標(biāo)題: Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 3 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E [打印本頁] 作者: Coagulant 時(shí)間: 2025-3-21 17:19
書目名稱Data Science in Engineering, Volume 9影響因子(影響力)
書目名稱Data Science in Engineering, Volume 9影響因子(影響力)學(xué)科排名
書目名稱Data Science in Engineering, Volume 9網(wǎng)絡(luò)公開度
書目名稱Data Science in Engineering, Volume 9網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science in Engineering, Volume 9被引頻次
書目名稱Data Science in Engineering, Volume 9被引頻次學(xué)科排名
書目名稱Data Science in Engineering, Volume 9年度引用
書目名稱Data Science in Engineering, Volume 9年度引用學(xué)科排名
書目名稱Data Science in Engineering, Volume 9讀者反饋
書目名稱Data Science in Engineering, Volume 9讀者反饋學(xué)科排名
作者: Ethics 時(shí)間: 2025-3-21 20:31
Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaur remaining useful life. SHM therefore enables a more efficient maintenance and operational decision-making process. Traditionally, SHM has been focussed on a single structure or system. In most maintenance strategies, detected damages or defects are repaired before they can progress further. However作者: Brain-Imaging 時(shí)間: 2025-3-22 02:42
Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaurhquake. The obtained information is also used for the estimation of the financial need requests, which are crucial for the rapid initiation of the recovery acts after an earthquake. This article aims at providing a data-based framework that guides a reconnaissance surveying team by actively learning作者: 不可救藥 時(shí)間: 2025-3-22 04:59 作者: flourish 時(shí)間: 2025-3-22 11:55 作者: 撫慰 時(shí)間: 2025-3-22 15:50 作者: 撫慰 時(shí)間: 2025-3-22 18:36 作者: Demonstrate 時(shí)間: 2025-3-22 22:05
Laura-Nicoleta Ivanciu,Gabriel Olteanferent structures. The attempts have been focussed on homogeneous and heterogeneous populations. A more general approach to transferring knowledge between structures is by considering all plausible structures as points on a multidimensional base manifold and building a fibre bundle. The idea is quit作者: GRATE 時(shí)間: 2025-3-23 04:09 作者: Ascribe 時(shí)間: 2025-3-23 09:32
https://doi.org/10.1007/978-1-4614-7245-2e detection. The advancements of both machine learning (ML) algorithms and non-destructive testing (NDT) techniques offer the correct setting to successfully tackle these challenges. In this work, non-destructive testing was performed with a scanning laser Doppler vibrometer (SLDV), in order to obta作者: Ruptured-Disk 時(shí)間: 2025-3-23 10:17 作者: GRUEL 時(shí)間: 2025-3-23 14:12 作者: 干旱 時(shí)間: 2025-3-23 19:56 作者: hauteur 時(shí)間: 2025-3-24 00:21
Vijayaraj Nagarajan,Mohamed O. Elasrit make it difficult to discern damage from environmental and operational (E&O) variability. Therefore, an improved process for identifying features that are sensitive to damage while insensitive to E&O effects is needed. In this study a SHM approach that utilizes causality metrics is proposed. The a作者: Mast-Cell 時(shí)間: 2025-3-24 05:25
Sk Mohiuddin,Samir Malakar,Ram Sarkary resources to prepare a large volume of the ground truth of a dataset labeled at the pixel level. Hybrid crack segmentation (Kang et al., Autom Constr 118:103291, 2020) is based on the integration of a faster region-based convolutional neural network (faster R-CNN) as the deep learning-based object作者: 向外供接觸 時(shí)間: 2025-3-24 10:11
https://doi.org/10.1007/978-3-030-75529-4d dynamics technology for structural health monitoring is being explored in order to optimize cost and improve performance. However, current imagers capture both the dynamic and static portions of a scene when only the dynamic portion is needed. The capture of both static and dynamic portions of a s作者: 盟軍 時(shí)間: 2025-3-24 14:35
Sk Mohiuddin,Samir Malakar,Ram Sarkarat are not captured by the mathematical model assumed. These models are often reduced order models (ROM) that have simplified physics or have been obtained through data-driven techniques, such as trained neural networks. In this paper, we evaluate two data assimilation techniques to perform paramete作者: Morsel 時(shí)間: 2025-3-24 16:51 作者: 滔滔不絕的人 時(shí)間: 2025-3-24 21:19 作者: output 時(shí)間: 2025-3-24 23:09 作者: 緊張過度 時(shí)間: 2025-3-25 04:23
Data Science in Engineering, Volume 9978-3-030-76004-5Series ISSN 2191-5644 Series E-ISSN 2191-5652 作者: Antarctic 時(shí)間: 2025-3-25 09:05
https://doi.org/10.1007/978-3-030-76004-5data science; Modal Analysis; Structural Dynamics; Dynamic Substructures; Structural Engineering; Confere作者: 燦爛 時(shí)間: 2025-3-25 12:43
978-3-030-76006-9The Society for Experimental Mechanics, Inc. 2022作者: ACME 時(shí)間: 2025-3-25 16:25 作者: 無禮回復(fù) 時(shí)間: 2025-3-25 21:28
