Abstract: The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p1), fractured layer (p2), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable gr (gravitational resistance) can be deduced from the elevation and apparent resistivity pa. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.
Abstract: This paper presents the experimental results of
discharge current phenomena on various humidity, temperature,
pressure and pollutant conditions of epoxy resin specimen. The
leakage distance of specimen was 3 cm, that it was supplied by high
voltage. The polluted condition was given with NaCl artificial
pollutant. The conducted measurements were discharge current and
applied voltage. The specimen was put in a hermetically sealed
chamber, and the current waveforms were analyzed with FFT.
The result indicated that on discharge condition, the fifth
harmonics still had dominant, rather than third one. The third
harmonics tent to be appeared on low pressure heavily polluted
condition, and followed by high humidity heavily polluted condition.
On the heavily polluted specimen, the peaks discharge current points
would be high and more frequent. Nevertheless, the specimen still
had capacitive property. Besides that, usually discharge current
points were more frequent. The influence of low pressure was still
dominant to be easier to discharge. The non-linear property would be
appear explicitly on low pressure and heavily polluted condition.