Seonghyun Kim

Forest fire monitoring system based on aerial image

Since natural disaster annually leads to casualties and property damages, developments for ICT-based disaster management techniques are fostering to minimize economic and social losses. For this reason, it is essential to develop a customized response technology for a natural disaster. In this paper, we introduce a smart-eye platform which is developed for disaster recognition and response. In addition, we propose a deep-learning based forest fire monitoring technique, which utilizes images acquired from an unmanned aerial vehicle with an optical sensor. Via training for image set of past forest fires, the proposed deep-learning based forest fire monitoring technique is designed to be able to make human-like judgement for a new input image automatically whether forest fire exists or not. Through simulation results, the algorithm architecture and detection accuracy of the proposed scheme is verified. By applying the proposed automatic disaster recognition technique to decision support system for disaster management, we expect to reduce losses caused by disasters and costs required for disaster monitoring and response.

Deep neural networks for wild fire detection with unmanned aerial vehicle

Wildfire threatens peoples lives and livelihoods. Wildfires kill hundreds of thousand people worldwide each year. Disaster information services are required to save lives and reduce economic losses when wildfire occurs. However, manned airplanes are too expensive to operate for frequent wildland monitoring. Satellite images cannot be used for early wildfire detection due to low temporal resolution and low spatial resolution. Unmanned aerial vehicles are cost-effective means to provide high resolution images for wildfire detection. A wildfire detection system utilizing unmanned aerial vehicles was developed with deep convolutional neural networks. The system achieved high accuracy for wide range of aerial photographs.

Electroluminescent properties of polymeric light-emitting devices with triple-barrier structure

Abstract We report the multi-color electroluminescence (EL) properties of light-emitting diodes (LEDs) with the triple-barrier structure consisting of ITO/ALOx/Poly[2,5-bia(dimethyloctylsilyl)-1,4-phenylene-vinylene]/ALOx/poly[2-methoxy-5-(2′-ethylhexyoxy)-1,4-phenylene-vinylene]/ALOx/Al. In comparison with the LED of stacked-polymers structure without aluminum oxide (ALOx), the device shows the multi-color EL behaviors clearly and the enhanced external quantum efficiency. This is due to the efficient confinement of carriers in the polymer layers by the energy barriers of ALOx.