Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
In this research it is applied a pattern recognition system by using an artificial neural networks to recognize several samples on weaving products, such as plain weave, twill weave, and sateen weave. In order to extract textural characteristics or features from sample images, it is used the Neighboring Grey Level Dependence Matrix (NGLDM)-method as proposed by Sun, which is invariant under rotation and linear grey level transformation. Five textural features i.e. Small Number Emphasis, Large Number Emphasis, Number Non uniformity, Second Moment, and Entropy will be used as the representative features of sample images. Those features are used as input to the neural networks, which have learned by the back propagation method. Baths methods (continuous and periodic) for changing the interconnection weights, and the performances of the two types of neuron transfer functions are also observed and investigated, in order to obtain an optimal network configurations. The results of experiment will be very useful for the next stage of research in designing an integrated vision system for the recognition of weaving product's quality in textile industry.

Abstrak :
Variasi promotor tekstural dan komposisi katalis telah dilakukan untuk melihat pengaruhnya pada reaksi dekomposisi katalitik metana. Dalam penelitian ini, promotor tekstural ditambahkan ke dalam katalis yang berbasis Ni dan Cu dengan metode preparasi impregnasi. Selanjutnya katalis dimasukkan ke dalam reactor unggun tetap yang terpasang online dengan alat kromatografi gas. Temperatur dalam reactor sebesar 700°C. Variasi yang dilakukan adalah pada textural promoter yang digunakan dan komposisinya. Hasil yang dianalisis adalah karakter katalis, produk konversi, dan hasil karbon nanotube yang dihasilkan. Dari penelitian yang telah dilakukan diketahui bahwa promotor tekstural terbaik adalah Al2O3, selanjutnya variasi komposisi menunjukan bahwa komposisi terbaik Ni:Cu:Al2O3 = 56,8:27,1:16,1. ......Variation of textural promoter and catalyst compositions have been performed to see its effect on catalytic decomposition of methane reaction. In this study, the textural promoter was added to the catalysts based on Ni and Cu with impregnation preparation method. Further catalyst was inserted into a fixed bed reactor directly connected with gas chromatography equipment. Temperature in the reactor was 700°C. The results that were being analyzed were the character of catalyst, conversion products, and the carbon nanotubes which were produced. From the research that has been done, it is known that the best textural promoter is Al2O3. Further the variations of compositions showed that the best composition of Ni: Cu: Al2O3 were 56.8: 27.1:16.1