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您的检索式：作者名="Shengbo Tang"
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| 1 | Research on recognition algorithm for gesture page turning based on wireless sensing显示文摘When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human motion.By extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be perceived.On the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning.This algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification modules.The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency. | Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu | 2023 | Intelligent and Converged Networks2023，4，1： | 0 |
| 2 | Heterogeneous synergistic catalysis by Ru-RuOx nanoparticles for Se-Se bond activation显示文摘转变从对 nanocatalysts 启用的异构的合成化学同类为无机的 nanoparticles 和器官的底层要求反应机制和结构活动关系的调查。此处,我们报导热水地综合的钌 nanoparticles 在 Se-Se 契约激活和 heterocycles 的 selenylation 不同地表现了,展出在催化活动和作文之间的一种塑造火山的关系。 synergistic 效果为 Ru-RuO x nanocatalysts 被观察,与众多的描述和密度建议一个 PhSeSePh 分子能开始在金属性的 Ru 地点上被吸附并且劈开 PhSe 进二的功能的理论( DFT )计算*种类,它随后移居到 RuO x 地点并且与 nucleophile 反应完成 heterocycles 的 selenylation 。 | Mu Lin Liqun Kang Jun Gu Linxiu Dai Shengbo Tang Tao Zhang YuhaoWang Lindong Li Xiaoyu Zheng Wei Zhu Rui Si Xuefeng Fu Lingdong Sun Yawen Zhang Chunhua Yan | 2017 | Nano Research2017，10，3： | 0 |
| 3 | Approximate Optimal Filter Design for Vehicle System through Actor‑Critic Reinforcement Learning显示文摘Precise state and parameter estimations are essential for identification,analysis and control of vehicle engineering problems,especially under significant model and measurement uncertainties.The widely used filtering/estimation algorithms,such as Kalman series like Kalman filter,extended Kalman filter,unscented Kalman filter,and particle filter,generally aim to approach the true state/parameter distribution via iteratively updating the filter gain at each time step.However,the optimal-ity of these filters would be deteriorated by unrealistic initial condition or significant model error.Alternatively,this paper proposes to approximate the optimal filter gain by considering the effect factors within infinite time horizon,on the basis of estimation-control duality.The proposed approximate optimal filter(AOF)problem is designed and subsequently solved by actor-critic reinforcement learning(RL)method.The AOF design transforms the traditional optimal filtering problem with the minimum expected mean square error into an optimal control problem with the minimum accumulated estimation error,in which the estimation error is used as the surrogate system state and the infinite-horizon filter gain is the control input.The estimation-control duality is proved to hold when certain conditions about initial vehicle state distributions and policy structure are maintained.In order to evaluate of the effectiveness of AOF,a vehicle state estimation problem is then demonstrated and compared with the steady-state Kalman filter.The results showed that the obtained filter policy via RL with different discount factors can converge to theoretical optimal gain with an error within 5%,and the average estimation errors of vehicle slip angle and yaw rate are less than 1.5×10–4. | Yuming Yin Shengbo Eben Li Kaiming Tang Wenhan Cao Wei Wu Hongbo Li | 2022 | Automotive Innovation2022，5，4： | 0 |
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