• Polymer(Korea)
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• v.36 no.4
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• pp.494-499
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• 2012

In this work, the effect of multi-walled carbon nanotube (MWCNT) on electromagnetic interference shielding effectiveness (EMI SE) and mechanical properties of MWCNT-reinforced polypropylene (PP) nanocomposites were investigated with varying MWCNT content from 1 to 10 wt%. Electric resistance was tested using a 4-point-probe electric resistivity tester. The EMI SE of the nanocomposites was evaluated by means of the reflection and adsorption methods. The mechanical properties of the nanocomposites were studied through the critical stress intensity factor ($K_{IC}$) measurement. The morphologies were observed by scanning electron microscopy (SEM). From the results, it was found that the EMI SE was enhanced with increasing MWCNT content, which played a key factor to determine the EMI SE. The $K_{IC}$ value was increased with increasing MWCNT content, whereas the value decreased above 5 wt% MWCNT content. This was probably considered that the MWCNT entangled with each other in PP due to an excess of MWCNT.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.296-301
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• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).