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Abstract：An electronic nose is a smart instrument that is designed to detect and discriminate among complex odors by using arrays of sensors. The arrays of sensors are treated with a variety of odor-sensitive biological or chemical materials. An electronic nose is a project that uses two researches areas which are hardware for developing sensors and software using theorem from neuron network technology. The operation begins when sensors hit the smell of beer. The result is converted from analog to digital and represented in a graph form. An artificial intelligence is a tool of a thinking system which can create knowledge as if a human does.This project concerns training and testing beer by using 10 types of beer which are Asahi, Chang, Cheer, Samiguel, Singha, Kloster, Heineken, Leo, Tiger and Tai. We separate the experiment into two parts. The first part is immediate checking, which is performed immediately after the beer can is opened. The second part is to check the beer after the can is opened for 24 hours.This project consists of two data classifications which are Rule base and Neural Network. Rule base is used to classify unknown data. Neural network is used to check types of beer. Our structure in a neural network consists of 25 input nodes, 28 hidden nodes, and 10 output nodes. The percentage of correctness is equal to 87.5%.

This research applied a genetic algorithm in the pattern of cellular automata and through Conway's rules of the game of life, to generate a system of printed Thai character recognition. The system consisted of two main parts, namely, recognition training and recognition testing. The printed character images fed to the first part were derived from standard character patterns widely used in a computer currently totalling 72, 864 characters. As for the images used for recognition testing, they were captured from a computer screen and stored in BMP pattern, amounting to 1,015 characters. The findings in this research revealed that the database used was of large size and data was transformed from a table frame of 64 x 64 pixels to be stored in the form of bit strings. A table size of 64 x 64 pixels was used to enable a wide variety of distribution patterns of the stable state of each character, making its identity more obvious. This, of course, caused a modification process in each generation till the final generation which took a long time while the database was used to represent the population of the final generation of each character must be large enough for the bit string used to represent these characters. This would enable the system to recognize a character based on its frequency with the largest number of those bit string patterns. Out of 1,015 printed Thai characters tested, it was found that the system could recognize (accept) 986 characters or 97.14%, while rejecting 6 characters or 0.59% and misrecognizing 23 characters or 2.27%. The recognition speed is 85 seconds per character on the average.