Article(id=1308462963908699110, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20260022, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1775664000000, revisedDateStr=2026-04-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891480662, onlineDateStr=2026-09-20, pubDate=1784044800000, pubDateStr=2026-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891480662, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891480662, creator=13701087609, updateTime=1789891480662, updator=13701087609, issue=Issue{id=1308462963522823142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='7', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1784044800000', pubDateStr='2026-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891480570, creator='13701087609', updateTime=1789892910062, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468959309751193, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468959309751194, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1308462964156163048, articleId=1308462963908699110, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Intelligent Prediction and Selection Method for Child Restraint Systems Based on Deep Multilayer Perceptron, columnId=1308462964089054183, journalTitle=Automotive Engineer, columnName=Special Topic on Automotive Passive Safety Technology, runingTitle=null, highlight=null, articleAbstract=

The traditional development of Child Restraint System (CRS) safety relies on simulation and physical test results, which suffers from high costs, low efficiency and obvious limitations. The paper constructes a simplified high-precision coupled model under standard working conditions to efficiently generate training datasets. By fusing the time-domain, frequency-domain and second-order waveform fitting features of crash pulses, an intelligent prediction model based on deep multilayer perceptron is adopted to rapidly predict 4 core safety indicators, namely the Head Injury Criterion (HIC), head acceleration, axial neck force and chest acceleration. Validation results on new vehicle models show that the prediction model achieves an average accuracy of 93.3%, which can support the rapid development of CRS safety and demonstrates excellent engineering applicability.

, authors=Jiawen Liu, Yu Wang, Shaohan Hou, Feifei Han, Dong Cui, Botao Hu, authorsList=Jiawen Liu, Yu Wang, Shaohan Hou, Feifei Han, Dong Cui, Botao Hu, authorCompany=null, correspAuthors=Botao Hu, 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=1308462964500095978, articleId=1308462963908699110, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于深度多层感知机的儿童座椅智能预测及选型方法, columnId=1308462964240049129, journalTitle=汽车工程师, columnName=汽车被动安全技术专题, runingTitle=null, highlight=null, articleAbstract=

针对传统儿童座椅安全性开发过程依赖仿真和物理试验结果，成本高、效率低等问题，通过构建标准工况下的简化高精度耦合模型高效生成训练数据集，融合碰撞脉冲的时域、频域及二阶波形拟合特征，利用基于深度多层感知机的智能预测模型对头部损伤指标（HIC）、头部加速度、颈部张力和胸部加速度4项安全指标进行快速预测。新车型验证结果表明，模型平均预测精度达93.3%，可支持儿童座椅安全性快速开发，具有良好的工程实用性。

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