• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.6
• /
• pp.473-480
• /
• 2021

In this paper, We analyze and present improvements to problems in software quality through Static Analysis for Open Source, which is widely used as the Flight Controller software for small unmanned aerial vehicle drones. MISRA coding rules, which are widely applied based on software quality, have been selected. Static analysis tools were used by LDRA tools certified international tools used in all industries, including automobiles, railways, nuclear power and healthcare, as well as aviation. We have identified some safety-threatening problems across the quality of the software, such as structure of open source modules, analysis of usage data, compliance with coding rules, and quality indicators (complexity and testability), and have presented improvements.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.2
• /
• pp.307-312
• /
• 2023

North Korea has already been using underground space for military purposes for decades, and is currently developing it as a key base for operating asymmetric forces. Accordingly, the special operations teams need fighting methods, weapon systems, and organizational structures to carry out subterranean operations. This paper presents an unmanned system platform for subterranean operations that combines tilt-rotor type drones, high-tech sensors, communication repeaters, and small robots, and a system that can be operated by special operation teams. Based on this, the survivability of the special operations teams can be strengthened and operational utility can be maximized. Afterwards, if Special Warfare Command collects collective intelligence based on the ideas related to subterranean operations presented in this paper and further develops these, it will be possible to drive subterranean operations doctrines, weapon systems, and organizational structures optimized for the battlefield on the Korean Theater of Operations in the near future.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.919-925
• /
• 2023

Modern drones are equipped with miniaturized mission equipment capable of performing various tasks such as surveillance and reconnaissance. Consequently, these mission equipment are exposed to disturbances like wind loads and motor rotations, which can lead to instability in the operation of the Electro-Optical Targeting System (EOTS). Specifically, simple step inputs for changing the line of sight in EOTS can cause abrupt changes in speed, inducing overshoot and potentially creating instability along with other disturbances. To address this, a velocity profile was designed so that the angular velocity moves in a trapezoidal shape when changing the EOTS line of sight. A Double-loop controller was designed to apply this profile as an input to the external loop receiving position feedback. The system's stability was then compared, and the velocity profile was optimized within a stable range by varying maximum speed and acceleration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.224-226
• /
• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.

• Geophysics and Geophysical Exploration
• /
• v.25 no.1
• /
• pp.26-37
• /
• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Journal of Aerospace System Engineering
• /
• v.17 no.4
• /
• pp.67-74
• /
• 2023

Recently, Urban Aircraft Mobility (UAM) has been attracting attention as a transportation system of the future, and small drones also play a role in various industries. The failure of various types of aviation systems can lead to crashes, which can result in significant property damage or loss of life. In the defense industry, where aviation systems are widely used, the failure of aviation systems can lead to mission failure. Therefore, this study proposes an anomaly detection model using deep learning technology to detect anomalies in aviation systems to improve the reliability of development and production, and prevent accidents during operation. As training and evaluating data sets, current data from aviation systems in an extremely low-temperature environment was utilized, and a deep learning network was implemented using the convolutional neural network, which is a deep learning technique that is commonly used for image recognition. In an extremely low-temperature environment, various types of failure occurred in the system's internal sensors and components, and singular points in current data were observed. As a result of training and evaluating the model using current data in the case of system failure and normal, it was confirmed that the abnormality was detected with a recall of 98 % or more.