• 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.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.709-716
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• 2020

Recently, the drone's equipment development and software technology have dramatically improved. With such developments, the applicability is increasing in various fields that require rapid geospatial information, and in practice, regulations and systems have been established, and the fields in use are increasing. Also, in Korea, corporations and public institutions are actually using and researching drones in fields such as aircraft development, communication technology development, construction site use, and surveying. However, there are no fields where drones are actually used in road construction sites. Therefore, in this study, to utilize drones that have been actively used in the civil engineering and construction fields for road construction, we investigated the current status of the Korea Highway Corporation's field drone use research and classified the possibility of drone introduction by road construction. Finally, a method of using drones at road construction sites was proposed to prepare a method for using drones at road construction sites.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.666-671
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• 2019

The flight controller(FC) used in small-sized drone was developed as simple structure does not perform complex operations because it uses different MCU with large-sized drone. Also, the balance control of small-sized drone should be simpler than Kalman filter using complex filter and the method using Complementary filter has relatively more operations. So, the method to realize the balance control on small-sized drone effectively using two-track control operating as proper method for above is suggested in this research. This method is a system maintaining effective balance with simple structure and less operations by operating adaptively for the unbalance of the drone with the acceleration sensor with the advantage which performing accurate correction by data processing for long term change and gyroscope sensor maintaining the balance of the drone by data processing for short term change. It is confirmed that stable operation was performed mostly based on the test result for repeatable test more than 100 times using two-track control and it maintained normal state operation more than 98% excluding the difficulty of maintaining normal operation when meets sudden and rapid wind yet.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• 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.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.18-25
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• 2020

This paper provides the basic data of low-altitude small drone management technology to solve the problem of drone's dysfunction that generally increases with the demand of the drone. Accordingly, various low-altitude small drone identification technology employed in many countries were investigated and analyzed. Herein, the research cases which have been developed to obtain diverse information such as the flight's plan, pilot's identity and contact number, and the flight's information such as the location and speed of small drones were mainly investigated. Furthermore, the list of the features of each case was analyzed. Moreover, the present paper suggests a drone identification system configuration which complements the problems of existing technologies and verifies the proposed system through a flight test.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.583-584
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• 2022

Due to the development of drones, drone missions are performed in various fields, and BVLOS (Beyond Visual Line Of Sight) flight is performed in a wide area. Most drones operate through radio frequency (RF) communication and can only fly in a limited radius of about 1-2 km. To overcome this, in this paper, we propose a multi-communication protocol-based drone control system to control drones performing missions in BVLOS using RF and LTE (Long Term Evolution). The proposed system consists of a control unit and a drone unit. The control unit transmits one control signal generated from the remote controller through RF and LTE. The drone unit classifies the control signal transmitted through RF and LTE according to the priority of the communication protocol and delivers it to the FC (Flight Controller). Through the proposed control system, it is possible to overcome the RF communication distance limit and prevent the communication disconnection situation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.112-122
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• 2024

Recently, as unmanned aerial vehicle technology such as drones has developed, there are many environmental, social and economic benefits, but if there is malicious intent against important national facilities such as airports, public institutions, power plants, and the military, it can seriously affect national safety and people's lives. It can cause damage. To respond to these drone threats, attempts are being made to introduce detection equipment such as RF scanners. In particular, power transmission towers installed in substations, power plants, and Korea's power system can affect detection performance if the transmission tower is located in the RF scanner detection path. In the experiment, a commercial drone was used to measure the signal intensity emitted from the drone and confirm the attenuation rate. The average and maximum attenuation rates showed similar trends in the 2.4 GHz and 5.8 GHz bands, and were also affected by the density of the structure.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.397-403
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• 2024

North Korea's rapidly advancing drone development and operational capabilities have become a significant threat to South Korea's security. The drone incursions by North Korea in 2014, 2017, and 2022 demonstrate the technological advancement and provocative potential of North Korean drones. This study aims to closely analyze the military threats posed by North Korean drones and seek effective countermeasures. The research examines the development level of North Korean drone technology, its military applications, the characteristics and patterns of recent drone incursions, the adequacy and limitations of South Korea's current response systems, and future countermeasures. For this purpose, domestic and international research literature and media reports were reviewed, and specific North Korean drone incursion cases were analyzed. The results indicate that North Korea's small drones possess technological features such as small size, low altitude, low-speed flight, long-duration flight, and reconnaissance equipment. These drones pose threats that can be utilized for reconnaissance, surveillance, surprise attacks, and terrorism. Additionally, South Korea's current response systems reveal limitations such as inadequate detection and identification capabilities, low interception success rates, lack of an integrated response system, and insufficient specialized personnel and equipment. Therefore, this study suggests various technical, policy, and international cooperative countermeasures, including the development of drone detection and identification technologies, the utilization of diverse drone neutralization technologies, the establishment of legal and institutional foundations, the construction of a cooperative framework among relevant agencies, and the strengthening of international cooperation. The study particularly emphasizes the importance of raising awareness of the North Korean drone threat across South Korean society and unifying national efforts to respond to these threats.