The objectives of this research were to create a model for time sereries data prices and yields of natural rubber to be more accurate prediction and to compare the proposed model with the classical time series models such as the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model was a linear mixed model (LMM) consisting with trend autoregression outliers and sesonal terms. Dummy varialbles and Fourier seasonal effects were studied. Bayesian estimation was used and the estimated prices and yields were used for prediction. The results show that the proposed model with a Fouries seasonal effect is the most appropriated comparing with the dummy seasonal effect the Holt-Winters ES and the SARIMA having the least mean absolute values (MAE) both fiting and validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.

Thailand is an agricultural country. Most of the population consists of farmer. The rice is suitable to the climate of the country. And the main export of Thai is rice. The preparation of grain is also important.The farmer have to has an experience to choose the grain for growing the rice in farm. Good grian will give the high product. And good grain should completely free from contamination of disease and insect. Then it will be healthy seedlings. It is easy to harvest crops in a very high quality. Even it has good and high quality but it still has in impurities in the seed. Then the idea to create a project of sorting impurities from seeds. From the first it was built to help farmers to separate impurities such as grasses and husks. But the problem is the size of the maching is 140 x 150 x 160 cm which is larg and heavy then it makes the inconvenient to move. The preparation of the project is to develop an interest in sorting impurities in grain. The objective is to reduce the size to a smaller size than before. And increase the work more efficiency. The main aim is to sorting impurities in grain. Preparing the rice and put into the machin when power is opened. Rice will be flowing through the sieve which it works as a distribution and contamination. Then it will fail down into the sorting room. Impurities will be removed and the wind from the fan will blow the seeds. So we will get the good product and high quality. The results of the test. Wind is the best performance of the machine. Separating rice grains is 5.4 meters per second. The test of separating of the rice grains in 5 times and in the test of the 10 20 30 40 and 50 kg with a mixture of 10 percent of husk. the most effective of sorting rice is 10 kg. It can be isolated 99.30 percent with in 1.23 minutes.

This research aims to apply the principle of stochastic process for modeling. The parameters are estimated using Bayesian methods. The monthly average real price of paddy rice 15% and yield of paddy rice in Thailand were studied. The price and the yield of paddy rice which are time series data consisting of four components autocorrelation an exponential cumulative distribution function for trend outliers and two different types of seasons: the dummy seasons and Fourier function seasons. Writing algorithms programming in OpenBUGS and evaluating the performance of models from simulation programming in R were conducted. After that Bayesian methods with dummy seasons and Fourier function season were compared using RMSE MSE and MAE as the criteria. The results show that the Bayesian model with an exponential cumulative distribution function for trend and dummy seasons give the lowest RMSE MSE and MAE for both model fitting and model validating.

The purpose of this research was to construct the rotating bending fatigue testing machine for metal material to find out the built-in machine performance and compare it with machines that meet international standards. The machine built into a compact machine using weight knob for a heavy load in testing. It can test the force range 50-300 Newton (N) the motor is a 1 HP transmitter with a speed of 2800 rpm. Start by pressing the Start button to release the weight that sends power to the workpiece and attend a sensor counting is available to detect the lack of testing material. For the efficiency performance of the machine uses 10 piece steel shafts diameter of 8 mm according to the standard of the material fatigue tester by independent end bend rotation. For testing to compare with the test international standard machine also uses 10 piece steel shafts using force 150 N. For the analysis to test statistical hypotheses with the Minitab computer program at the significance level of 0.05 it was found that the values obtained from be made the rotating bending fatigue testing machine for metal material there is no difference with standard machines from statistically significant. All values are within the set limits. In conclusion the machine was created to be as effective as the international standard machine.

The objectives of this research are to propose a linear mixed model (LMM) for spatial time series with a seasonal component to apply the proposed model to monthly rubber yields in southern provinces of Thailand to forecast rubber yields in southern provinces of Thailand and to compare the performance of the proposed model to the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model is a linear mixed model (LMM) with spatial effects following a conditional autoregressive model (CAR model). Seasonal dummy variables and Fourier terms are two methods used to account for the seasonal effects. A Bayesian method is used for parameter estimation. The estimated monthly yields are used to forecast the monthly rubber yields. The dependent variables are the monthly rubber yields in each province. The effects considered are spatial effects heterogeneity effects and seasonal effects. The data are secondary data at a provincial level. The results show that the effects influencing on the amount of rubber yields are spatial heterogeneity and seasonal effects. The proposed model with sesonal dummy variables is the most appropriate model copared to the LMM with Fourier sesonal effects Holt-Withers ES and SARIMA. The mean absolute errors (MAE) are smallest in both model fitting and model validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.