• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.703-710
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• 2010

In this study, a tactile stimulator was developed to resolve some problems from the previous version of the system such as system configuration, inappropriate stimulation control and additional problems. The developed tactile stimulator consists of control unit, drive unit and vibrator unit. The control unit was controlled by E-Prime software to generate appropriate vibration pulses. The drive unit supplies enough energy to the vibrator to generate effective stimulation pulses. The vibrator unit consists of small coin type vibrator and velcro, and was made to be attached at the hand easily. The developed tactile stimulator was designed by small-size, light-weight, low-power, simple-fabrication, max 35 channels and little delay time from instruction signal of E-Prime software to vibrator. The duration and magnitude of stimulation was controlled by 10 grades and the problems concerning stimulation control were compensated by wideband frequency ranges. Additionally, the electrical safety was ensured by low voltage operation. Vibrator was made to be attached on finger as well as on any part of the subject. Since this tactile stimulator is developed based on E-Prime software which is widely used in cognitive science, it is believed that this stimulator be suitable for the wide application of cognitive science study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.127-134
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• 2019

With the advancement of IT technology, the amount of data generated has been growing exponentially every year. As an alternative to this, research on distributed systems and in-memory based big data processing schemes has been actively underway. The processing power of traditional big data processing schemes enables big data to be processed as fast as the number of nodes and memory capacity increases. However, the increase in the number of nodes inevitably raises the frequency of failures in a big data infrastructure environment, and infrastructure management points and infrastructure operating costs also increase accordingly. In addition, the increase in memory capacity raises infrastructure costs for a node configuration. Therefore, this paper proposes an in-memory-based hybrid big data processing scheme for improve the big data processing rate. The proposed scheme reduces the number of nodes compared to traditional big data processing schemes based on distributed systems by adding a combiner step to a distributed system processing scheme and applying an in-memory based processing technology at that step. It decreases the big data processing time by approximately 22%. In the future, realistic performance evaluation in a big data infrastructure environment consisting of more nodes will be required for practical verification of the proposed scheme.