• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.3
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• pp.71-84
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• 2024

Artificial intelligence can be cited as a key linkage technology for expanding drones' application fields, and drones combined with artificial intelligence are expected to improve drones' operational capabilities based on algorithms that can solve complex tasks through learning. The purpose of this study is to analyze various latest research cases that apply deep reinforcement learning to drones to solve limitations for performing swarm flight and to propose a new research direction that applies them to multi-agent communication optimization technology. The process of the research is to investigate and analyze the methods for efficient operation of control and communication technologies required for swarm flight to be successful, and to apply algorithms that have the advantage of exchanging richer feedback between agents and having less learning than conventional methods when learning deep reinforcement learning algorithms. It is expected that the efficiency and performance of learning communication protocols optimized for swarm flight will be improved, which will increase the efficiency of mission performance when exploring or scouting large areas through swarm flight in the future.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1007-1011
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• 2020

Recently, natural and anthropogenic disasters have rapidly increased due to the on-going climate change and various human activities. Remote sensing (RS) technology enables the continuous monitoring and rapid detection of disastrous events thanks to its advantages covering vast areas at high temporal resolution. Moreover, RS technology has been very actively used in disaster monitoring and assessment since cluster- and micro-satellites and drones were introduced and became popular. In this special issue, nine papers were introduced, including the processing and applications of remote sensing data for monitoring, assessment, and prediction of various natural disasters. These papers are expected to serve as useful references for disaster management in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1080-1088
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• 2018

General methods of controlling a drone are divided into manual control and automatic control, which means a drone moves along the route. In case of manual control, a man should be able to figure out the location and status of a drone and have a controller to control it remotely. When people control a drone, they collect information about the location and position of a drone with the eyes and have its internal information such as the battery voltage and atmospheric pressure delivered through telemetry. They make a decision about the movement of a drone based on the gathered information and control it with a radio device. The automatic control method of a drone finding its route itself is not much different from manual control by man. The information about the position of a drone is collected with the gyro and accelerator sensor, and the internal information is delivered to the CPU digitally. The location information of a drone is collected with GPS, atmospheric pressure sensors, camera sensors, and ultrasound sensors. This paper presents an investigation into drone control by a remote computer. Instead of using the automatic control function of a drone, this approach involves a computer observing a drone, determining its movement based on the observation results, and controlling it with a radio device. The computer with a Depth camera collects information, makes a decision, and controls a drone in a similar way to human beings, which makes it applicable to various fields. Its usability is enhanced further since it can control common commercial drones instead of specially manufactured drones for swarm flight. It can also be used to prevent drones clashing each other, control access to a drone, and control drones with no permit.

• Convergence Security Journal
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• v.22 no.4
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• pp.25-34
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• 2022

This paper proposes a secret sharing based fast node authentication technique applicable to Flying Ad-Hoc Network (FANET) that can be used to construct self-organized communication network in multi drones and drone squadrons operations. Before deployment, each node stores an exponential share, exponential secret and a portion of PUF CRP table. After being deployed in the field, in the early-stage of network formation, each node broadcasts its ID, exponential share and a hash value of PUF Response and pseudo-random number. Then each node performs a reconstruction of the exponential secret using the exponential shares transmitted from neighboring nodes. When the exponential secret is reconstructed, simultaneous authentication is completed for all nodes that have transmitted the exponential share used in the reconstruction. A node that transmits an incorrect exponential share to disturb the reconstruction of the exponential secret during the authentication process can be detected before performing the reconstruction through the verification of the hash value, and will be excluded from the reconstruction.