3篇
您的检索式：作者名="Fenglei Tan"
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| 1 | Highly efficient and multidimensional extraction of targets from complex matrices using aptamer-driven recognition显示文摘吸附物广泛地在基础被采用并且使用了象分离技术,生物工学,和环境科学那样的研究区域。选择和可重用性是为吸附物的二个很重要的要求。Aptamers 展览完成式选择和容易的新生,它使他们成为特别地有效的吸附材料。此处,我们讲道理地设计了新奇基于 aptamer 的吸附物并且从一个水的解决方案在目标的抽取 / 分离调查了他们的性能。这些吸附物能有选择地从包含背景混合物的复杂样品矩阵提取目标。而且,没有吸附能力的重要损失,他们能容易也被再循环。尤其是,吸附物没影响孤立的生物样品的活动,揭示他们为 biomolecules 的纯化 / 分离的潜力。合成吸附物用基于 aptamer 的吸附物和多孔的聚合物被构造,显示从水的答案的高度有效的目标分离。最后,分离列被构造,并且在水的答案的目标被这些列高效地分开。这里描述的 aptamerbased 吸附物在分离技术,生物工学,和环境相关的区域为潜在的应用展出大诺言。 | Jie Wang Haijing Shen Chi Huang Qinqin Ma Yaning Tan Fenglei Jiang Chao Ma Quan Yuan | 2017 | Nano Research2017，10，1： | 2 |
| 2 | Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting显示文摘Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance. | Hao Chen Jianzhong Zhang Yubo Tao Fenglei Tan | 2019 | Protection and Control of Modern Power Systems2019，4，1： | 0 |
| 3 | A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models显示文摘Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness. | Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，10： | 0 |
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