• Composites Research
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• v.19 no.5
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• pp.7-11
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• 2006

The improved SWNTs/PANi composite actuator films applicable to an artificial muscle were fabricated successfully using a new process of manufacture that consists of 90% pure single-walled carbon nanotubes (SWNT) and chemical polymerization. PANi is electrically conducting polyaniline polymer. The conductivities of the composite SWNTs/PANi film-type actuators and the pure PANi films fabricated were measured as 56.15 S/cm and 17.38 S/cm, respectively, by the 4-prove method. The conductivity of the composite actuator is 3.2 times higher than the pure PANi film. The fabricated composite actuator showed higher conductivity than any other similar ones. The quality of samples was investigated by an electron scanning microscope (SEM). To measure the actuating strains, a specially designed beam balance apparatus was developed and strains of the composite actuators was measured by a laser displacement sensor subjected to electric currents. During the operation, the sample was soaked in the $NaNO_3$ solution and the sine-wave voltage in the range of $+1V{\sim}-1V$ was applied. The length of the composite actuator changed from $l_0=12.690$ mm to $l_1=12.733$ so that the change of length was l=0.043 mm and the strain was 0.34 %. This is a very high strain for this kind of a composite actuator. Other result reported by Tahhan showed 0.23 % strain, so that the present result is improved by 48%.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.