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Titlebook: Robotics and Rehabilitation Intelligence; First International Jianhua Qian,Honghai Liu,Dalin Zhou Conference proceedings 2020 The Editor(s

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21#
發(fā)表于 2025-3-25 03:51:53 | 只看該作者
Zeju Wu,Cuijuan Jiao,Jianyuan Sun,Liang Chen and exciting subject require further consolidation and a determined effort from the technology community to continue to develop and promote technology education through the opportunities which are presenting in New Zealand in 2016 – and whatever may follow.
22#
發(fā)表于 2025-3-25 10:08:52 | 只看該作者
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發(fā)表于 2025-3-25 15:32:27 | 只看該作者
24#
發(fā)表于 2025-3-25 16:14:18 | 只看該作者
ns, offshore wind structures, and railway systems. And finally, Part E is an excursion on diagnostics in arts and culture. The book integrates knowledge of basic sciences and engineering disciplines with contri978-3-662-50732-2978-3-642-25850-3
25#
發(fā)表于 2025-3-25 20:58:06 | 只看該作者
26#
發(fā)表于 2025-3-26 03:44:41 | 只看該作者
Power Control Algorithm of D2D Communication Based on Non-cooperative Game Theory constraint of the interference tolerance of the base station to the transmission power. Then, by using the interference size of D2D users to cellular users, base station updates the interference price of the cellular system. Accordingly, the D2D users adjust the transmission power and maximize the
27#
發(fā)表于 2025-3-26 05:06:50 | 只看該作者
28#
發(fā)表于 2025-3-26 10:31:19 | 只看該作者
A Novel Auxiliary Strategy with Compliance and Safety for Walking Rehabilitation Training Robot calculating the user’s walking intents. Our proposed method can effectively provide the assist strategy with compliance and safety for the elderly and the disabled. The walking assisted-robot with similar structure, which integrates the proposed approach, has a good universal property for the elder
29#
發(fā)表于 2025-3-26 14:59:57 | 只看該作者
Tire Defect Detection Based on Faster R-CNNed method combined the convolution features of the third layer and the convolution features of the fifth layer in the ZF network (a kind of convolution neural network). Then, the improved ZF network was used to extract deep characteristics as inputs for Faster R-CNN. From the experiment, the propose
30#
發(fā)表于 2025-3-26 19:47:27 | 只看該作者
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