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Junghwa Kang(강정화),Jae-Hyun Kim(김재현) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Unmanned aerial vehicles (UAVs) can serve as aerial base stations when cellular networks are disrupted. Most studies on UAV communications in indoor disaster situations aimed to provide throughput while using less power consumption. However, it is necessary to establish a user connectivity and a path loss by considering the movements of UAVs and the target building situation. This paper proposes a new adaptive UAV localization algorithm to maximize the user connectivity subject to the throughput requirements in indoor disaster environment using searching time. In searching time, the connectivity of indoor users can be improved, whereas there are wasted movements. Therefore, we design an adaptive UAV localization using Lyapunov optimization to maximize connectivity with fewer UAV movements. Proposed localization algorithm guarantees throughput and connectivity, in contrast to the randomly located UAV scenario.
실내 재난 환경에서 강화학습 기반 UAV 최적 경로 학습 연구
강정화(Junghwa Kang),김경록(Kyeongrok Kim),김재현(Jae-Hyun Kim) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
Unmanned aerial vehicle (UAV)는 셀룰러 네트워크를 이용하기 어려운 실내 재난 상황에서 공중 기지국 역할을 대신한다. 하지만 긴급 상황에서는 사용자들의 통신 요구사항과 지리적 환경 등이 급격하게 변하므로 통신 성능을 보장하기 어렵다. 따라서 본 논문에서는 non-orthogonal multiple access (NOMA)를 이용하여 네트워크의 성능을 보장하고 강화학습을 통해 UAV의 최적 이동 경로에 대해 학습한다. 성능분석 결과, NOMA를 사용한 경우, 기존의 프로토콜에 비해 총 소모 시간이 감소하였고, 할인 계수에 따라 총 소모 시간과 총 수신 데이터양 사이의 trade-off를 확인하였다.
퍼지 유전 알고리즘과 분석적 접근을 통한 대지 표적 위협도 평가
편재관(Jaekwan Pyeon),김도영(Doyoung Kim),강정화(Junghwa Kang),박상철(Sangchul Park) (사)한국CDE학회 2024 한국CDE학회 논문집 Vol.29 No.2
Modern battlefield environments are filled with complexity and uncertainty, necessitating accurate assessment of threats posed by targets, armaments, and protective assets. In these complex settings, objective threat assessment is crucial for strategic decision-making and efficient resource allocation. This study proposes a new methodology for threat assessment, integrating fuzzy logic with genetic algorithms. Utilizing real-world data on weaponry and equipment, this methodology derives optimal membership function values using genetic algorithms. These values are then used to extract threat weights for each piece of equipment. Additionally, proximity calculations are employed to determine the final threat level. This approach offers a more objective and precise evaluation compared to traditional methods, effectively reflecting the diverse characteristics of ground targets. Future research will focus on developing algorithms that consider a broader range of battlefield conditions and target characteristics, along with validating their applicability in real-world scenarios.