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Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 17th EAI Internation Honghao Gao,Xinheng Wang Conference proceedings 202

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41#
發(fā)表于 2025-3-28 16:12:27 | 只看該作者
Optimal Control and?Reinforcement Learning for Robot: A Surveylopment of reinforcement learning and optimal control has become an impetus of engineering, which has show large potentials on automation. Currently, the optimization applications on robot are facing challenges caused by model bias, high dimensional systems, and computational complexity. To solve th
42#
發(fā)表于 2025-3-28 22:35:50 | 只看該作者
43#
發(fā)表于 2025-3-29 01:29:20 | 只看該作者
Recommendation Model Based on Social Homogeneity Factor and Social Influence Factorsider the social homogeneity factor between users and do not consider the social influence factor. To make the recommendation model more in line with the real-life situation, this paper proposes a novel graph attention network to model the homogeneity effect and the influence effect in the user doma
44#
發(fā)表于 2025-3-29 05:07:26 | 只看該作者
Attention Based Spatial-Temporal Graph Convolutional Networks for?RSU Communication Load Forecastinghicular Ad-hoc Networks (VANETs). However, because of peak hour communication demands in VANETs and limited energy storage, it is necessary for RSU to adjust their participation in communication according to the requirements and allocate energy reasonably to balance the workload. Firstly, tidal traf
45#
發(fā)表于 2025-3-29 08:37:49 | 只看該作者
Mobile Encrypted Traffic Classification Based on?Message Type Inferenceation confidentiality and prevent privacy leakage. These cryptographic protocols make it difficult to classify encrypted traffic for network management and intrusion detection. Existing mobile encrypted traffic classification approaches intend to alleviate this problem for TLS 1.2 encrypted traffic
46#
發(fā)表于 2025-3-29 12:05:50 | 只看該作者
47#
發(fā)表于 2025-3-29 16:20:07 | 只看該作者
Underwater Information Sensing Method Based on Improved Dual-Coupled Duffing Oscillator Under Lévy Nsent, most interference models for underwater information sensing tasks under substantial interference choose Gaussian noise models. However, it often contains a strong impact and does not conform to the Gaussian distribution. Moreover, in the current research on the sensing of underwater unknown fr
48#
發(fā)表于 2025-3-29 19:57:37 | 只看該作者
49#
發(fā)表于 2025-3-30 03:35:12 | 只看該作者
50#
發(fā)表于 2025-3-30 07:37:53 | 只看該作者
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