• ETRI Journal
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• v.45 no.5
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• pp.781-794
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• 2023

Uncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV-BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV-BS system that utilizes a high-capacity wireless backhaul operating in millimeter-wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.1
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• pp.25-33
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• 2020

Multipath routing protocols with unmanned aerial vehicles have been proposed to improve reliability in tactical ad hoc networks. Most of existing studies tend to establish the paths with multiple metrics. However, these approaches suffer from link loss and congestion problems according to the network condition because they apply same metric for both ground and air path or employ the simple weight value to combine multiple metrics. To overcome this limitation, in this study, we propose new routing metrics for path over unmanned aerial vehicles and use the multi-criteria decision making (MCDM) method to determine the weight factors between multiple metrics. For the case studies, we extend the ad-hoc on-demand distance vector protocol and propose a strategy for modifying the route discovery and route recovery procedure. The simulation results show that the proposed mechanism is able to achieve high end-to-end reliability and low end-to-end delay in tactical ad hoc networks.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.14-21
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• 2017

A Linear parameterized Sigma-Pi neural network (SPNN) is applied to a tilt-duct unmanned aerial vehicle (UAV) which has a very large longitudinal stability ($C_{L{\alpha}}$). It is uncontrollable by a proportional, integral, derivative (PID) controller due to heavy stability. It is shown that the combined inner loop and outer loop of SPNN controllers could overcome the sluggish longitudinal dynamics using a method of dynamic inversion and pseudo-control to compensate for reference model error. The simulation results of the way point guidance are presented to evaluate the performance of SPNN in comparison to a PID controller.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Journal of Aerospace System Engineering
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• v.3 no.1
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• pp.36-41
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• 2009

Quad-rotor is one kind of a rotorcraft in Unmanned Aerial Vehicle (UAV), which consists of four rotors in total and fixed-pitch blades located at the four corners. This vehicle is emerging as popular platform for UAV research due to the simplicity of its construction, the ability of hovering and the vertical take-off and landing (VTOL) capability, etc. Because of those specific capabilities, this vehicle can be applied to many fields: search and rescue, mobile sensor networks, fire observation, etc. However a quad-rotor is much affected by the disturbance due to the characteristics of structure. So this vehicle needs attitude control for stabilizing. In this paper, we design the control law for automatic stabilization. The PID controller is used to control a brushless DC motor. And an accelerometer is used to measure the roll and pitch angles of a quad-rotor.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.202-210
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• 2018

The modern battlefield is being developed as a network-centric warfare where priority is given to rapid status grasp and power deployment through scientification and modernization. Therefore, tactical data link has been continuously improving the network speed, and recently, security technology is required for wireless communication with the UAV and various devices for reconnaissance. In addition, the future information warfare will utilize advanced IT technology positively. Efforts are needed to integrate various systems and networks. However, these efforts are meaningful only when they can assume sufficient security in a newly changing information and communication environment. In this paper, we propose a new cryptographic synchronization for link encryption suitable for tactical data links. The proposed cryptographic synchronization is useful for T4 UAV link encryption, and it is also adaptable for lower BER, then we analyze the performances analysis of that.