Article(id=1309640087256793305, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250551, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1759248000000, revisedDateStr=2025-10-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172128738, onlineDateStr=2026-09-23, pubDate=1776960000000, pubDateStr=2026-04-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172128738, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172128738, creator=13701087609, updateTime=1790172128738, updator=13701087609, issue=Issue{id=1309640086191432626, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1776960000000', pubDateStr='2026-04-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172128484, creator='13701087609', updateTime=1790232864374, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894831258825016, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894831258825017, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=34, endPage=41, ext={EN=ArticleExt(id=1309640087416176858, articleId=1309640087256793305, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Laser Echo Signal Optimization Based on Dynamic Cognitive Enhancement Network, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issue of attenuation and waveform distortion in laser echo signals, laser echo signal model is constructed. Lightweight CNN-Transformer hybrid network is built to extract both local details and global temporal features of the signal, and a dynamic adjustment mechanism is designed to adaptively fuse local and global features. Furthermore, cognitive enhancement module is introduced, which utilizes an external memory bank to store and retrieve prior information from historical echoes, and optimizes feature inference through knowledge distillation loss. The results indicate that the attenuation of the laser echo is positively correlated with the severity of adverse weather conditions. Through distillation learning, the student network fully retains the core feature extraction logic of the teacher network. The proposed Dynamic Cognitive Enhancement Network algorithm reduces Mean Squared Error (MSE) by 40.2% and improves Peak Signal to Noise Ratio (PSNR) by 2.21 dB compared to the suboptimal model.

, authors=Yan Ding, Ying Yuan, Lei Wang, authorsList=Yan Ding, Ying Yuan, Lei Wang, authorCompany=null, correspAuthors=null, 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=1309640087625892059, articleId=1309640087256793305, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于动态认知增强网络算法的激光回波信号优化研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对激光回波信号衰减和失真问题，通过构建轻量化CNN-Transformer混合网络，提取激光回波信号的全局时序特征与局部细节，设计动态调整机制自适应融合局部和全局特征，并引入认知增强模块，利用外部记忆库存储和调用历史回波先验信息，结合知识蒸馏损失进行特征推理与优化。试验结果表明：激光回波衰减与天气恶劣程度呈正相关，学生网络经蒸馏学习完整保留教师网络的核心特征捕捉逻辑，相较于次优模型，动态认知增强网络算法的均方误差（MSE）降低40.2%，峰值信噪比（PSNR）提升2.21 dB。

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