Article(id=1322559848244433372, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, 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=1739030400000, receivedDateStr=2025-02-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252439795, onlineDateStr=2026-10-29, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252439795, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252439795, creator=13701087609, updateTime=1793252439795, updator=13701087609, issue=Issue{id=1322559792544072145, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='1', pageStart='1', pageEnd='306', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252426515, creator='13701087609', updateTime=1793252675339, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322560836246286849, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322560836246286850, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559792544072145, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=246, endPage=254, ext={EN=ArticleExt(id=1322559848399622621, articleId=1322559848244433372, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Identification Method of Froth Conditions in the Multi-Cell of Coal Slime Flotation Based on 3D Point Cloud Data, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of spatial information loss in traditional 2D images and the one-sidedness of single-cell data during the identification of coal slime flotation froth, a multi-cell feature fusion method based on 3D point cloud data was proposed. The Percipio Infrared Binocular FM855-E1 industrial camera was used to collect the 3D point cloud data of the froth surfaces from each flotation cell. Preprocessing and data augmentation techniques were adopted to optimize the dataset. A PointNet network with non-shared weights was utilized to extract the high-dimensional features of each cell. After dimensionality reduction by a multi-layer perceptron, these features were fused into multi-cell forth features through a concatenation function. Finally, the working condition categories were determined by a combination of a multi-layer perceptron and the Softmax function. The experimental results show that the classification accuracy of the multi-cell network reaches 94.89%, which is higher than other networks, the loss value is reduced to 0.216 9, with excellent robustness and generalization ability. It is proved that the multi-cell forth feature fusion method can significantly improve the recognition accuracy of flotation conditions, and provide a high-reliability three-dimensional visual solution for the intelligent flotation of coal slime.

, authors=Pinyu LI, Ranfeng WANG, Xiang FU, Yang LIU, Changming ZHANG, Xinkai QIN, Zhiheng DOU, authorsList=Pinyu LI, Ranfeng WANG, Xiang FU, Yang LIU, Changming ZHANG, Xinkai QIN, Zhiheng DOU, authorCompany=null, correspAuthors=Ranfeng WANG, 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=1322559848605143518, articleId=1322559848244433372, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于3D点云数据的多槽煤泥浮选泡沫工况识别方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对传统二维图像在煤泥浮选泡沫识别中存在空间信息丢失及单槽数据片面性问题，提出一种基于三维点云数据的多槽特征融合方法。该方法通过图漾红外双目FM855-E1工业相机采集浮选机每个浮选槽的泡沫三维点云数据，采用预处理与数据增强技术优化数据集，利用非共享权重的PointNet网络提取各槽高维特征，经多层感知机降维后，通过拼接函数融合为多槽泡沫特征，最终由多层感知机和Softmax函数实现工况类别判定。试验结果表明：多槽网络的分类准确率达94.89%，显著优于其他网络，损失值降低至0.216 9，具有良好的鲁棒性和泛化能力。研究证明，多槽泡沫特征融合显著提升了浮选工况识别精度，为煤泥浮选智能化提供了高可靠性的三维视觉解决方案。

, authors=李品钰, 王然风, 付翔, 刘洋, 张长明, 秦新凯, 窦治衡, authorsList=李品钰, 王然风, 付翔, 刘洋, 张长明, 秦新凯, 窦治衡, authorCompany=null, correspAuthors=王然风, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=wYoHnPoi6p05/koE04htKA==, magXml=xkEElj4FqVmKaQwB86MdnA==, pdfUrl=null, pdf=ichd0Gfwjq8GyHcdZLKLjQ==, pdfFileSize=2681577, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=rkaMf3QQzciCA+pAq0W2rQ==, mapNumber=null, fund=null)}, authors=[Author(id=1322559849020379619, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559848244433372, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=leepinyu@126.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322559849100071397, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559848244433372, authorId=1322559849020379619, language=EN, stringName=Pinyu LI, firstName=Pinyu, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322559849162985958, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559848244433372, authorId=1322559849020379619, language=CN, stringName=李品钰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=太原理工大学 矿业工程学院，山西 太原　030024, bio={"content":"

李品钰（1999—），女，山东泰安人，硕士研究生，主要研究方向为矿物加工智能化检测。E-mail:leepinyu@126.com

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李品钰（1999—），女，山东泰安人，硕士研究生，主要研究方向为矿物加工智能化检测。E-mail:leepinyu@126.com

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