2191-5644 indings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:.Data Science in Engineering Applications.Engineering Mathematics.Computational Methods in Engineering.978-3-030-76006-9978-3-030-76004-5Series ISSN 2191-5644 Series E-ISSN 2191-5652 作者: FLIP 時(shí)間: 2025-3-26 02:33
Sk Mohiuddin,Samir Malakar,Ram Sarkar detection method and modified tubularity flow field (TuFF) as computer vision-based segmentation. In this paper, we further conducted experimental studies to investigate the performance of the developed hybrid concrete crack segmentation method using additional images with complex backgrounds in challenging environments.作者: 絕種 時(shí)間: 2025-3-26 04:21
Conference proceedings 2022 nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:.Data Science in Engineering Applications.Engine作者: allude 時(shí)間: 2025-3-26 11:00
Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaurevolves. The framework is further studied using experimental measurements from shock-loaded electronic packages. The results show that the proposed approach can effectively classify different structure characteristics, including damage-sensitive features.作者: 忘川河 時(shí)間: 2025-3-26 14:05
Marius George Linguraru,Ronald M. Summerstate-space models for estimating dispersion curves from FRFs. The vector fitting algorithm creates a data-driven model of the unit cell from noisy FRFs. A multi-unit cell lattice is simulated from data-driven models using FBS. Additionally, the paper investigates tuning of elastic band gaps by changing the mass and the stiffness of the unit cells.作者: 類型 時(shí)間: 2025-3-26 17:34
https://doi.org/10.1007/978-1-4614-7245-2ect end-to-end classification of the SLDV’s data and the classification of data obtained through feature engineering with modal analysis. Finally, results are presented for multiple plates of different materials and containing defects of varying size.作者: 獨(dú)裁政府 時(shí)間: 2025-3-26 22:15
Sk Mohiuddin,Samir Malakar,Ram Sarkarnuous and continuous Gaussian noises. We use ensemble Kalman filter and Kalman-Bucy filter techniques on representative structures, such as the slender flat beam with nonlinear features to illustrate how this approach could be applied to more complex structures.作者: jarring 時(shí)間: 2025-3-27 04:50
Priyajit Sen,Rajat Pandit,Debabrata Sarddarine the knowledge from the two sources automatically to form a grey-box model. This chapter explores the formulation and use of a prior based on partial knowledge of the system of interest. The benefits of the approach, particularly in extrapolation, are shown in an example of wind turbine power curve modelling.作者: 陪審團(tuán) 時(shí)間: 2025-3-27 06:16 作者: indemnify 時(shí)間: 2025-3-27 11:36 作者: evasive 時(shí)間: 2025-3-27 15:37
Damage Localization on Lightweight Structures with Non-destructive Testing and Machine Learning Tecect end-to-end classification of the SLDV’s data and the classification of data obtained through feature engineering with modal analysis. Finally, results are presented for multiple plates of different materials and containing defects of varying size.作者: 量被毀壞 時(shí)間: 2025-3-27 19:23 作者: 低三下四之人 時(shí)間: 2025-3-27 23:51 作者: 小說 時(shí)間: 2025-3-28 02:53 作者: right-atrium 時(shí)間: 2025-3-28 08:04 作者: 返老還童 時(shí)間: 2025-3-28 12:38
Towards Population-Based Structural Health Monitoring, Part V: Networks and Databases, remaining useful life. SHM therefore enables a more efficient maintenance and operational decision-making process. Traditionally, SHM has been focussed on a single structure or system. In most maintenance strategies, detected damages or defects are repaired before they can progress further. However作者: concise 時(shí)間: 2025-3-28 16:44 作者: Neuropeptides 時(shí)間: 2025-3-28 21:06 作者: 勉強(qiáng) 時(shí)間: 2025-3-28 22:57 作者: glisten 時(shí)間: 2025-3-29 05:02 作者: esculent 時(shí)間: 2025-3-29 09:11
On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks,ences about structures and their condition. Such methods almost exclusively rely on the quality of the data. Within the SHM discipline, data do not always suffice to build models with satisfactory accuracy for given tasks. Even worse, data may be completely missing from one’s dataset, regarding the 作者: Noisome 時(shí)間: 2025-3-29 14:14
On an Application of Graph Neural Networks in Population-Based SHM,ferent structures. The attempts have been focussed on homogeneous and heterogeneous populations. A more general approach to transferring knowledge between structures is by considering all plausible structures as points on a multidimensional base manifold and building a fibre bundle. The idea is quit作者: Gudgeon 時(shí)間: 2025-3-29 17:01 作者: 連詞 時(shí)間: 2025-3-29 20:35 作者: 火光在搖曳 時(shí)間: 2025-3-30 03:27
