Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov-Sinai entropy | |
Xia, Da-Hai; Ma, Chao; Song, Shizhe; Jin, Weixian; Behnamian, Yashar; Fan, Hongqiang; Wang, Jihui; Gao, Zhiming; Hu, Wenbin; Fan, HQ (reprint author), Shanghai Univ, Lab Microstruct, Inst Mat, Sch Mat Sci & Engn, Shanghai 200072, Peoples R China.; Xia, DH (reprint author), Tianjin Univ, Tianjin Key Lab Composite & Funct Mat, Sch Mat Sci & Engn, Tianjin 300072, Peoples R China. | |
2017-05-15 | |
发表期刊 | CORROSION SCIENCE
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ISSN | 0010-938X |
卷号 | 120页码:251-256 |
摘要 | Fuzzy Kolmogorov-Sinai (K-S) entropy was used to characterize the irregularity of the spatial information distribution in a corrosion image. The fuzzy K-S entropy was measured for horizontal and vertical orientations of corrosion images of steel samples exposed to a marine atmosphere over 191 days. The fuzzy K-S entropy for horizontal and vertical orientations decreased as the corrosion propagated. A relationship between the fuzzy K-S entropy and the weight loss in the steel sample can be presented with a linear mathematical expression. Fuzzy K-S entropy was found to be a semiquantitative and fast method to quantify atmospheric corrosion. (C) 2017 Elsevier Ltd. All rights reserved.; Fuzzy Kolmogorov-Sinai (K-S) entropy was used to characterize the irregularity of the spatial information distribution in a corrosion image. The fuzzy K-S entropy was measured for horizontal and vertical orientations of corrosion images of steel samples exposed to a marine atmosphere over 191 days. The fuzzy K-S entropy for horizontal and vertical orientations decreased as the corrosion propagated. A relationship between the fuzzy K-S entropy and the weight loss in the steel sample can be presented with a linear mathematical expression. Fuzzy K-S entropy was found to be a semiquantitative and fast method to quantify atmospheric corrosion. (C) 2017 Elsevier Ltd. All rights reserved. |
部门归属 | [xia, da-hai ; ma, chao ; song, shizhe ; wang, jihui ; gao, zhiming ; hu, wenbin] tianjin univ, tianjin key lab composite & funct mat, sch mat sci & engn, tianjin 300354, peoples r china ; [fan, hongqiang] shanghai univ, lab microstruct, inst mat, sch mat sci & engn, shanghai 200072, peoples r china ; [behnamian, yashar] univ alberta, dept chem & mat engn, edmonton, ab t6g 2v4, canada ; [jin, weixian] cent res inst iron & steel, zhoushan marine corros inst, zhoushan 316000, zhejiang, peoples r china ; [xia, da-hai] chinese acad sci, inst met res, cas key lab nucl mat & safety assessment, shenyang 110016, liaoning, peoples r china |
关键词 | Carbon Steel Weight Loss Atmospheric Corrosion Kolmogorov-sinai Entropy Image Processing |
学科领域 | Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering |
资助者 | Major State Basic Research Development Program (973 Program) [2014CB046805]; National Natural Science Foundation of China [51131007, 51671144, 51471117]; Key Project of Tianjin Natural Science Foundation [13JCZDJC29500]; CAS Key laboratory of Nuclear Materials and Safety Assessment (Institute of Metal Research, Chinese Academy of Sciences, China) [2016NMSAKF02] |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000401378100023 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.imr.ac.cn/handle/321006/78138 |
专题 | 中国科学院金属研究所 |
通讯作者 | Fan, HQ (reprint author), Shanghai Univ, Lab Microstruct, Inst Mat, Sch Mat Sci & Engn, Shanghai 200072, Peoples R China.; Xia, DH (reprint author), Tianjin Univ, Tianjin Key Lab Composite & Funct Mat, Sch Mat Sci & Engn, Tianjin 300072, Peoples R China. |
推荐引用方式 GB/T 7714 | Xia, Da-Hai,Ma, Chao,Song, Shizhe,et al. Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov-Sinai entropy[J]. CORROSION SCIENCE,2017,120:251-256. |
APA | Xia, Da-Hai.,Ma, Chao.,Song, Shizhe.,Jin, Weixian.,Behnamian, Yashar.,...&Xia, DH .(2017).Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov-Sinai entropy.CORROSION SCIENCE,120,251-256. |
MLA | Xia, Da-Hai,et al."Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov-Sinai entropy".CORROSION SCIENCE 120(2017):251-256. |
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