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Digital image correlation-based structural state detection through deep learning
《结构与土木工程前沿(英文)》 2022年 第16卷 第1期 页码 45-56 doi: 10.1007/s11709-021-0777-x
关键词: structural state detection deep learning digital image correlation vibration signal steel frame
Jing HU, Pengfei LIU, Bernhard STEINAUER
《结构与土木工程前沿(英文)》 2017年 第11卷 第3期 页码 329-337 doi: 10.1007/s11709-017-0407-9
关键词: asphalt mixture microstructure morphology digital image processing fatigue damage
Deformation field and crack analyses of concrete using digital image correlation method
Yijie HUANG, Xujia HE, Qing WANG, Jianzhuang XIAO
《结构与土木工程前沿(英文)》 2019年 第13卷 第5期 页码 1183-1199 doi: 10.1007/s11709-019-0545-3
关键词: deformation filed distribution crack development digital image correlation method mechanical properties water-cement ratio characteristics of deformation and crack
《机械工程前沿(英文)》 2006年 第1卷 第1期 页码 85-89 doi: 10.1007/s11465-005-0023-6
A novel fuzzy clustering method based on chaos immune evolutionary algorithm (CIEFCM) is presented to solve fuzzy edge detection problems in image processing. In CIEFCM, a tiny disturbance is added to a filial generation group using a chaos variable and the disturbance amplitude is adjusted step by step, which greatly improves the colony diversity of the immune evolution algorithm (IEA). The experimental results show that the method not only can correctly detect the fuzzy edge and exiguous edge but can evidently improve the searching efficiency of fuzzy clustering algorithm based on IEA.
关键词: disturbance amplitude disturbance diversity generation processing
Seyed Hamid KALALI; Hamid ESKANDARI-NADDAF; Seyed Ali EMAMIAN
《结构与土木工程前沿(英文)》 2022年 第16卷 第12期 页码 1633-1652 doi: 10.1007/s11709-022-0876-3
关键词: fiber-reinforced concrete forta-ferro and polypropylene fiber fracture process cracking behavior digital image correlation
Deep learning in digital pathology image analysis: a survey
Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu
《医学前沿(英文)》 2020年 第14卷 第4期 页码 470-487 doi: 10.1007/s11684-020-0782-9
关键词: pathology deep learning segmentation detection classification
Three-dimensional reconstruction of light microscopy image sections: present and future
null
《医学前沿(英文)》 2015年 第9卷 第1期 页码 30-45 doi: 10.1007/s11684-014-0337-z
Three-dimensional (3D) image reconstruction technologies can reveal previously hidden microstructures in human tissue. However, the lack of ideal, non-destructive cross-sectional imaging techniques is still a problem. Despite some drawbacks, histological sectioning remains one of the most powerful methods for accurate high-resolution representation of tissue structures. Computer technologies can produce 3D representations of interesting human tissue and organs that have been serial-sectioned, dyed or stained, imaged, and segmented for 3D visualization. 3D reconstruction also has great potential in the fields of tissue engineering and 3D printing. This article outlines the most common methods for 3D tissue section reconstruction. We describe the most important academic concepts in this field, and provide critical explanations and comparisons. We also note key steps in the reconstruction procedures, and highlight recent progress in the development of new reconstruction methods.
