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Titlebook: Intelligent Robotics and Applications; 14th International C Xin-Jun Liu,Zhenguo Nie,Rui Song Conference proceedings 2021 Springer Nature Sw

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發(fā)表于 2025-3-26 22:14:00 | 只看該作者
Vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screeningthod, 19 toddlers (8 ASD toddlers and 11 non-ASD toddlers) between the ages of 16 and 32 months participate in this study. The accuracy of the automatic evaluation method for pointing behavior is 17/19. It shows that the ENP protocol and the proposed method based on computer vision are feasible in the early screening of autism.
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發(fā)表于 2025-3-27 02:54:34 | 只看該作者
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發(fā)表于 2025-3-27 08:14:41 | 只看該作者
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發(fā)表于 2025-3-27 09:54:21 | 只看該作者
978-3-030-89133-6Springer Nature Switzerland AG 2021
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發(fā)表于 2025-3-27 14:58:54 | 只看該作者
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發(fā)表于 2025-3-27 20:42:25 | 只看該作者
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發(fā)表于 2025-3-27 23:54:46 | 只看該作者
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發(fā)表于 2025-3-28 05:05:45 | 只看該作者
An Intelligent Path Generation Method of Robotic Grinding for Large Forging Partsng lines, burrs and high islands, which are traditionally removed through manual grinding by skilled operators. These random defects pose a big challenge to researchers interested in large forging parts grinding path generation by a CAD/CAM system. This paper proposes a new path generation method ba
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發(fā)表于 2025-3-28 07:43:06 | 只看該作者
Research and Analysis on Energy Consumption of Underwater Hexapod Robot Based on Typical Gaitth planning. The purpose of this paper is to analyze the motion energy consumption of the underwater hexapod robot walking in the seabed environment. Based on the principle of kinematics, a typical gait is designed for the hexapod robot proposed in this paper, and a detailed dynamic model of the und
40#
發(fā)表于 2025-3-28 13:25:50 | 只看該作者
Bearing Fault Diagnosis Based on?Attentional Multi-scale CNN so it is necessary to detect the health status of bearing in real time. In this paper, a multi-scale feature fusion convolutional neural network with attention mechanism (AMMNet) is proposed for bearing fault diagnosis. Firstly, different scale shallow features of the input signal are extracted by
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