Yoon Namkyung, Kim Kiseok, Lee Sangmin, Bai Jin Hyoung, Kim Hwangnam
School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
Digital Convergence Department, KEPCO E&C, Gimcheon 39660, Republic of Korea.
Sensors (Basel). 2024 Aug 22;24(16):5451. doi: 10.3390/s24165451.
Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models.
由于无人机能够提供实时数据并执行自动化任务，它们已成为各个行业的重要工具。然而，在单个无人机上集成多个传感器会带来诸如 payload 限制和数据管理问题等挑战。本文提出了一个综合系统，该系统利用先进的深度学习技术，特别是基于注意力的生成对抗网络（GAN），来解决无人机收集的时间序列传感器数据中的数据稀缺问题。通过根据操作条件调整传感频率同时保持数据分辨率，我们的系统确保了一致且高质量的数据收集。GAN 中的注意力机制增强了合成数据的生成，用逼真的数据填补了因传感频率降低而造成的空白。这种方法提高了各种应用的效率和性能，如精准农业、环境监测和监视。实验结果证明了我们的方法在扩展无人机的操作范围和持续时间以及利用各种评估指标提供可靠的增强数据方面的有效性。此外，通过将所提出的系统与各种对比 GAN 模型进行比较，验证了该系统的优越性能。
