• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.179-185
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• 2017

Communication technologies hold a significant place in the swarm flight of drones for surveillance, inspection of disasters and calamities, entertainment performances, and drone collaborations. A GCS(ground control station) for the control of drone swarms needs its devoted communication method to control a large number of drones at the same time. General drone controllers control drones by connecting transmitters and drones in 1:1. When such an old communication method is employed to control many drones simultaneously, problems can emerge with the control of many transmitter modules connected to a GCS and frequency interference among them. This study implemented a transmitter controller to control many drones simultaneously with a communication chip of 2.4GHz ISM band and a Cortex M4-based board. It also designed a GCS to control many transmitter controllers via a network. The hierarchical method made it possible to control many more drones. In addition, the problem with frequency interference was resolved by implementing a time- and frequency-sharing method, controlling many drones simultaneously, and adding the frequency hopping feature. If PPM and S.BUS protocol features are added to it, it will be compatible with more diverse transmitters and drones.

• 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 the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.53-60
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• 2017

This paper is to design and implement the drone that supports a wideband multimedia communication and a long-range to save people in maritime. The drone is an Unnamed Aerial Vehicle (UAV) that is controlled by a radio wave not by people boarding the machine. We use the drone to respond quickly to the boating accident. To develop a smart drone for the high speed seamless video streaming in a long-range maritime, a necessary techniques are hardware design techniques that design structure of a drone, controlling techniques that operate a drone and communication techniques that control a drone in a long distance. In this paper, the limitations and techniques to design and implement the structure of drone supporting wideband multimedia communication for long-range maritime are explained. By expanding this communication drone network, it is aimed at improving utility of a drone.

• Journal of Institute of Convergence Technology
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• v.6 no.1
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• pp.43-47
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• 2016

In this paper, we report a work-in-progress about development of multi-drone system for smart agriculture. The multi-drone system is controlled via a haptic teleoperation by a human operator. The purpose of the multi-drone system is that let the human operator to easily handle the multiple drones which are maintaining a fixed formation using ZigBee communication network.

• Annual Conference of KIPS
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• 2019.05a
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• pp.60-61
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• 2019

Drones are opening new horizon as a major Internet-of-Things (IoT) player which is a network of objects. Drone needs to charge itself during providing services from the charging stations. If there are lots of drones and one charging station, then it is a critical situation to decide which drone should get charged first and make order of priorities for drones to get charged sequentially. In this paper, we propose an efficient scheduling algorithm for drone power charging (ESADPC), in which charging station would have a scheduler to decide which drone can get charged earlier among many other drones. Simulation results have showed that our algorithm reduces the deadline miss ration and turnaround time.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.55-64
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• 2019

In drone-assisted wireless sensor networks, drones collect data from sensors in an energy-efficient manner and quickly distribute urgent information to sensor nodes. This article introduces recent communication and security schemes for drone-assisted wireless sensor networks. For the communication schemes, we introduce data collection optimization schemes, drone position and movement optimization schemes, and drone flight path optimization schemes. For the security schemes, we introduce authentication and key management schemes, cluster formation schemes, and cluster head election schemes. Then, we present some enhancement methodologies for these communication and security schemes. As a conclusion, we present some interesting future work items.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.20-29
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• 2021

This study aims to explore the issue of drones and crime in newspaper articles. BIG KINDS, an online news archive of the Korea Press Foundation, collected 1,213 newspaper articles that met the terms of "drone" and "crime" in 11 central and 28 regional comprehensive newspapers between January 1, 1990 and May 1, 2021. Among them, we perform keyword frequency, centrality analysis, network structure construction, CONCOR analysis, and density matrix analysis on 117 key keywords. According to the analysis, the main issues were classified into eight, and the report analysis on drones and crimes in newspaper articles showed that the government's policy-making and social problems on protecting people's privacy, preventing illegal filming, securing navigation safety, social security and resolution. This study attempts to expand the field of humanities and social studies related to drones and crime, and specifically suggests the current status and counterplan against drone-related crimes as policy implications and media implications.

• The Journal of Bigdata
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• v.9 no.1
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• pp.115-126
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• 2024

The scale and cost of disasters are increasing globally, emphasizing the importance of logistics activities in disaster response. A disaster response logistics system must place logistics hub centers in regions relatively safe from disasters and ensure the stable supply of relief goods and emergency medicines to the affected areas. Therefore, this study focuses on locating drone delivery centers that minimize disaster vulnerability when designing a disaster response delivery network. To facilitate the transport of relief supplies via drones, the maximum delivery range of drones is considered and we employed a natural disaster vulnerability index to develop optimization models for selecting drone delivery center locations that minimize disaster vulnerability. The analysis indicates that while the optimization models to minimize disaster vulnerability increase the number of hub investments, these approaches mitigate disaster vulnerability and allows the safe and effective operation of a disaster response logistics system utilizing drone deliveries.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.