This study was performed to classify the acoustic emission (AE) signal due to surface check and water movement of the flat-sawn boards of oak (Quercus Variablilis) during drying using the principle component analysis (PCA) and artificial neural network (ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count, event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 % of the variance of AE parameters could be accounted for by the first and second principal components. An ANN was successfully used to classify the AE signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.
In this study, microwave free-space transmission technique was applied to measure the
moisture content of powdered food (wheat flour, milk powder, and coffee powder). In frequency
range from 1 to 15 GHz, the microwave attenuation and phase shift due to moisture content of food
samples were measured and analyzed using vector network analyzer, double rigid horn antennas, and
sample holder filled with moist food samples. To estimate the relationship between moisture density
of powdered food and the attenuation and phase shift, correlation analysis was performed. The
correlation coefficients at each food sample were greater than 0.91. The calibration equation for
moisture content measurement having attenuation and phase shift as independent variables at 15 GHz
was developed and evaluated. The coefficient of determination and root mean square for all food
samples were 0.974 and 0.328 % respectively.
This study was conducted to analyze the ultrasonic transmitted signal for apple using wavelet transform. Fruit consists of non-linear visco-elastic properties such as flesh, an ovary and rind and hence most ultrasonic wave is attenuated and its frequency is shifted during passing the fruit. Thus it was not easy to evaluate the internal quality of the fruit using typical ultrasonic parameters such as wave velocity, attenuation, and frequency spectrum. The discrete wavelet transform was applied to the ultrasonic transmitted signal for apple. The magnitude of the first peak frequency of the wavelet basis from the ultrasonic transmitted signal showed a close correlation to the storage time of apple.
Conventional eddy current bobbin probes, multi-pancake and/or rotating pancake probes,
and transmit-receive eddy current probes are currently utilized in testing metal tubing. Each method
has respective strengths and weaknesses. This paper proposes another eddy current probe with new
features. The structure is designed to be sensitive to circumferential cracks, which are not easily
detected with ordinary bobbin coil probes. The directions of the magnetic field and the eddy current
around the coil were considered in design of the probe structure. Signals of these probes from the
artificial defects were acquired and analyzed. Experimental results show that the developed probes
are more sensitive to circumferential defects than comparable ordinary bobbin probes. In addition, the
new probes are insensitive to axial defects. By employing both the new probes and ordinary bobbin
probes, more reliable ECT can be performed.
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