关键词: microtomy 3D imaging computer-assisted image processing 3D printing tissue scaffold
《结构与土木工程前沿(英文)》 页码 1228-1248 doi: 10.1007/s11709-023-0931-8
关键词: UHPC MT-DIC flexural behavior tensile behavior steel fiber polypropylene fiber
简述图像被动取证技术 Review
林祥, 李建华, 王士林, 刘伟聪, 程峰, 黄潇洒
《工程(英文)》 2018年 第4卷 第1期 页码 29-39 doi: 10.1016/j.eng.2018.02.008
随着图像编辑和篡改技术越发成熟,数字图像的真实性通常难以从视觉上直接分辨。为了检测数字图像篡改,在过去十年内,已经出现多种数字图像取证技术。其中,主动取证方法需要嵌入额外信息。相比之下,被动取证方法因为其适用场景更广而更加流行,也吸引了学术界和工业界越来越多的研究兴趣。一般而言,被动取证基于以下依据来检测图像伪造:图像采集或存储过程中会在原始图像中遗留某些固有的模式特征,或者在图像存储或编辑过程中会留下某些特定的模式特征。通过分析上述模式特征,可以验证图像的真实性。被动数字取证方法正处于快速发展之中,本文简要回顾其发展,并全面介绍该研究领域的最新进展。根据所追踪痕迹的不同,这些取证方法被分为3 类,即采集痕迹法、存储痕迹法和编辑痕迹法。我们将逐一详解这些方法的取证场景、基本原理和研究现状。此外,我们讨论了当前图像取证方法的主要局限,并指出了该领域一些可能的研究方向和关键问题。
Chen WANG, Yuching WU, Jianzhuang XIAO
《结构与土木工程前沿(英文)》 2018年 第12卷 第4期 页码 461-473 doi: 10.1007/s11709-017-0441-7
关键词: RAC nano-indentation digital image multiscale microscopic randomness homogenization
Amit SHIULY; Debabrata DUTTA; Achintya MONDAL
《结构与土木工程前沿(英文)》 2022年 第16卷 第3期 页码 347-358 doi: 10.1007/s11709-022-0819-z
关键词: support vector machine deep convolutional neural network microscope digital image curing period
How do digital technologies improve supply chain resilience in the COVID-19 pandemic?
《工程管理前沿(英文)》 页码 39-50 doi: 10.1007/s42524-022-0230-4
关键词: digital technologies supply chain resilience information processing theory COVID-19 China
基于图像处理的超高速撞击碎片云的动态建模与损伤估计 Research Article
曾入,宋燕,吕伟臻
《信息与电子工程前沿(英文)》 2022年 第23卷 第4期 页码 555-570 doi: 10.1631/FITEE.2100049
关键词: 碎片云;超高速撞击;图像处理;损伤估计
基于学习自适应区域选择的自动增强图像 None
Na LI, Jian ZHAN
《信息与电子工程前沿(英文)》 2019年 第20卷 第2期 页码 206-221 doi: 10.1631/FITEE.1700125
Quantification of coarse aggregate shape in concrete
Xianglin GU,Yvonne TRAN,Li HONG
《结构与土木工程前沿(英文)》 2014年 第8卷 第3期 页码 308-321 doi: 10.1007/s11709-014-0266-6
关键词: coarse aggregate form angularity digital image analysis statistical distribution splitting tensile strength
标题 作者 时间 类型 操作
A study on fatigue damage of asphalt mixture under different compaction using 3D-microstructural characteristics
Jing HU, Pengfei LIU, Bernhard STEINAUER
期刊论文
Deformation field and crack analyses of concrete using digital image correlation method
Yijie HUANG, Xujia HE, Qing WANG, Jianzhuang XIAO
期刊论文
a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in ImageProcessing
期刊论文
fracture process in forta and polypropylene fiber-reinforced concrete using experimental analysis and digitalimage correlation
Seyed Hamid KALALI; Hamid ESKANDARI-NADDAF; Seyed Ali EMAMIAN
期刊论文
Deep learning in digital pathology image analysis: a survey
Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu
期刊论文
polypropylene-reinforced ultra-high-performance concrete through numerical analyses and experimental multi-target digitalimage correlation
期刊论文
Three-scale stochastic homogenization of elastic recycled aggregate concrete based on nano-indentation digital
Chen WANG, Yuching WU, Jianzhuang XIAO
期刊论文
Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques
Amit SHIULY; Debabrata DUTTA; Achintya MONDAL
期刊论文