• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.336-343
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• 2021

Avionics corresponds to the brain, nerves and five senses of an aircraft, and consists of aircraft mounted electronic equipment of communication, identification, navigation, weapon, and display systems to perform flight and missions. It occupies about 50% of the aircraft system, and its importance is increasing as the technology based on the 4th industrial revolution is developed. As the development period of the aircraft is getting shorter, it is definitely necessary to develop a stable avionics SIL in a timely manner for the integration and verification of the avionics system. In this paper, we propose a method to replace the legacy SIL with the avionics simulation model framework based one and evaluate the framework based on the result of alternative application.

• Water for future
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• v.53 no.7
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• pp.23-29
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• 2020

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.351-360
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• 2024

With the increasing use of drone technology, the Unmanned Aerial Vehicle (UAV) is now being utilized in various fields. However, this increased use of drones has resulted in various issues. Due to its small size, the drone is difficult to detect with radar or optical equipment, so acoustical tracking methods have been recently applied. In this paper, a method of localization of multiple drones using the acoustic characteristics of the quadcopter drone is suggested. Because the acoustic characteristics induced by each rotor are differentiated depending on the type of drone and its movement state, the sound source of the drone can be reconstructed by spatially clustering the results of the estimated positions of the blade passing frequency and its harmonic sound source. The reconstructed sound sources are utilized to finally determine the location of multiple-drone sound sources by applying the source localization algorithm. An experiment is conducted to analyze the acoustic characteristics of the test quadcopter drones, and the simulations for three different types of drones are conducted to localize the multiple drones based on the measured acoustic signals. The test result shows that the location of multiple drones can be estimated by utilizing the acoustic characteristics of the drone. Also, one can see that the clarity of the separated drone sound source and the source localization algorithm affect the accuracy of the localization for multiple-drone sound sources.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.77-81
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• 2018

Many studies that improve the efficiency of data collection and a network's lifetime by using a mobile sink have been conducted using wireless sensor networks. If a drone is used as a mobile sink, the drone can collect data more efficiently than can existing mobile sinks operating on the ground because the drone can minimize the effects of obstacles and the terrain. In this paper, we propose a rendezvous node selection scheme which considers estimated drone's trajectory and data collection latency of sensor networks for reliable data collection, when a drone whose trajectory is not predetermined works with terrestrial wireless sensor networks. A selected rendezvous node on the ground collects data from the entire network and it sends then collected data to the drone via direct communication. We also verify that the proposed scheme is more reliable than previous schemes without considering the drone's trajectory and data collection latency.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.67-73
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• 2021

In this paper, in order to prevent the fall of the drone, a study was conducted to collect vibration data from the motor connected to the propeller of the drone, and to predict the abnormal vibration of the drone using recurrent neural network (RNN) and long short term memory (LSTM). In order to collect the vibration data of the drone, a vibration sensor is attached to the motor connected to the propeller of the drone to collect vibration data on normal, bar damage, rotor damage, and shaft deflection, and abnormal vibration data are collected through LSTM and RNN. The root mean square error (RMSE) value of the vibration prediction result were compared and analyzed. As a result of the comparative simulation, it was confirmed that both the predicted result through RNN and LSTM predicted the abnormal vibration pattern very accurately. However, the vibration predicted by the LSTM was found to be 15.4% lower on average than the vibration predicted by the RNN.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.133-148
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• 2016

Voices demanding a revision of the aviation law on the operating drones are continuously rising high with the increase of their applicability in various industry fields. According to the current regulations, drones are permitted to fly under very strict conditions, which include limited places and the line-of-sight visibility from pilots. Because of the strict regulations, it is almost impossible for drones to be used in many industries such as parcel delivery services. To improve the business value of drones, we have to improve the accuracy of drones' positions and provide the proper protection levels in order to detect and avoid any risks including the collisions with the other drones. SBAS(Satellite Based Augmentation System) can support the aviation requirements with the accuracy and integrity so as to reduce the position errors and to calculate the protection levels of drones. In this paper, we assign the flight heights of drones according to the decision heights as per LAAS(Local Area Augmentation System) landing categories and conduct a simulation to predict the SBAS available time of the day.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.382-392
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• 2020

The use of unmanned aerial vehicle (UAV) have been regarded as a promising technique in both military and civilian applications. Nevertheless, due to the lack of relevant and regulations and laws, the misuse of illegal drones poses a serious threat to social security. In this paper, aiming at deriving the three-dimension optimal surveillance trajectories for group monitoring drones, we develop a group trajectory planner based on the particle swarm optimization and updating mechanism. Together, to evaluate the trajectories generated by proposed trajectory planner, we propose a group-objectives fitness function in accordance with energy consumption, flight risk. The simulation results validate that the group trajectories generated by proposed trajectory planner can preferentially visit important areas while obtaining low energy consumption and minimum flying risk value in various practical situations.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.393-407
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• 2020

In order to reduce the threat by illegal unmanned aerial vehicles, a tracking system based on sound was implemented. There are three main points to the drone acoustic tracking method. First, it scans the space through variable beam formation to find a sound source and records the sound using a microphone array. Second, it classifies it into a hidden Markov model (HMM) to find out whether the sound source exists or not, and finally, the sound source is In the case of a drone, a sound source recorded and stored as a tracking reference signal based on an adaptive beam pattern is used. The simulation was performed in both the ideal condition without background noise and interference sound and the non-ideal condition with background noise and interference sound, and evaluated the tracking performance of illegal drones. The drone tracking system designed the criteria for determining the presence or absence of a drone according to the improvement of the search distance performance according to the microphone array performance and the degree of sound pattern matching, and reflected in the design of the speech reading circuit.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.604-611
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• 2016

Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.153-158
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• 2017

Rendezvous point is used to gather the data from sensor nodes and send to sink node efficiently in selected area. It incurs a unbalanced energy consumption nearby the rendezvous point which can shorten the network life time shortly. Thus, it is very important to select the rendezvous point effectively among all sensors in order to not drain the battery of a sensor node in construction sites. In this paper, we propose a rendezvous point replacement mechanism which considers remaining energy of nodes to prolong the network lifetime. Also, for shortening the distance of drone at the same time, it increases the probability of the near-by drone node becoming rendezvous point. The simulation results show that the proposed scheme can significantly improve the network lifetime and the flight distance compared with the existing LEACH, L-LEACH algorithm.