Publication
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Project
Enhanced classification of dissolved organic matter sources based on rivers using machine learning and data augmentation
This paper investigates the improvement in organic matter classification accuracy from different aquatic environments through the application of machine learning and deep learning techniques, supplemented with data generated by an LSTM-GAN model. Samples from the Nakdong and Yeongsan Rivers in South Korea were analyzed using Orbitrap HR-MS to obtain natural organic matter (NOM) data. Classification was performed using three machine learning algorithms—random forest, support vector machine (SVM), and logistic regression—and one deep learning algorithm, a multi-layer perceptron (MLP). Due to the limited performance of deep learning with insufficient data, an LSTM-GAN-based augmentation model was proposed, improving MLP performance. The MLP with augmented data achieved the highest classification accuracy (79% for Yeongsan River, 68% for Nakdong River), demonstrating the significant potential of LSTM-GAN in enhancing deep learning models for river classification tasks. This approach provides a robust framework for improving environmental monitoring through machine learning. DOI: https://doi.org/10.4491/eer.2024.725
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Project
Deadline-Aware Redundant Transmission on Multi-Paths for Real-Time Service
This study addresses the challenge of meeting deadlines in real-time applications such as video streaming and online gaming, in which data block delivery is time sensitive. Traditional loss-recovery solutions such as retransmission are inadequate, particularly in multi-path networks with long round-trip times, resulting in delays and missed deadlines. The proposed deadline-aware redundant transmission (DART) approach proactively transmits redundant packets across multiple paths, considering the next transmission time and one-way delay on each path to meet deadlines with minimal overhead. It also addresses deadlocks caused by insufficient network capacity by prioritizing the transmission of the smallest data block in the queue. DART, which operates independently of existing schedulers, simplifies implementation and reduces disturbances to existing schedulers. Simulation results demonstrate that DART increases the block transmission success rates by 5% to 10%, depending on the network conditions, while requiring minimal overheads. DOI: https://ieeexplore.ieee.org/document/10937038
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Project
Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping
Abstract: This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve this, we developed a novel SLAM framework that integrates five additional functionalities into the existing Google Cartographer and Robot Operating System (ROS). These innovations include an inertial data generation system and a sensor data preprocessing system to mitigate issues arising from various sensor configurations. Additionally, the framework enhances system utility through real-time 3D topographic mapping, multi-node SLAM capabilities, and elliptical sensor data filtering. The average execution times for sensor data preprocessing and virtual inertial data generation are 0.55 s and 0.15 milliseconds, indicating a low computational overhead. Elliptical filtering has nearly the same execution speed as the existing filtering scheme. DOI: https://doi.org/10.3390/s25061808
