• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.15-17
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• 2021

This paper proposes a drone flight trajectory method for accurate air pollutant emission designation detection. In areas with many factories, such as industrial complexes, there are workplaces that illegally emit air pollutants in a situation where monitoring is neglected. In the past, studies have been actively conducted to measure air pollutants in these areas using drones. The measurement method using a drone uses a method of detecting pollution by stopping around the chimney of a factory, but it has a problem in that the detection of air pollutants is inaccurate depending on environmental factors such as air pressure and wind. Therefore, this paper proposes a drone flight trajectory method for accurate air pollutant emission designation detection. This paper devises a screw orbit flight method in which a drone flies upward while rotating the chimney, and the total area of the chimney is detected and measured considering environmental factors. In the experiment, our proposal shows a higher performance than the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.23-39
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• 2019

The use of drone-bots is demanded in times regarding the reduction of military force, the spread of the life-oriented thought, and the use of innovative technology in the defense through the fourth industrial revolution. Especially, the drone's surveillance and reconnaissance are expected to play a big role in the future battlefield. However, there are not many cases in which the concept of operation is studied scientifically. In this study, We propose search algorithms for reconnaissance drone through simulation analysis. In the simulation, the drone and target move linearly in continuous space, and the target is moving adopting the Random-walk concept to reflect the uncertainty of the battlefield. The research investigates the effectiveness of existing search methods such as Parallel and Spiral Search. We analyze the probabilistic analysis for detector radius and the speed on the detection probability. In particular, the new detection algorithms those can be used when an enemy moves toward a specific goal, PS (Probability Search) and HS (Hamiltonian Search), are introduced. The results of this study will have applicability on planning the path for the reconnaissance operations using drone-bots.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.364-370
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• 2020

This paper is related to detection and classification for micro-sized aircraft that flies at low-altitude. The deep-learning based method using sounds coming from the micro-sized aircraft is proposed to detect and identify them efficiently. We use MFCC as sound features and CNN as a detector and classifier. We've proved that each micro-drones have their own distinguishable MFCC feature and confirmed that we can apply CNN as a detector and classifier even though drone sound has time-related sequence. Typically many papers deal with RNN for time-related features, but we prove that if the number of frame in the MFCC features are enough to contain the time-related information, we can classify those features with CNN. With this approach, we've achieved high detection and classification ratio with low-computation power at the same time using the data set which consists of four different drone sounds. So, this paper presents the simple and effecive method of detection and classification method for micro-sized aircraft.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.164-174
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• 2019

This article presents low altitude small drone identification trends at home and abroad. To reduce the dysfunction caused by the proliferation of drones worldwide, there is a growing interest in remote identification technologies that can identify the basic information of the drone. First, this article introduces policy trends in major countries. US, Europe, and China have recently provided recommendations regarding technologies available for the remote identification and tracking of a drone. Next, standardization activities on identification communications and identification systems are introduced. For this, standards organizations for the small drone identification, such as the International Organization for Standardization, IEEE 802, Radio Technical Commission for Aeronautics, International Civil Aviation Organization, and $3^{rd}$ Generation Partnership Project, are investigated. Finally, drone identification technology trends are introduced. In the US and Europe, various drone identification technologies have been studied to identify a drone owner and drone registration information with a drone identifier. In South Korea, drone identification technology is still in its infancy, whereas drone detection and physical counterattack technologies are somewhat more developed. As such, major drone manufacturers are also currently studying and developing drone identification systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.63-66
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• 2019

Recently, as the technology of drones develops, a malicious attack using a drones becomes a problem, and an anti-drone technology for detecting an attack dron for a malicious attack is required. However, currently used drone detection systems are expensive and require a lot of manpower. Therefore, in this paper, we propose an anti - drone method using the analysis and algorithms of the anti - drone that can monitor the attack drones. In this paper, we identify and detect attack drones using sniffing, and propose capture and deception algorithm through spoofing using current GPS based detection system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1081-1087
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• 2015

Recently, the researches of aerial drones are actively executed in various areas, the researches of surface drones and underwater drones are also executed in marine areas. In case of surface drones, they essentially utilize acoustic information by the sonar and consequently have the local information in the obstacle avoidance as the sonar has the limitations due to the beam width and detection range. In order to overcome this, more global method that utilizes optical images by the camera is required. Related to this, the aerial drone with the camera is desirable as the obstacle detection of the surface drone with the camera is impossible in case of the existence of clutters. However, the dynamic-floating aerial drone is not desirable for the long-term operation as its power consumption is high. To solve this problem, a collaborative obstacle avoidance method based on the acoustic information by the sonar of the surface drone and the optical image by the camera of the static-floating aerial drone is proposed. To verify the performance of the proposed method, the collaborative obstacle avoidances of a MSD(Micro Surface Drone) with an OAS(Obstacle Avoidance Sonar) and a BMAD(Balloon-based Micro Aerial Drone) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.94-98
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• 2021

As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.