The purposes of this research were to study and develop veneer sheets from sawdust wood for furniture decoration in residential housing with the study from the test of sampling group of sawdust wood scraps from wood conversion sawmill factories in the District of Bang Sue Bangkok Metropolis Province with appropriate qualifications ideal to enter the conversion process with the research results demonstrated hereunder. The results of the research were with findings in respect of Production Analysis Standard Criteria in respect of Beauty And Satisfaction of the consumers of the furniture in the residential housing from veneer sheets made of sawdust wood newly developed being with the mean of 4.36 rated as Good Level. Consecutively in respect the Production with the mean of 3.86 rated as Good Level. Eventually the least in respect of Beauty with the mean of 3.59 rated as Good Level and Product User respects being with the mean of 4.36 rated as Good Level.

This study Aims to research and develop identity verification using freeware. For optimization of workflow systems authentication systems. This research is a research and software development. To be applied to the management system to verify identity. The development process of the study process the performance of the verification system . To software development It is a tool used for development . Program for the development of the Linux operating system . The results of research Such programs can be used to produce real target audience is the personnel of the agency. This research has made evaluating its use for system identification using freeware. Users have found that satisfaction with the program was good .

This research proposes a hidden Markov model for detecting machines with multiple fillers producing soft drink for quality control in order to reduce defective products. The proposed model is compared with a classical generalized estimating equations model (GEE). The monthly data the number of defective products the correctness and the time spending in correction were collected from a machine in a sample factory in Pratumthanee province. The two hidden states of the fillers are workable and unworkable. The states has a logistic distribution. The results show that the correctness of the filler error occurrence does not improve the probability of being in a unworkable state neither the period of time spending on correction. The current state seems to be the same as the previous state

The objective of this research is to select a proper cumulative function for trend in an analysis of simultaneous multiple time series data with autocorrelation trend and outliers using a Bayesian method. The proposed model is applied to fluctuate stock price data in the stock exchange of Thailand. The data consist of the closing prices of 5 stocks. The algorithms are written in OpenBUGS and the performance of the model is evaluated via a simulation study in R. The cumulative distribution functions considered here are a Weibull cumulative distribution function and an exponential cumulative distribution. They are compared using RMSE MAPE and MAE as criteria. The research found that an exponential cumulative function for trends gives smaller values of the three criteria in both model fitting and model validation for the4 stocks EA NWR and KTB. However a Weibull cumulative distribution function gives smaller values of three criteria for the PTT in both model fitting and model validation.

This research proposes a Bayesian logistic regression model which is applied to the data from autoparts manufacturing machines. Factors related to defective and bad products are investigated. The proposed model is compared with the logistic regression using maximum likelihood method for parameter estimation. The data were collected from 132 machines in an autoparts manufacturing factory. The research found that useful life machine type 6 worker group 3 and 4 working step 1 and 2 influence to the risk of producing defective and bad products. When the useful life is increased by 1 month the risk of producing defective and bad products will be increased by 2.2%. The risk that the machine type 6 will produce defective and bad products is 4.078 times greater than the risk that the machine type will do. The risk that the worker group 3 will produce defective and bad products is 61.7% less than the risk that the worker group 12 will do. The risk that the worker group 4 will produce defective and bad products is 61.5% less than the risk that the worker group 12 will do. The risk that the working step 1 will produce defective and bad products is 2.831 times greater than the risk that the working step 4 will do. The risk that the working step 2 will produce defective and bad products is 13.8 % greater than the risk that the working step 4 will do. The parameter estimates from the Bayesian logistic regression are very close to the ones from the logistic regression using maximum likelihood method for parameter estimation