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A Temperature Prediction-Assisted Approach for Evaluating Propagation Delay and Channel Loss of Underwater Acoustic Networks., , , , , , и . MSN, стр. 781-785. IEEE, (2022)Achieving Time-Sharing and Spatial-Reuse Underwater Wireless Sensor Networks with Communication Fairness: A Distributed Deep Multi-Agent Reinforcement Learning Approach., , , , , и . WUWNet, стр. 26:1-26:5. ACM, (2021)Analysis of Thermocline Influencing Factors Based on Decision Tree Methods., , , , , и . CSA/CUTE, том 474 из Lecture Notes in Electrical Engineering, стр. 328-334. Springer, (2017)A Scalable and Fair Power Allocation Scheme Based on Deep Multi-Agent Reinforcement Learning in Underwater Wireless Sensor Networks., , , , , и . WUWNet, стр. 27:1-27:5. ACM, (2021)Field Experiment and Analysis of Underwater String Networks Based on PMAC., , , , , и . WUWNet, стр. 38:1-38:8. ACM, (2021)TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction., , , и . Sensors, 18 (11): 3797 (2018)Thermocline Analysis Based on Entropy Value Methods., , , , , и . CSA/CUTE, том 474 из Lecture Notes in Electrical Engineering, стр. 335-340. Springer, (2017)Construction of High Resolution Thermocline Grid Data Sets., , , , , , и . CSA/CUTE, том 474 из Lecture Notes in Electrical Engineering, стр. 353-358. Springer, (2017)High-resolution temperature and salinity model analysis using support vector regression., , , , , и . J. Ambient Intell. Humaniz. Comput., 15 (2): 1517-1525 (февраля 2024)DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction., , , , и . IEEE Access, (2020)