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標(biāo)題: Titlebook: Artificial Intelligence in Vision-Based Structural Health Monitoring; Khalid M. Mosalam,Yuqing Gao Book 2024 The Editor(s) (if applicable) [打印本頁(yè)]

作者: Glycemic-Index    時(shí)間: 2025-3-21 16:49
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作者: facilitate    時(shí)間: 2025-3-21 21:14
Hugo W. Moser,Gerald V. RaymondStructural damage segmentation is another key task in vision-based SHM. As introduced in Chap.?2, images are labeled pixel by pixel and segmentation algorithms and models aim to recognize all pixels, group a region of pixels with the same label, and assign a class label for each region to match the ground truth.
作者: Anterior    時(shí)間: 2025-3-22 04:04

作者: Commonwealth    時(shí)間: 2025-3-22 06:04

作者: 柔美流暢    時(shí)間: 2025-3-22 11:20
Progress in Inflammation ResearchIn previous chapters, promising results have been achieved using AI, e.g., DL-based models, in vision-based SHM problems. However, the internal working principle in AI, especially for the DL model, is hard to understand by a human and is treated as a discouraging “black box”, especially for the inquisitive engineers.
作者: 愛哭    時(shí)間: 2025-3-22 15:39
Structural Vision Data Collection and DatasetImages and videos are the two most commonly used data types in vision-based SHM. The image represents the instantaneous state in the structure and the video provides continuous changes of the state of the structure, which is composed of a sequence of frames.
作者: modifier    時(shí)間: 2025-3-22 18:48

作者: 傻瓜    時(shí)間: 2025-3-22 22:34

作者: 領(lǐng)先    時(shí)間: 2025-3-23 03:40
Semi-Supervised LearningPrevious chapters demonstrate the effectiveness of ML and DL under the supervised learning setting, where all training data are well-labeled, refer to Sect.?3.1.
作者: 思想靈活    時(shí)間: 2025-3-23 06:46

作者: Soliloquy    時(shí)間: 2025-3-23 12:47

作者: originality    時(shí)間: 2025-3-23 17:03

作者: 亞麻制品    時(shí)間: 2025-3-23 19:27

作者: 預(yù)知    時(shí)間: 2025-3-24 00:36
https://doi.org/10.1007/978-3-642-00446-9ple periods of research and development [.]. The concept of DL originated from the study in neuroscience and its objective was to simulate the mechanisms of the human brain to understand and interpret data, e.g., images, texts, and sounds.
作者: 不透明    時(shí)間: 2025-3-24 03:40

作者: 壓倒    時(shí)間: 2025-3-24 08:28

作者: 青少年    時(shí)間: 2025-3-24 14:30

作者: 狗窩    時(shí)間: 2025-3-24 14:56

作者: 類型    時(shí)間: 2025-3-24 21:03

作者: FLAGR    時(shí)間: 2025-3-25 02:31

作者: Trypsin    時(shí)間: 2025-3-25 06:36
Artificial Intelligence in Vision-Based Structural Health Monitoring978-3-031-52407-3Series ISSN 2573-3168 Series E-ISSN 2573-3176
作者: Awning    時(shí)間: 2025-3-25 11:27
The Great Prize of Mathematical Sciences,ion detection. Soukup and Huber-M?rk [.] applied CNN?to detect steel surface defects of the railway, which is a binary classification problem. Cha et al. [.] used a deep CNN?to detect concrete cracks as a binary classification without calculating the defect features.
作者: Ablation    時(shí)間: 2025-3-25 13:51

作者: nettle    時(shí)間: 2025-3-25 18:25

作者: insightful    時(shí)間: 2025-3-25 21:17
Drake C. Mitchell,Burton J. Litmana. If the SSL method is employed, both labeled and unlabeled data can be utilized simultaneously, which can improve the AI model performance to some extent, as demonstrated with the BSS-GAN in Chap.?..
作者: CLOUT    時(shí)間: 2025-3-26 03:37
Khalid M. Mosalam,Yuqing GaoComprehensive review of the rapidly expanding field of vision-based SHM using artificial intelligence approaches.Includes comprehensive details about the procedure of conducting AI approaches.With exa
作者: Integrate    時(shí)間: 2025-3-26 07:07

作者: 裙帶關(guān)系    時(shí)間: 2025-3-26 10:40

作者: 改變    時(shí)間: 2025-3-26 15:28

作者: Hearten    時(shí)間: 2025-3-26 18:27
Structural Damage Localizationbased SHM. As introduced in Chap.?., damage locations are represented by bounding boxes?with coordinates and localization algorithms and models aim to conduct a regression task to accurately predict the coordinates, which closely match the ground truth.
作者: 牌帶來(lái)    時(shí)間: 2025-3-26 21:00
Active Learninga. If the SSL method is employed, both labeled and unlabeled data can be utilized simultaneously, which can improve the AI model performance to some extent, as demonstrated with the BSS-GAN in Chap.?..
作者: Axon895    時(shí)間: 2025-3-27 01:10

