• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.19-26
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• 2004

This paper deals with the automatic flight control system for an unmanned target drone which is operated by an army as an anti-air gun shooting training. By automation of unmanned target drone that is manually operated by external pilot, pilot can reduce workload and an army can reduce the budget. Most UAVs which are developed until today use high-cost sensors as AHRS and IMU to measure the attitude, but those are contradictory for the reduction of budget. This paper says the development of low-cost automatic flight control system which makes possible of automatic flight with low-cost sensors. We have developed the integrated automatic flight control system by integrating electricity module, switching module, monitoring module and RC receiver as an one module. We also prove the performance of automatic flight control system by flight test.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.133-143
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• 2023

As drones (also known as UAV) become popular with advanced information and communication technology (ICT), they have been utilized for various fields (agriculture, architecture, and so on). However, malicious attackers with advanced drones may pose a threat to critical national infrastructures. Thus, anti-drone systems have been developed to respond to drone threats. In particular, remote identification data (R-ID)-based UAV detection and identification systems that detect and identify illegal drones with R-ID broadcasted by drones have been developed, and are widely employed worldwide. However, this R-ID-based UAV detection/identification system is vulnerable to security due to wireless broadcast characteristics. In this paper, we analyze the security vulnerabilities of DJI Aeroscope, a representative example of the R-ID-based UAV detection and identification system, and propose a replay-attack-based neutralization method using the analyzed vulnerabilities. To validate the proposed method, it is implemented as a software program, and verified against four types of attacks in real test environments. The results demonstrate that the proposed neutralization method is an effective neutralization method for R-ID-based UAV detection and identification systems.

• Journal of Life Science
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• v.30 no.5
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• pp.428-433
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• 2020

In this study, we created pupal stage extracts of Apis mellifera L. drones for use in cosmetic materials. The effect of the drone pupae extract (DPE) on HDF cells was assessed for analysis of anti-wrinkle activity by collagen or collagenase gene expression, and the skin-lightening effect was studied by in vitro tyrosinase inhibition and B16F10 melanoma assay; the two cells were found to be non-cellular when the concentration of DPE was 100 ㎍/ml. Albutin concentration (positive control) in the whitening test was set at a capacity of 100 ug/ml and m-melanocyte stimulating hormone (α-MSH). A melanin-producing induction material was set at a concentration of 100 nM, and the expression of collagen type I and MMP1 collagenase was measured using HDF cells. MMP1 expression was seen to reduce in a concentration-dependent manner in treatment with DPE. Inhibiting melanin generation with B16F12 cells indicated a tendency to decrease in the DPE treatment group. Both L-Tyrosine and L-DOPA as DPE were used in an in vitro tyrosinase induction test to demonstrate the effects of tyrosinase suppression on concentrations. The higher the concentration of DPE, the greater the wrinkle reduction and whitening effect. In conclusion, it was found that DPE is an effective smoothing and whitening material by increasing collagen generation and inhibiting collagenase expression and reducing melanin production.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.