9 research outputs found
협대역 EMI 주입을 통한 드론 무력화 방법
학위논문(박사) - 한국과학기술원 : 정보보호대학원, 2024.2,[v, 62 p. :]Recently, drones have emerged as game-changers in modern warfare, sparking a significant surge in interest in both offensive drones and anti-drone technologies. drone neutralization technology, in particular, plays a pivotal role in enhancing societal safety and security. However, existing neutralization techniques such as GPS jamming, spoofing, EMP, and ultrasonic attacks have limitations, with countermeasures already developed and the potential for collateral damage to surrounding devices.
In this dissertation, we introduce a novel drone neutralization technology: narrow-band EMI (Electromagnetic Interference) injection. This method leverages the vulnerability of multi-copter control systems to EMI and the reliance of drones on the Inertial Measurement Unit (IMU) for their attitude control. Our intuition is that the distortion of communication between the IMU and the control unit leads to immediate drone neutralization. Furthermore, the discovery of a specific vulnerable frequency dependency on a single control board substantiates this study, minimizing secondary damage and proving its effectiveness in circumventing conventional recovery methods. Additionally, this countermeasure technology necessitates shielding for all multi-copters, significantly undermining their price competitiveness.한국과학기술원 :정보보호대학원
The Effect of Spatio-Temporal Contextual Information in Visual Working Memory on Change Detection Process
The Effect of an Interval between Target Pre-cue and Search Array Onset on the Formation of a Target Template
Agglomeration Prevention of SnO2 Nanosphere Revealed by In Situ Transmission Electron Microscopy
리튬이온전지 음극재를 위한 Co3O4 NISPs (nanograins-interconnected secondary particles)의 높은 부피 용량의 연구
소재 조성 도출용 라이브러리 생성 장치 및 방법
The present invention provides an apparatus and a method for constructing a library for deriving a material composition using empirical result. Which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction
