Hong Sok Kim

Generalized logit model of demand systems for energy forecasting

A number of different versions of the Generalized Logit (GL) model have been applied in the literature, and the primary objective of the paper is to determine which one is the best. Using annual data for energy demand in the USA at the state level, the final model selected is similar to a simple form that was originally proposed by Considine and Mount (1984). A second objective of the paper is to demonstrate that the estimated elasticities are sensitive to the units specified for prices, and to show how price scales should be estimated as part of the model. The GL models of demand systems for energy and other factors have been shown to work well in comparison with other popular models, such as the Almost Ideal Demand System and the TransLog model. The main reason is that the derived price elasticities are robust when expenditure shares are small.

The band selection algorithm in supervised classification using mixed-pixels and canonical correlation analysis

The commonly used methods for the optimum band selection in supervised classification of multi-spectral data are Divergence, Transformed divergence (TD) and Jeffreys-Matusita distance (JM distance). But those methods might be ineffective when there is a need to change in the number of bands used and some spectral information in multi-spectral data can be redundant in classification process. This study introduces new algorithm with bands variables set and classes variables set, the canonical correlation analysis is made use of feature classification. Using the canonical cross-loadings we can orderly identify the bands correlation that largely affects the remotely sensed data. To verify the suitability of the new algorithm, the classifications using the each best band combination through TD, JM distance and new method were performed and the accuracy was assesed. As a result of classification accuracy assessment, overall accuracy and k A for the new method were superior to TDs and had competitive results to JM distance method.