胆小懦夫 发表于 2025-3-23 12:38:04
Malka Rappaport,Beth Levin,Mary Laughrene the concept of supervisors and provide a definition for supervisors. We propose a simplification algorithm that effectively reduces the complexity level of extended finite state machine systems. Finally, through examples demonstration we validate our conclusions’ correctness as well as demonstrate范围广 发表于 2025-3-23 15:46:48
Verbs in Depictives and Resultativesssues, hindering agents from attaining the highest reward. To address the mentioned issues, an improved parameter updating method based on a weighted average of advantage value is proposed. The simulation results on the highway simulation platform demonstrate that the enhanced A3C algorithm offers iCEDE 发表于 2025-3-23 19:51:37
http://reply.papertrans.cn/23/2292/229133/229133_13.pngindigenous 发表于 2025-3-23 23:25:58
Multi-brain Collaborative Target Detection Based on RAPmbining downsampling and mean filtering is used to extract time-domain features from segmented data. Then, three different classifiers are used to train and predict the experimental data, and multi-brain information fusion is performed for the predicted results as the final result. Finally, the real厌倦吗你 发表于 2025-3-24 04:43:50
http://reply.papertrans.cn/23/2292/229133/229133_15.pngseparate 发表于 2025-3-24 07:18:12
http://reply.papertrans.cn/23/2292/229133/229133_16.pngCAMP 发表于 2025-3-24 13:47:56
CCA-MTFCN: A Robotic Pushing-Grasping Collaborative Method Based on Deep Reinforcement Learninganalysis (CCA) is designed to effectively evaluate the quality of push actions in pushing-and-grasping collaboration. This enables us to explicitly encourage pushing actions that aid grasping thus improving the efficiency of sequential decision-making. Our approach was trained in simulation through膝盖 发表于 2025-3-24 17:58:03
http://reply.papertrans.cn/23/2292/229133/229133_18.png暂时休息 发表于 2025-3-24 20:56:19
http://reply.papertrans.cn/23/2292/229133/229133_19.png协迫 发表于 2025-3-25 03:03:01
Image Compressed Sensing Reconstruction via Deep Image Prior with Feature Space and Texture Informatining, a unified loss function guides the alternating optimization of both paths. Evaluation of prominent benchmark datasets, including Set5, Set11, and BSD68, reveals that our proposed method outperforms traditional iterative approaches and existing deep learning-based methodologies in terms of bot