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| 1 | 基于机器学习的自适应双模协同无线充电调度策略显示文摘为了打破无线传感器网络的能量瓶颈,考虑无线充电效率对充电距离的敏感性,提出一种基于机器学习的自适应双模式设备协同调度的无线充电方案。首先,基于剩余能量、能耗以及充电效率来定义节点状态,提出一种计及节点状态的自适应阈值选择充电算法。然后设计改进式遗传算法,以最大化能量效用为目标为各节点选择合适的充电模式。此外,为进一步降低充电算法时间复杂度,利用基于支持向量机的机器学习方法学习上述充电模式切换机制,构建节点状态智能预测模型。仿真结果表明,所提算法可在保证较低充电时延的基础上,有效提升多无线充电设备的能量效用,增强传感网络的可持续性。 | 吴润泽 王浩楠 郭昊博 许晨 高娟 | 2023 | 电力系统保护与控制2023，51，8： | 2 |
| 2 | Photo-responsive shape memory polymer composites enabled by doping with biomass-derived carbon nanomaterials显示文摘As photothermal conversion agents,carbon nanomaterials are widely applied in polymers for light-triggered shape memory behaviors on account of their excellent light absorption.However,they are usually derived from non-renewable fossil resources,which go against the demand for sustainable development.Biomass-derived carbon nanomaterials are expected as alternatives if they are designed with good dispersibility as well as splendid photothermal properties.Up to date,very few researches focused on this area.Herein,we report a novel light-triggered shape memory composite by incorporating renewable biomass-derived carbon nanomaterials into acrylate polymers without deep purification and processing.These functionalized carbon nanomaterials not only have stable dispersion in polymers as fillers,but also can endow the polymers with excellent and stable thermal and photothermal responsive properties in biological friendly environment.With the introduction of biomass-derived carbon nanomaterials,the mechanical properties of the composites are also further enhanced with the formation of hydrogen bonding between the carbon nanomaterials and the polymers.Notably,the doping of 1%carbon nanomaterials endows the polymer with sufficient hydrogen bonds that not only exhibit excellent thermal and photothermal responsive properties,but also with enough space for the motion of chains.These properties make such composite a promising and safe candidate for shape memory applications,which provide a new avenue in smart fabrics or intelligent soft robotics. | Nina Yan Zhiyu Zheng Yunliang Liu Xizhi Jiang Jiamin Wu Min Feng Lei Xu Qingbao Guan Haitao Li | 2022 | Nano Research2022，15，2： | 1 |
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