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Article(id=1322557144340218554, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322557042389275039, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.8.200, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766505600000, receivedDateStr=2025-12-24, revisedDate=1769356800000, revisedDateStr=2026-01-26, acceptedDate=1770825600000, acceptedDateStr=2026-02-12, onlineDate=1793251795134, onlineDateStr=2026-10-29, pubDate=1788019200000, pubDateStr=2026-08-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793251795134, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793251795134, creator=13701087609, updateTime=1793251795134, updator=13701087609, issue=Issue{id=1322557042389275039, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='8', pageStart='1', pageEnd='354', issueExtLink='null', onlineDate='null', pubDate='1788019200000', pubDateStr='2026-08-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793251770826, creator='13701087609', updateTime=1793251770826, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=200, endPage=210, ext={EN=ArticleExt(id=1322557144751260347, articleId=1322557144340218554, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Data-driven Online Prediction Method for Transmission Efficiency in Laser Wireless Power Transmission, columnId=1152281494212408178, journalTitle=Journal of Power Supply, columnName=Wireless Power Transfer, runingTitle=null, highlight=null, articleAbstract=

The laser wireless power transmission (LWPT) technology offers advantages such as long-range capability, high directivity and high power density, showing great application promise in the fields such as unmanned aerial vehicle (UAV) charging. However, the environmental disturbances including atmospheric scattering, absorption and turbulence induce strong nonlinearity and time-varying characteristics in a medium transmission state. The conventional mechanism models rely on environmental parameters, so they are unsuitable for online awareness. To solve this problem, a feature-attention enhancement neural network (FAENN) is proposed, which enables high-accuracy online prediction of transmission efficiency by only using easily measurable electrical signals such as laser diode current and photovoltaic current. The architecture integrates feature-wise gating with residual connections to balance the modeling capacity and training stability. Experimental results show that this model achieves an R2 of 0.995 54 on the test set, significantly outperforming the linear regression, support vector regression and feedforward neural network. On a 6-m LWPT experimental platform, a mean absolute error of only 0.036 kW/m2 in irradiance power density prediction was obtained. The proposed method requires no atmospheric sensors, and it has high accuracy, low latency and strong generalization capability, providing reliable state awareness support for intelligent LWPT systems.

, authors=Liangrong SUN1, Yunfeng BAI1, 2, Jinsong KANG3, authorsList=Liangrong SUN, Yunfeng BAI, Jinsong KANG, authorCompany=null, correspAuthors=Jinsong KANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322557145527206591, articleId=1322557144340218554, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于数据驱动的激光无线能量传输效率在线预测方法, columnId=1149830138994647045, journalTitle=电源学报, columnName=无线电能传输, runingTitle=null, highlight=null, articleAbstract=

激光无线能量传输LWPT（laser wireless power transmission）技术具有远距离、高定向性和高功率密度等优势，在无人机UAV（unmanned aerial vehicle）供能等领域应用前景广阔。然而，大气散射、吸收与湍流等环境扰动使介质传输状态呈现强非线性与时变特性，而传统机理模型依赖环境参数，难以支持在线感知。为此，提出一种特征自适应增强型全连接神经网络FAENN（feature-attention enhancement neural network），利用激光器电流与光伏电流等易测电气量，实现对传输效率的高精度在线预测。融合逐维特征门控与残差连接，兼顾建模能力与训练稳定性。实验表明，模型在测试集上决定系数R2达0.995 54，显著优于线性回归、支持向量回归及前馈神经网络；在6 m激光传能平台上，辐照功率密度预测平均绝对误差仅为0.036 kW/m2。所提方法无需大气介质传感器，具备高精度、低延迟与强泛化能力，可为智能激光供能系统提供可靠状态感知支撑。

, authors=孙梁榕1, 白云峰1, 2, 康劲松中国电源学会高级会员3, authorsList=孙梁榕, 白云峰, 康劲松中国电源学会高级会员, authorCompany=null, correspAuthors=康劲松中国电源学会高级会员, authorNote=

孙梁榕（1998—　），男，博士研究生。研究方向：激光无线能量传输。E-mail：。

白云峰（1995—　），男，博士。研究方向：激光无线能量传输。E-mail：。

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康劲松（1972—　），男，中国电源学会高级会员，通信作者，博士，教授。研究方向：电力电子与电机控制，新能源变换与控制技术。E-mail：。
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孙梁榕（1998—　），男，博士研究生。研究方向：激光无线能量传输。E-mail：。

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白云峰（1995—　），男，博士。研究方向：激光无线能量传输。E-mail：。

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基于数据驱动的激光无线能量传输效率在线预测方法
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孙梁榕 1 , 白云峰 1, 2 , 康劲松中国电源学会高级会员 3
电源学报 | 无线电能传输 2026,24(8): 200-210
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电源学报 |无线电能传输 2026 , 24 (8) : 200 -210
基于数据驱动的激光无线能量传输效率在线预测方法
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孙梁榕1 , 白云峰1, 2 , 康劲松中国电源学会高级会员3
作者信息
  • 1.同济大学电子与信息工程学院，上海　201804
  • 2.上海航天电子有限公司，上海　201821
  • 3.同济大学交通学院，上海　201804
通讯作者:
康劲松（1972—　），男，中国电源学会高级会员，通信作者，博士，教授。研究方向：电力电子与电机控制，新能源变换与控制技术。E-mail：。
作者简介:

