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In this article, an artificial neural network (ANN) and a regression model are applied to forecast long term electricity consumption in Thailand. The inputs of both nonlinear models are gross domestic product, number of population. Maximum ambient temperature and electricity power demand are used as inputs in a neural network to predict electricity consumption. The results show that the ANN model can give more accurate estimations than regression model as indicated by the performance measures, namely coefficient of determination, mean absolute percentage error and root mean square error. Accoding to the forecasting results by the regression and ANN models of this study, the electricity consumption of the country in 2010, 2015, and 2020 will reach 160,136, 188,552, and 216,986 GWh, respectively, for the regression model while the ANN model will reach 155,917, 174,394, and 188,137 GWh, respectively.

Probe-type Coordinate Measuring Machines (CMMs) rely on the measurement of several discrete points to capture the geometry of part features. The sampled points are then fit to verify a specified geometry. The most widely used fitting method, the least squares fit (LSQ), occasionally overestimates the tolerance zone. This could lead to the economical disadvantage of rejecting some good parts and the statistical disadvantage of normal (Gaussian) distribution assumption. Support vector machines (SVMs) represent a relatively new revolutionary approach for determining the approximating function in regression problems. Its upside is that the normal distribution assumption is not required. In this research, support vector regression (SVR), a new data fitting procedure, is introduced as an accurate method for finding the minimum zone straightness and flatness tolerances. Numerical tests are conducted with previously published data and the results are found to be comparable to the published results, illustrating its potential for application in precision data analysis such as used in minimum zone estimation.

Complex forms such as conicity have been largely ignored in the coordinate form literature, in spite of the sufficient need to inspect them in parts such as tapered rollers in bearings. This paper attempts to develop guidelines for inspection of cones and conical frustums using probe-type coordinate measuring machines. The sampling problem, the path determination, and fitting of form zones are each addressed in detail. Moreover, an integrative approach is taken for form verification and detailed experimental analysis is conducted as a pilot study for demonstrating the need for the same. Three separate sampling methods are applied: Hammersley, Halton-Zaremba, and Aligned Systematic; at various sample sizes using sampling theory and prior work in two-dimensional sampling. Linear and nonlinear deviations are formulated using optimization and least-squared methods and solved to yield competitive solutions. Comprehensive experimental analysis investigated issues of model adequacy, nesting, interactions, and individual effects, while studying conicity as a response variable in the light of sampling strategies, sizes, cone surface areas, and fitting methods. In summary, an orderly framework for sampling and fitting cones is developed which can lead to the development of comprehensive standards and solutions for industry.