• Journal of the Korean Solar Energy Society
• /
• v.39 no.6
• /
• pp.113-125
• /
• 2019

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R² and RMSE between observed data and the curve was significantly reduced.

• Journal of the Korean Geosynthetics Society
• /
• v.15 no.2
• /
• pp.65-75
• /
• 2016

In this study, we constructed a database by collecting field pullout test data of the soil nailing using pressurized grouting, and suggested a method to estimate the ultimate pullout resistance using nonlinear regression analysis to overcome the problems of ultimate pullout resistance estimation using graphical methods. The load-displacement curve estimated by nonlinear regression showed a very high correlation with the field pullout test data. Estimated ultimate pullout load by nonlinear regression method was average 29% higher than estimated ultimate pullout load using previous graphical method. A sigmoidal growth model was found to be the best-fitting nonlinear regression model against rapid pullout failure. Further, an asymptotic regression model was found to be the best fit against progressive nail pullout. The unit ultimate skin friction suggested in this research reflected in the domestic geotechnical characteristics and the specifications of the pressurized grouting method. This research is expected to contribute towards establishing an independent design standard for the soil nailing by providing solutions to the problems that occur when using design charts based on foreign research.

• Journal of the Korean Statistical Society
• /
• v.31 no.1
• /
• pp.63-75
• /
• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• The KIPS Transactions:PartD
• /
• v.11D no.3
• /
• pp.597-604
• /
• 2004

Accurate software cost estimation is essential to both developers and customers. Most of the cost estimating models based on the size measure methods, such as LOC and FP, are obtained through size estimation. The accuracy of size estimation directly influences the accuracy of cost estimation. As a result, the overall structure of regression-based cost models applies the power function based on software size. Many growth phenomenon in nature such as the growth in living organism, performance of technology, and learning capability of human show an S-shaped curve. This paper proposes a model which estimates the developing effort by using the growth curve. The presented model assumes that the relation cost and size follows the growth curve. The appropriateness of the growth curve model based on Function Point, Full-Function Point and Use-Case Point, which are the general methods in estimating the software size have been confirmed. The proposed growth curve model shows similar performance with power function model. In conclusion, the growth curve model can be applied in the estimation of the software cost.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.2
• /
• pp.137-142
• /
• 2013

The indiscriminate use of the fossil fuel has caused serious environmental pollutions such as the shortage of energy and global warming. Microalgae have being emphasized as $3^{rd}$ generation biomass which makes the carbon dioxide reduce effectively as well as produces the biofuel. Large scale production of microbial biomass by continuous culture is a quite challenging issue, because off-line optimization strategies of a microbial process utilizing a model-based scheme give rise to many difficult problems. In this paper, the static and simple control method which was able to be applied in time-variant growth environment and large scale of algae culture was studied. The significant disturbances in on-line measurement of cell density were reduced by Savitzky-Golay FIR smoothing filter. Dunaliella salina was cultivated continuously in a flat panel photobioreactor by the on-off control of the turbidostat process.

• Journal of Bio-Environment Control
• /
• v.26 no.4
• /
• pp.235-241
• /
• 2017

This study was carried out to estimate growth characteristics of Kimchi cabbage cultivated in two different growing seasons and three transplanting dates in the greenhouses, and to create a predicting model for the production of Kimchi cabbage based on the growth parameters and climatic elements. Kimchi cabbages were transplanted three times at intervals of two weeks in spring and autumn growing seasons. Sigmoidal models for the estimation of fresh weight (Fw) was designed with days after transplanting, which were Fw=4451.5/[1+exp{-(DAT-34.1)/3.6}]($R^2=0.992$) and Fw=7182.0/[1+exp{-(DAT-53.8)/11.6}] ($R^2=0.979$), respectively. The relationship between fresh weight of Kimchi cabbage and growing degree days (GDD) was highly correlated, and the regression model represented by Fw=4451.5/[1+exp{-(GDD-34.1)/3.6}] ($R^2=0.992$) in spring growing season. The yield of Kimchi cabbage under spring and autumn growing season were estimated 11348.3kg/10a and 15128.2kg/10a, respectively, which were much different than outdoor culture each growing season, while greenhouse cultivation have shown similar results. To estimate the efficacy of prediction yield in Kimchi cabbage, we will need to supplement a predicting model, which was based on the parameters and climatic elements by the field cultivation.