孙梁榕（1998—　），男，博士研究生。研究方向：激光无线能量传输。E-mail：。

白云峰（1995—　），男，博士。研究方向：激光无线能量传输。E-mail：。

Data-driven Online Prediction Method for Transmission Efficiency in Laser Wireless Power Transmission
Liangrong SUN1 , Yunfeng BAI1, 2 , Jinsong KANG Senior Member, CPSS3
Affiliations
  • 1.College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
  • 2.Shanghai Aerospace Electronics Co., Ltd., Shanghai 201821, China
  • 3.College of Transportation, Tongji University, Shanghai 201804, China
出版时间: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.200
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激光无线能量传输LWPT（laser wireless power transmission）技术具有远距离、高定向性和高功率密度等优势，在无人机UAV（unmanned aerial vehicle）供能等领域应用前景广阔。然而，大气散射、吸收与湍流等环境扰动使介质传输状态呈现强非线性与时变特性，而传统机理模型依赖环境参数，难以支持在线感知。为此，提出一种特征自适应增强型全连接神经网络FAENN（feature-attention enhancement neural network），利用激光器电流与光伏电流等易测电气量，实现对传输效率的高精度在线预测。融合逐维特征门控与残差连接，兼顾建模能力与训练稳定性。实验表明，模型在测试集上决定系数R2达0.995 54，显著优于线性回归、支持向量回归及前馈神经网络；在6 m激光传能平台上，辐照功率密度预测平均绝对误差仅为0.036 kW/m2。所提方法无需大气介质传感器，具备高精度、低延迟与强泛化能力，可为智能激光供能系统提供可靠状态感知支撑。

激光无线能量传输  /  介质传输效率  /  数据驱动  /  通道注意力机制  /  反向传播神经网络

The laser wireless power transmission (LWPT) technology offers advantages such as long-range capability, high directivity and high power density, showing great application promise in the fields such as unmanned aerial vehicle (UAV) charging. However, the environmental disturbances including atmospheric scattering, absorption and turbulence induce strong nonlinearity and time-varying characteristics in a medium transmission state. The conventional mechanism models rely on environmental parameters, so they are unsuitable for online awareness. To solve this problem, a feature-attention enhancement neural network (FAENN) is proposed, which enables high-accuracy online prediction of transmission efficiency by only using easily measurable electrical signals such as laser diode current and photovoltaic current. The architecture integrates feature-wise gating with residual connections to balance the modeling capacity and training stability. Experimental results show that this model achieves an R2 of 0.995 54 on the test set, significantly outperforming the linear regression, support vector regression and feedforward neural network. On a 6-m LWPT experimental platform, a mean absolute error of only 0.036 kW/m2 in irradiance power density prediction was obtained. The proposed method requires no atmospheric sensors, and it has high accuracy, low latency and strong generalization capability, providing reliable state awareness support for intelligent LWPT systems.

Laser wireless power transmission (LWPT)  /  medium transmission efficiency  /  data-driven  /  channel attention mechanism  /  back propagation neural network (BPNN)
孙梁榕, 白云峰, 康劲松中国电源学会高级会员. 基于数据驱动的激光无线能量传输效率在线预测方法. 电源学报, 2026 , 24 (8) : 200 -210 . DOI: 10.13234/j.issn.2095-2805.2026.8.200
Liangrong SUN, Yunfeng BAI, Jinsong KANG. Data-driven Online Prediction Method for Transmission Efficiency in Laser Wireless Power Transmission[J]. Journal of Power Supply, 2026 , 24 (8) : 200 -210 . DOI: 10.13234/j.issn.2095-2805.2026.8.200
基金
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  • 中央高校基本科研业务费专项资金资助项目(2023-4-ZD-03)
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2026年第24卷第8期
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doi: 10.13234/j.issn.2095-2805.2026.8.200
  • 接收时间：2025-12-24
  • 首发时间：2026-10-29
  • 出版时间：2026-08-30
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  • 收稿日期：2025-12-24
  • 修回日期：2026-01-26
  • 录用日期：2026-02-12
基金
Fundamental Research Funds for the Central Universities(2023-4-ZD-03)
中央高校基本科研业务费专项资金资助项目(2023-4-ZD-03)
作者信息
    1.同济大学电子与信息工程学院，上海　201804
    2.上海航天电子有限公司，上海　201821
    3.同济大学交通学院，上海　201804

通讯作者:

康劲松（1972—　），男，中国电源学会高级会员，通信作者，博士，教授。研究方向：电力电子与电机控制，新能源变换与控制技术。E-mail：。
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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.8.200
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2种不同金属材料的力学参数
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genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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