Article(id=1321893362072933233, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760544000000, receivedDateStr=2025-10-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093537109, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093537109, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093537109, creator=13701087609, updateTime=1793093537109, updator=13701087609, issue=Issue{id=1321893331622286028, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='7', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093529849, creator='13701087609', updateTime=1793094587578, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897768357048353, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897768357048354, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=133, endPage=142, ext={EN=ArticleExt(id=1321893363146675058, articleId=1321893362072933233, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Gas Concentration Prediction of Tunneling Working Face Based on Improved KAN-iTransformer, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The tunneling working face is an area prone to frequent gas accidents during coal mining. In view of the strong nonlinear and spatiotemporal dynamic correlation characteristics of gas concentration variations, a prediction model for gas concentration at the tunneling working face, named KAN-iTransformer, was proposed, which integrates the Kolmogorov-Arnold Networks (KAN) and an improved iTransformer (Inverted Transformer). Firstly, a relevant feature dataset for gas concentration prediction was constructed through data preprocessing and Spearman correlation coefficient analysis. Secondly, based on the inverted Transformer structure, the complete variable sequence was regarded as a token, and a graph attention mechanism was introduced to explicitly model the spatial topological relationships of the sensor network, enabling effective modeling of the dynamic dependencies among sensors. Thirdly, the learnable activation functions of KAN were used to replace the traditional linear weights, enhancing the model's ability to characterize complex nonlinear relationships among multi-source monitoring parameters. Finally, a hierarchical response mechanism combining point prediction and interval estimation was proposed to enable advanced dynamic hierarchical response to gas overrun risk. The field test results show that the MSE, MAE and MAPE of the improved KAN-iTransformer model in single-step prediction are 0.000 307, 0.012 921 and 2.321 373%, respectively, significantly outperforming both the iTransformer and Transformer models. In multi-step prediction, the MSE of the improved KAN-iTransformer model is 0.000 913, which is 63.7% and 80.9% lower than those of the comparison models, respectively, demonstrating better long-term prediction performance and effectively realizing accurate prediction and dynamic early warning of gas concentration at the tunneling working face.

, authors=Xiaojing Zhang¹, Xinyu Li¹, Shaoqi Kong², authorsList=Xiaojing Zhang, Xinyu Li, Shaoqi Kong, authorCompany=null, correspAuthors=Shaoqi Kong, 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=1321893363998118771, articleId=1321893362072933233, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于改进KAN-iTransformer的掘进工作面瓦斯浓度预测研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

掘进工作面是煤矿开采过程中瓦斯事故频发的区域，针对其浓度变化呈现出的强非线性及时空动态相关性特点，提出了一种融合科尔莫格罗夫-阿诺德网络（KAN）与改进iTransformer的掘进工作面瓦斯浓度预测模型（KAN-iTransformer）。首先，通过数据预处理和Spearman相关系数分析构建了瓦斯浓度预测相关特征数据集；其次，基于倒置Transformer结构将完整变量序列视为标记，并引入图注意力机制显式建模传感器网络的空间拓扑关联，增加了传感器之间动态依赖的有效建模；再次，利用KAN的可学习激活函数替代传统线性权重，提升了模型对多源监测参数间复杂非线性关系的表征能力；最后，提出了融合点预测与区间估计的分级响应机制，实现了瓦斯超限风险的超前动态分级响应。现场试验结果表明，改进KAN-iTransformer模型在单步预测中的MSE、MAE和MAPE分别为0.000 307，0.012 921和2.321 373%，明显优于iTransformer与Transformer两种对比模型；在多步预测中，改进KAN-iTransformer模型的MSE为0.000 913，较对比模型分别降低了63.7%与80.9%，展现出更好的长期预测性能，有效实现了掘进工作面瓦斯浓度的精准预测与动态预警。

, authors=张晓景¹, 李新宇¹, 孔少奇², authorsList=张晓景, 李新宇, 孔少奇, authorCompany=null, correspAuthors=孔少奇, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=prEoWAD6oLrEWSM+H74ycg==, magXml=l9ZD21RByyljXGwPzJha2g==, pdfUrl=null, pdf=tB7h1uTo2YvA6tOa0u7xAg==, pdfFileSize=2371505, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=aR2VS9FKYaE8+SV0eZgB+g==, mapNumber=null, fund=null)}, authors=[Author(id=1321893364891505531, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893362072933233, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=3778127414@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893365172523901, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893362072933233, authorId=1321893364891505531, language=EN, stringName=Xiaojing Zhang, firstName=Xiaojing, middleName=null, lastName=Zhang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Shanxi Xinzhou Shenda Daqiaogou Coal Industry Co., Ltd., Xinzhou, Shanxi 034099, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893365273187198, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893362072933233, authorId=1321893364891505531, language=CN, stringName=张晓景, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．山西忻州神达大桥沟煤业有限公司，山西 忻州市　034099, bio={"content":"

张晓景（1986—），男，山西朔州人，高级工程师，主要从事采矿工程和生产技术等方面的研究工作。E-mail:3778127414@qq.com

"}, bioImg=null, bioContent=

张晓景（1986—），男，山西朔州人，高级工程师，主要从事采矿工程和生产技术等方面的研究工作。E-mail:3778127414@qq.com

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