苍白
发表于 2025-3-27 00:38:11
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未完成
发表于 2025-3-27 03:08:53
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险代理人
发表于 2025-3-27 05:19:57
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撤退
发表于 2025-3-27 09:37:10
Application of Improved ResNet18 Based Neural Network for Non-invasive Blood Glucose Testinges greater stability but also superior performance, with a Root Mean Square Error and The absolute mean value of the error of 0.86 and 0.73, respectively. According to the Clarke Error Grid analysis, the results of our model solution fall within the acceptable range for clinical trials, indicating good potential for clinical application.
Colonoscopy
发表于 2025-3-27 17:14:48
Enhancing Repetitive Action Counting Through Hierarchical Transformer-Based Radar-Vision Fusionl motion sensing but also significantly improves learning action features from videos. Our experimental results confirm the effectiveness of our proposed method in enhancing repetitive action counting.
phlegm
发表于 2025-3-27 19:27:06
Knowledge-Specific Reinforcement Learning for Job-Shop Scheduling with Dynamic Processing States in ction to improve job-shop scheduling collaboratively. We conducted experiments in a disassembly factory layout compared to rule-based scheduling methods. The experiments showed our method had superior performance in the disassembly machine utilization rate.
Schlemms-Canal
发表于 2025-3-28 00:15:45
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柱廊
发表于 2025-3-28 03:08:44
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BAIT
发表于 2025-3-28 09:34:21
mmWave Radar Point Cloud Based Pose Estimation with Residual Blocks for Rehabilitation Exercisens after each convolutional layer to ensure continuous information flow. This method improves learning from radar data, aiding in capturing both global and local pose details. Experiments on an open-source mm-wave radar dataset confirm our method’s effectiveness, with an average localization error of 6.37 cm.
关节炎
发表于 2025-3-28 10:34:21
Realizing Human Pose Estimation Based on Deep Kalman Filteringtroduces the application of transformer networks to compute inter-joint constraints and obtain accurate joint coordinates and velocities. Experimental evaluations validate the effectiveness of the proposed approach, demonstrating significant improvements in pose estimation precision and tracking accuracy compared to traditional methods.