Article(id=1281616669340569792, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.0102, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758384000000, receivedDateStr=2025-09-21, revisedDate=1764604800000, revisedDateStr=2025-12-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490825256, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490825256, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490825256, creator=13701087609, updateTime=1783490825256, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=44, endPage=51, ext={EN=ArticleExt(id=1281616669567062209, articleId=1281616669340569792, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Prediction of pilot human errors during the carrier landing phase under complex task environments based on an improved CREAM method, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

To improve the safety of aircraft landing phase, this study investigates the prediction of pilot human error during the carrier landing phase complex task environments based on CREAM. Based on the pilot operational workflows during the carrier landing phase, this work recalibrates the original Common Performance Condition (CPC) factors in CREAM, and proposes an improved approach for predicting the probability of human error in pilots based on the improved CREAM method, describing the situational environment in which the pilot is located during the carrier landing phase. It introduces an Environmental Impact Index and Effect Impact Index to characterize the influence of various task environments on pilots' cognitive functions. A simulated carrier landing assessment experiment is designed and conducted based on the actual situational environment during the carrier landing phase. By analyzing characteristic indicators such as electroencephalogram (EEG), eye-tracking, electrocardiogram (ECG), and electromyography (EMG) data, and National Aeronautics and Space Administration Task Load Index(NASA-TLX) scale data of the subjects, the load level of the subjects in different environments is assessed, and then the effect impact index of different cognitive functions is calculated to predict the probability of human error. The results demonstrate that the proposed method for predicting probability of human error in pilots can improve the accuracy of prediction results by using objective data assessment, which instead of experts' subjective evaluations. This method can predict the probability of human error in pilots under the influence of 20 different environmental factors.

, authors=Weicheng LIU^{1, 2}, Youchao SUN^{**, 1}, Heng JIN¹, Zichang CHEN^{1, 2}, authorsList=Weicheng LIU, Youchao SUN, Heng JIN, Zichang CHEN, authorCompany=null, correspAuthors=Youchao SUN, 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=1281616670087155908, articleId=1281616669340569792, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于改进CREAM的着舰阶段复杂任务环境飞行员人因失误预测, columnId=1277328387270112092, journalTitle=中国安全科学学报, columnName=安全科学理论与方法, runingTitle=null, highlight=null, articleAbstract=

为提高着舰阶段安全性，基于认知可靠性和失误分析法（CREAM）开展着舰阶段复杂任务环境下飞行员人因失误预测研究。结合着舰阶段飞行员动作流程，修正原始CREAM方法中共同绩效因子（CPC）定义，提出基于改进CREAM方法的飞行员人因失误概率预测方法，描述着舰阶段飞行员所处的情景环境；引入环境影响指数与效应影响指数，表征不同任务环境对飞行员认知功能的影响；结合着舰阶段实际情景环境，设计并开展模拟着舰评估试验；通过分析被试人员的脑电、眼动、心电、肌电数据及美国航空航天局任务负荷指数（NASA-TLX）量表数据等特征指标，评估被试人员在不同环境下的负荷水平，进而计算不同认知功能的效应影响指数，预测人因失误概率。研究结果表明：所提出的飞行员人因失误概率预测方法，使用客观数据评估替代专家主观评价，能够提高预测结果真实性，该方法可以预测20种不同环境因素影响下飞行员人因失误概率。

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²中国航空无线电电子研究所，上海 202241, bio={"content":"

刘威成（2000—），男，河南商丘人，硕士研究生，主要研究方向为人机交互、人机工效等。E-mail: liuweicheng@nuaa.edu.cn。

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刘威成（2000—），男，河南商丘人，硕士研究生，主要研究方向为人机交互、人机工效等。E-mail: liuweicheng@nuaa.edu.cn。

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