Article(id=1319726467009855747, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726426404803327, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20250119, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=1742745600000, revisedDateStr=2025-03-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1792576909067, onlineDateStr=2026-10-21, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792576909067, onlineIssueDateStr=2026-10-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792576909067, creator=13701087609, updateTime=1792576909067, updator=13701087609, issue=Issue{id=1319726426404803327, tenantId=1146029695717560320, journalId=1263187385517883426, year='2025', volume='53', issue='12', pageStart='3433', pageEnd='3832', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792576899386, creator='13701087609', updateTime=1792577516933, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319729016953749818, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726426404803327, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319729016953749819, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319726426404803327, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3779, endPage=3789, ext={EN=ArticleExt(id=1319726470646317316, articleId=1319726467009855747, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Linear Classification Model for Element Diffusion Computation at Fiber Optic Interfaces, columnId=1319726165087077079, journalTitle=Journal of the Chinese Ceramic Society, columnName=Research Article, runingTitle=null, highlight=null, articleAbstract=
Introduction Hard optical fiber imaging components as two-dimensional array imaging materials comprised of tens of millions to billions of micro- and nano-scale optical glass fibers have extensive applications in numerous optical systems, including low-light-level image intensifiers and space target detection systems. Resolution and contrast are crucial indicators affecting the performance of these components. Elemental diffusion at the core-clad interface results in a gradient distribution of material refractive index rather than the ideal abrupt distribution, thereby degrading the resolution and contrast of the components. It is necessary for the demands for refined manufacturing processes of these components to gain a comprehensive understanding of the diffusion behavior and distribution status of elements at the core-clad interface. At present, energy dispersive spectrometry (EDS) is often employed as a characterization tool for elemental distribution status, yet it suffers from a low image resolution (20 nm) and lacks corresponding quantitative calculation methods. In this study, high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) was utilized to acquire high-precision images of elemental spatial distribution. Furthermore, a multi-model fusion machine learning approach was proposed to address the challenges of characterization and quantitative analysis in the study of elemental migration at the core-clad interface of hard optical fiber imaging components.
Methods In this study, monofilaments undergoing various thermal processes were fabricated by a drawing technique. High-Angle Annular Dark-Field (HAADF) images were captured and utilized to train Tree-SVM/LDA models. These models enabled pixel-level core-clad classification and x-y two-dimensional spatial classification, thereby yielding quantitative results on elemental interface diffusion. In addition, the reliability of the models was also analyzed.
The Tree-SVM/LDA model is grounded on the following assumptions, i.e., element Si as a key component in the formation of both core and cladding glass network structures remains stable during the diffusion process. The deviation distances of other elements are benchmarked against that of Si, the average deviation (D) is defined as an arithmetic mean of the migration distances of all elements within the tested range, serving to characterize the overall diffusion behavior of elements rather than individual extreme values, elements undergo unidirectional migration, consistent with the direction of the concentration gradient, manifesting as the perpendicular direction to the interface in monofilament samples, the transition zone at the core-cladding interface is formed through the superposition of diffusion behaviors of different elements, and the algorithmic procedure of model encompasses the following steps (i.e., constructing training and prediction datasets; training the decision tree model; developing a labeled prediction dataset; and obtaining classification hyperplane parameters).
Results and discussion The results obtained by SVM and LDA models both indicate the thermodynamic principles at higher resolutions, with the deviation D_K∈[10, 40]nm, D_Ba∈[0, 11]nm, and D_Ti∈[0,13]nm. At lower resolutions, where the width of a single pixel in the raw data is 3 nm, the computational error of the models is amplified threefold, leading to the results that deviate from actual principles. At 810 ℃, the SVM model exhibits a poor data separability due to the low content of Ti in the cladding region, resulting in abnormal calculation results. This phenomenon underscores the superiority of the LDA model over SVM in terms of training set diversity, demonstrating a greater generalization capability. The calculation results of the SVM model and LDA model show a good consistency, while also presenting regular differences, i.e., the boundaries obtained by the LDA model tend to skew towards the cladding region relative to those of the SVM model. This regularity may be associated with differences in model characteristics and the element distribution patterns in the HAADF images of the core and cladding regions, having a further in-depth research. In general, the SVM model achieves higher accuracy than the LDA model. However, the LDA model possesses a better generalization capability.
Conclusions The migration capabilities of the elements K, Ti, and Ba intensified as the wire drawing temperature increased. Specifically, the average migration distance of element K could range from 10 nm to 40 nm, while that of elements Ti and Ba could span from 0 to 13 nm. Affecting the reliability of models were since the spatial resolution of the raw data could be in atomic or sub-atomic scale, models could be sensitive to data anomalies, thus requiring repeated training, elements with great diffusion capabilities could decrease the accuracy of the model, while simultaneously enhancing its generalization capability. The SVM model demonstrated superior accuracy, compared to the LDA model, having a weaker generalization capability. The SVM model could be recommended at a high quality of the raw data, whereas the results of the LDA model could be more realistic at a low quality of the raw data.
, authors=Shujin ZHANG, Jing ZHANG, Haoyang YU, Jinsheng JIA, authorsList=Shujin ZHANG, Jing ZHANG, Haoyang YU, Jinsheng JIA, authorCompany=null, correspAuthors=Jinsheng JIA, authorNote=
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针对硬质光纤传像元件芯皮界面元素迁移研究中存在的表征与定量分析难题，提出一种基于多模型融合的Tree-SVM/LDA计算模型。该模型通过整合决策树（Tree）、线性支持向量机（SVM）和线性判别分析算法，建立了基于扫描透射电子显微镜高角环形暗场像（HAADF-STEM）的元素迁移定量分析方法，重点构建了特定元素平均迁移距离D的计算体系。采用电阻加热配套棒管的方法制备具有温度梯度的单丝样品体系，通过获取多尺度分辨率（0.5~3.0 nm）的HAADF图像数据进行模型训练与评估。定量分析表明：在优化拉丝温度区间内，K元素平均迁移距离D_K∈［10，40］nm，Ba和Ti元素平均迁移距离分别为D_Ba∈［0，11］nm、D_Ti∈［0，13］nm。高分辨率（0.5 nm）条件下模型可靠性显著提升，其中，Tree-SVM模型分类准确率优于Tree-LDA模型，但Tree-LDA模型展现出更优的泛化性能。该单次热过程元素扩散计算框架，为后续多次热过程元素迁移的非线性计算模型研究提供了一种重要的方法。
, authors=张淑瑾, 张敬, 于浩洋, 贾金升, authorsList=张淑瑾, 张敬, 于浩洋, 贾金升, authorCompany=null, correspAuthors=贾金升, authorNote=
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贾金升（1971—），男，高级工程师。
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ZHANG Shujin (2000-), female, Master Candidate. E-mail: zhangsj000207@163.com
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ZHANG Shujin (2000-), female, Master Candidate. E-mail: zhangsj000207@163.com
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张淑瑾（2000—），女，硕士研究生。
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张淑瑾（2000—），女，硕士研究生。
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