
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• v.31 no.5
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• pp.1-6
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• 2013

In carrying out the elastic-plastic analysis, four conditions (equilibrium equation, constitutive equation, condition of compatibility and yield condition) should be satisfied. In welding, the temperature largely changed from a melting temperature to a room temperature. So, yield stress of materials largely changed, too. In particular, yield stress becomes about zero over $700^{\circ}C$. The analysis should be carried out under the condition that equivalent stress generated in temperature increment ${\Delta}T$ did not exceed yield stress of materials at high temperature over $700^{\circ}C$. It should be sufficiently recognized that the obtained results were not reliable if this condition was not satisfied.

• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.257-266
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• 2014

The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.3
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• pp.315-320
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• 2002

Test-day records (n=13677) sampled from 896 ewes in 5-9 (${\mu}$=7.5) monthly test-days were used to estimate genetic and phenotypic parameters of test-day yields, lactation milk yield (TMY), length of the milking period (DAYS) and three measures of persistency of milk yield in Boutsico dairy sheep. Τhe measures of persistency were the slope of the regression line (${\beta}$), the coefficient of variation (CV) of the test-day milk yields and the maximum to average daily milk yield ratio (MA). The estimates of variance components were obtained under a linear mixed model by restricted maximum likelihood. The heritability of test-day yields ranged from 0.15 to 0.24. DAYS were found to be heritable ($h^2$=0.11). Heritability estimates of ${\beta}$, CV and MA were 0.15, 0.13, 0.10, respectively. Selection for maximum lactation yields is expected to result in prolonged milking periods, high rates of decline of yields after peak production, variable test-day yields and higher litter sizes. Selection for flatter lactation curves would reduce lactation yields, increase slightly the length of the milking period and decrease yield variation as well as litter size. The most accurate prediction of TMY was obtained with a linear regression model with the first five test-day records.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.4
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• pp.371-374
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• 1984

This study was conducted to establish the statistic model by use the soil physico-chemical properties for prediction of ginseng root yield. Twenty seven farmer's red ginseng fields from the ginseng growing area were chosen for this study. Root yield of 6-year old ginseng was $1.85{\pm}0.54Kg$ per $3.3m^2$, and it showed positive correlation between yield and porosity, content of clay, clay and silt, organic matter, cation exchange capacity of the field soils, respectively, but showed a negative correlation with available phosphate. Prediction of root yield was possible with equation combined with porosity($X_1$), content of clay($X_2$), clay and silt($X_3$), available phosphate($X_4$), CEC($X_5$), the equation is $Y=-1.175+0.033X_1-0.04X_2+0.012X_3-0.001X_4+0.171X_5$. Standard partial regression coefficients were 0.3799 in CEC, 0.1550 in content of clay, 0.0890 in porosity, 0.0599 in content of clay silt, and -0.0138 in available phosphate.