Titlebook: Cognitive Systems and Information Processing; 8th International Co Fuchun Sun,Qinghu Meng,Bin Fang Conference proceedings 2024 The Editor(s

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Conference proceedings 2024efully reviewed and selected from 136 submissions. The papers are organized in the following topical sections:?.Volume I :?Award;?Algorithm & Control; and?Application..Volume?II:?Robotics & Bioinformatics; and?Vision..

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1865-0929 s were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections:?.Volume I :?Award;?Algorithm & Control; and?Application..Volume?II:?Robotics & Bioinformatics; and?Vision..978-981-99-8020-8978-981-99-8021-5Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Studies in Linguistics and Philosophy supplemented by an attention module to retain valuable information. Experiments on multimodal physiological data from 13 typical epilepsy patients demonstrated that the proposed model achieves 93.5% sensitivity and 97.3% specificity.

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Multimodal Wearable Device Signal Based Epilepsy Detection with?Multi-scale Convolutional Neural Net supplemented by an attention module to retain valuable information. Experiments on multimodal physiological data from 13 typical epilepsy patients demonstrated that the proposed model achieves 93.5% sensitivity and 97.3% specificity.

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Conference proceedings 2024nd Information Processing, ICCSIP 2023, held in Luoyang, China, during August 10–12, 2023.?.The 52 full papers presented in these proceedings were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections:?.Volume I :?Award;?Algorithm & Control;

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Obstacle Avoidance Control Method for Robotic Assembly Process Based on Lagrange PPOstate to the robot arm’s action space to generate the robot arm’s action strategy. By interacting with the environment and collecting trajectory data, the strategy of the robotic arm is continuously improved by performing gradient updates using the L-PPO. Experimental results show that by using the

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Zero-Shot Sim-To-Real Transfer of Robust and Generic Quadrotor Controller by Deep Reinforcement Learained controller exhibits remarkable robustness and versatility across different quadrotor models, successfully completing flight tasks in real-world scenarios without requiring additional training or modifications. These results highlight the potential of deep reinforcement learning for achieving z

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