• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.4
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• pp.54-63
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• 2022

A 3-tonf class liquid rocket engine that powers the upper stage of a small launcher and lifts 500 kg payload to 500 km SSO is designed. The small launcher is to utilize the flight-proven technology of the 75-tonf class engine for the first stage. A combination of liquid oxygen and liquid methane has been selected as their cryogenic states can provide an extra boost in specific impulse as well as enable a weight saving via the common dome arrangement. An expander cycle is chosen among others as the low-pressure operation makes it robust and reliable while a specific impulse of over 360 seconds is achievable with the nozzle extension ratio of 120. Key components such as combustion chamber and turbopump are designed for additive manufacturing to a target cost. The engine system provides an evaporated methane for the autogenous pressurization system and the reaction control of the stage. This upper stage propulsion system can be extended to various missions including deep space exploration.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.