作者: Exclaim    時(shí)間: 2025-3-27 06:06

作者: 不能逃避    時(shí)間: 2025-3-27 10:34
,Le Grand Prix des Sciences Mathématiques,ween the input data and their corresponding labels. Compared with the other two categories, supervised learning is the most active branch in ML?research and is widely used in many current ML?applications.
作者: 打谷工具    時(shí)間: 2025-3-27 15:46

作者: 大量殺死    時(shí)間: 2025-3-27 18:39

作者: RAFF    時(shí)間: 2025-3-28 01:13
Basics of Machine Learningween the input data and their corresponding labels. Compared with the other two categories, supervised learning is the most active branch in ML?research and is widely used in many current ML?applications.
作者: 媒介    時(shí)間: 2025-3-28 03:58
Multi-task Learningion or separately focus on finding the location or area of the damage as a localization or segmentation problem. Abundant information in the images from multiple sources and inter-task relationships are not fully exploited.
作者: Disk199    時(shí)間: 2025-3-28 08:40
Practical applications of oils and fats,aset are ambiguous and subjective, because there is no explicit definition or specific threshold value to clearly separate them. It is also inappropriate and sometimes impossible to infer the results simply by examining the scale of the collected dataset without validation experiments.
作者: Decongestant    時(shí)間: 2025-3-28 12:07
Structural Image Classificationaset are ambiguous and subjective, because there is no explicit definition or specific threshold value to clearly separate them. It is also inappropriate and sometimes impossible to infer the results simply by examining the scale of the collected dataset without validation experiments.
作者: Wordlist    時(shí)間: 2025-3-28 15:17
Book 2024oring (SHM). In this data explosion epoch, AI-aided SHM and rapid damage assessment after natural hazards have become of great interest in civil and structural engineering, where using machine and deep learning in vision-based SHM brings new research direction. As researchers begin to apply these co
作者: ostracize    時(shí)間: 2025-3-28 21:44
Book 2024ion-based SHM? .This book introduces and implements the state-of-the-art machine learning and deep learning technologies for vision-based SHM applications. Specifically, corresponding to the above-mentioned scientific questions, it consists of: (1) motivation, background & progress of AI-aided visio
作者: 支架    時(shí)間: 2025-3-28 23:22

作者: misshapen    時(shí)間: 2025-3-29 04:52
Correction to: Artificial Intelligence in Vision-Based Structural Health Monitoring,
作者: Incise    時(shí)間: 2025-3-29 09:06
Artificial Intelligence in Vision-Based Structural Health Monitoring
作者: FRAUD    時(shí)間: 2025-3-29 14:15
Introduction,tructural damage in an instrumented structural system and can be classified in terms of their scale–local or global damage detection methods. Whereas global methods employ numerical models that intake global characteristics of a structure (such as modal frequencies) that are indicative of possible d
作者: GULF    時(shí)間: 2025-3-29 19:06
Vision Tasks in Structural Imagesion detection. Soukup and Huber-M?rk [.] applied CNN?to detect steel surface defects of the railway, which is a binary classification problem. Cha et al. [.] used a deep CNN?to detect concrete cracks as a binary classification without calculating the defect features.
作者: arterioles    時(shí)間: 2025-3-29 21:00
Basics of Machine Learningng, . learning, and . learning based on the data characteristics. Supervised learning trains a model that can learn and infer the mapping function between the input data and their corresponding labels. Compared with the other two categories, supervised learning is the most active branch in ML?resear
作者: candle    時(shí)間: 2025-3-30 03:10
Basics of Deep Learningple periods of research and development [.]. The concept of DL originated from the study in neuroscience and its objective was to simulate the mechanisms of the human brain to understand and interpret data, e.g., images, texts, and sounds.
作者: 人類    時(shí)間: 2025-3-30 04:43
Structural Image Classificationhnologies into the field of SHM?is not straightforward. Therefore, in this chapter, the feasibility of applying AI?methods in vision-based SHM?is explored. This is mainly evaluated by the accuracy and efficiency of the trained AI?models. ML?and DL?can achieve very accurate or promising results throu
作者: Muscularis    時(shí)間: 2025-3-30 09:32
Structural Damage Localizationbased SHM. As introduced in Chap.?., damage locations are represented by bounding boxes?with coordinates and localization algorithms and models aim to conduct a regression task to accurately predict the coordinates, which closely match the ground truth.
作者: 鈍劍    時(shí)間: 2025-3-30 12:53
Active Learninga. If the SSL method is employed, both labeled and unlabeled data can be utilized simultaneously, which can improve the AI model performance to some extent, as demonstrated with the BSS-GAN in Chap.?..
作者: 落葉劑    時(shí)間: 2025-3-30 18:56
Multi-task Learning. However, many previous studies solely work on the existence of damage in the images and directly treat the problem as a single attribute classification or separately focus on finding the location or area of the damage as a localization or segmentation problem. Abundant information in the images fr




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