Article(id=1281627170359448387, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.1133, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757779200000, receivedDateStr=2025-09-14, revisedDate=1763740800000, revisedDateStr=2025-11-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493328894, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493328894, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493328894, creator=13701087609, updateTime=1783493328894, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=63, endPage=71, ext={EN=ArticleExt(id=1281627170548192068, articleId=1281627170359448387, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=ISBOA-KELM multi-sensor data fusion model for early warning method in laboratory safety, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the challenges of complex data environments, low accuracy of single-sensor detection, and limited precision in traditional laboratory safety systems, this study presented a multi-sensor fusion early warning model based on an ISBOA and algorithm KELM. First, the KELM framework was employed to integrate heterogeneous sensor data and construct the warning model, where a regularization term was introduced to alleviate overfitting. Then, the improved ISBOA adaptively optimized the regularization coefficient C and kernel parameterσ of the KELM, thereby enhancing parameter robustness and diagnostic accuracy. Finally, simulation and experimental analyses were conducted using both synthetic and real laboratory datasets, and the proposed ISBOA-KELM model was compared with the unimproved Secretary Bird Optimization Algorithm (SBOA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms. The experimental results show that the ISBOA-KELM model improved accuracy by 4%, 3%, and 2%, respectively, compared with the other three models. In four representative laboratory safety scenarios, including fire and gas leakage, the detection accuracy exceeds 96% with the false negative rate below 6%, which significantly improves the reliability and robustness of safety accident early warning.

, authors=Liang GE^{1, 2}, Nüqing ZHOU¹, Honglei CHE³, Guoqing XIAO⁴, Xi LAI¹, Wen ZENG⁵, authorsList=Liang GE, Nüqing ZHOU, Honglei CHE, Guoqing XIAO, Xi LAI, Wen ZENG, 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=1281627172796339015, articleId=1281627170359448387, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=实验室安全ISBOA-KELM多传感器数据融合预警模型, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决传统实验室环境信息复杂、单传感器检测不准确且精度有限等问题，提出一种面向实验室安全的改进型鹭鹰优化算法（ISBOA）-核极限学习机（KELM）多传感器数据融合预警算法模型。首先，分析KELM的数据融合机制，并通过引入正则化项来有效缓解模型过拟合问题；然后，利用改进ISBOA对KELM中的正则化参数C和核参数σ进行自适应优化，构建ISBOA-KELM多传感器数据融合模型，从而避免人工选取KELM参数所导致的故障诊断准确率低的问题；最后，以模拟数据和试验数据为基础，分别与未改进的鹭鹰优化算法（SBOA）、粒子群算法（PSO）以及灰狼优化算法（GWO）进行性能对比分析。试验结果表明：ISBOA-KELM算法模型相较于其他3种模型准确率分别提高4%、3%、2%，且在实际测试实验室环境下火灾等4种情况的准确率均高于96%，漏报率低于6%，显著提升安全事故预警的可靠性与鲁棒性。

, authors=葛亮教授^{1, 2}, 周女青¹, 车洪磊正高级工程师³, 肖国清教授⁴, 赖希¹, 曾文教授⁵, authorsList=葛亮教授, 周女青, 车洪磊正高级工程师, 肖国清教授, 赖希, 曾文教授, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=MhViLmUuGHy2sbUX7tQuCA==, magXml=ZJa/VGF8rkvSda/a7qqYgg==, pdfUrl=null, pdf=XTMDop+T4Bpp23WWUVgJuQ==, pdfFileSize=8400354, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=XhDhJAkOvLQi5semWXD+Rw==, mapNumber=null, fund=null)}, authors=[Author(id=1281627173635199832, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627170359448387, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=cgroad@swpu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281627173735863131, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627170359448387, authorId=1281627173635199832, language=EN, stringName=Liang GE, firstName=Liang, middleName=null, lastName=GE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu Sichuan 610500, China
²National Key Laboratory of Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu Sichuan 6105003, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281627173844915036, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627170359448387, authorId=1281627173635199832, language=CN, stringName=葛亮教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹西南石油大学 机电工程学院，四川 成都 610500
²西南石油大学 油气藏地质及开发工程全国重点实验室，四川 成都 6105003, bio={"content":"

葛亮（1985—），男，湖北咸宁人，博士，教授，主要从事油气安全监检测与控制等方面的研究。E-mail: cgroad@swpu.edu.cn。

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葛亮（1985—），男，湖北咸宁人，博士，教授，主要从事油气安全监检测与控制等方面的研究。E-mail: cgroad@swpu.edu.cn。

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