Challenges for SHM from Structural Repairs: An Outlier-Informed Domain Adaptation Approach,ill be the same as those experienced when the method is deployed. However, structural repairs alter the physical properties of the system, leading to a change in structural response. This change in response leads to a shift in the data distributions from the pre- to post-repair states—known as domai作者: 比喻好 時(shí)間: 2025-3-30 05:30 作者: Militia 時(shí)間: 2025-3-30 11:01
An Unsupervised Deep Auto-encoder with One-Class Support Vector Machine for Damage Detection,ing data from not only undamaged structural scenarios but also various damaged scenarios (DSs) of the monitored structures. However, acquiring sufficient training data from various DSs for the infrastructures in service is impractical, and labeling huge amounts of training data with specific structu作者: insomnia 時(shí)間: 2025-3-30 14:06 作者: CURT 時(shí)間: 2025-3-30 16:51
Hybrid Concrete Crack Segmentation and Quantification Across Complex Backgrounds Without a Large Try resources to prepare a large volume of the ground truth of a dataset labeled at the pixel level. Hybrid crack segmentation (Kang et al., Autom Constr 118:103291, 2020) is based on the integration of a faster region-based convolutional neural network (faster R-CNN) as the deep learning-based object作者: Autobiography 時(shí)間: 2025-3-30 20:57 作者: 群居動(dòng)物 時(shí)間: 2025-3-31 02:42 作者: 神圣在玷污 時(shí)間: 2025-3-31 07:52 作者: folliculitis 時(shí)間: 2025-3-31 10:19 作者: Exploit 時(shí)間: 2025-3-31 14:09
Grey-Box Modelling via Gaussian Process Mean Functions for Mechanical Systems,operational structures, via the Gaussian process model. Here, the term grey-box model is used to refer to one that has a physics-based (white box) and data-based (black box) component. In this chapter, domain knowledge is modelled by the mean function of a Gaussian process prior, while its covarianc作者: micronutrients 時(shí)間: 2025-3-31 20:10
Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaurin a much broader understanding of the damage that can occur in all structures. This paper discusses the most important aspects of using databases in population-based SHM and will also focus on the exploitation of the unique Echo framework, providing a platform for diagnostics across populations of 作者: GEN 時(shí)間: 2025-3-31 23:47
Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaurvide a maximal information gain and reduce the overall travel time required for the surveying team. A joint selection of structural and earthquake parameters, along with the sparse damage observations, are used to train the Gaussian process regression model for damage emulations. To validate the pro作者: 徹底檢查 時(shí)間: 2025-4-1 02:47
https://doi.org/10.1007/978-981-19-2519-1rce Research Lab’s DROPBEAR apparatus, exhibiting accuracy on par with MLPs trained offline. Results show that these two algorithms serve as viable candidates for real-time structural health monitoring applications.作者: Custodian 時(shí)間: 2025-4-1 06:23
Optimization Algorithms Surpassing Metaphortor-level mistuning identification technique using a feed-forward neural network is presented. Using this approach, mistuning prediction for individual sectors is achieved using only a subset of forced responses from within a sector. The knowledge or use of system modal response information is not r作者: 只有 時(shí)間: 2025-4-1 12:31
Laura-Nicoleta Ivanciu,Gabriel Olteanng that the structure’s response is represented by points in a manifold, part of the space will be formed due to variations in external conditions affecting the structure. This idea proves efficient in SHM, as it is exploited to generate structural data for specific values of environmental coefficie作者: Gossamer 時(shí)間: 2025-4-1 17:13 作者: 放氣 時(shí)間: 2025-4-1 21:22
https://doi.org/10.1007/978-1-4614-7245-2tional data-based methods on the post-repair data. Transfer learning, in the form of domain adaptation, provides a solution to this problem, allowing knowledge from the pre-repair labels to be transferred to the post-repair dataset by forming a shared latent space where the pre- and post-repair data作者: 租約 時(shí)間: 2025-4-1 23:53
https://doi.org/10.1007/978-3-540-70778-3the population, creating a single classification model that generalises across the complete population. This paper explores ., a branch of transfer learning where datasets have inconsistent feature spaces, i.e. the dimensions of datasets from one structure are different to those from another. In PBS
