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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.6
- 발행연도
- 2023.12
- 수록면
- 472 - 482 (11page)
- DOI
- 10.5573/IEIESPC.2023.12.6.472
이용수
초록· 키워드
오류제보하기
This study analyzes the field-level AI reasoning technology of its terminal equipment and applies it to optimizing computing resources under the cooperation of multiple UAVs. The experimental results indicates that the performance of the ICEM method is superior to the other three methods, and the maximum value is 15 × 10<SUP>8</SUP> bits/Joule. An increase of 10 thresholds can reduce the number of iterations 50-fold when the amount of CDF iterations is 0.9. This can reach more than 20 times if the number of UAVs and mobile terminal devices doubles. The UAV will run at the maximum speed in most time slots, and the maximum computation of the terminal equipment can reach 15 Mbits when the entire working cycle is 30 seconds. Therefore, the algorithm proposed in this study achieves higher computing energy efficiency based on less convergence time and is effective in end-to-end computing power network computing resource scheduling.
목차
Abstract
1. Introduction
2. Related Work
3. Analysis of Key Technologies of End to side Computing Network based on Field Level AI Reasoning of Terminal Equipment
4. Simulation Performance Analysis on Key Technologies of End-side Computing Network of UAV Communication Terminal
5. Conclusion
References
참고문헌 (18)
참고문헌 신청
M. Żarski, B. Wójcik, K. Książek, and J. A. Miszczak, “Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices,” Computer‐Aided Civil and Infrastructure Engineering, Vol. 37, No. 4, pp. 500-515
Z.T. Njitacke, et al., “Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation,” Nonlinear Dynamics, Vol. 107, No. 3, pp. 2867-2886. 2022.
L. Chen, et al., “Intelligent ubiquitous computing for future UAV-enabled MEC network systems,” Cluster Computing, Vol. 25, No. 4, pp. 2417-2427. 2022.
Y. Chen, et al., “Dynamic task offloading for mobile edge computing with hybrid energy supply,” Tsinghua Science and Technology, Vol. 28, No. 3, pp. 421-432. 2022.
M, Kamel, et al., “Distributed Address Table (DAT): A decentralized model for end-to-end communication in IoT,” Peer-to-Peer Networking and Applications, Vol. 15, No. 1, pp. 178-193. 2022.
Z.T. Njitacke, et al., “Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation,” Nonlinear Dynamics, Vol. 107, No. 3, pp. 2867-2886. 2022.
L. Chen, et al., “Intelligent ubiquitous computing for future UAV-enabled MEC network systems,” Cluster Computing, Vol. 25, No. 4, pp. 2417-2427. 2022.
Y. Chen, et al., “Dynamic task offloading for mobile edge computing with hybrid energy supply,” Tsinghua Science and Technology, Vol. 28, No. 3, pp. 421-432. 2022.
M, Kamel, et al., “Distributed Address Table (DAT): A decentralized model for end-to-end communication in IoT,” Peer-to-Peer Networking and Applications, Vol. 15, No. 1, pp. 178-193. 2022.
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