標(biāo)題: Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 4 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E [打印本頁] 作者: 萬能 時間: 2025-3-21 18:25
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
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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: LANCE 時間: 2025-3-21 20:58 作者: Malcontent 時間: 2025-3-22 00:59
Estimation of Structural Vibration Modal Properties Using a Spike-Based Computing Paradigm, combine spike-based computing and machine-learning-based neural networks that emulate the operation of the human brain. Spiking neural networks have the ability to be easily integrated into neuromorphic hardware, such as Intel’s . chip. The advantages of neuromorphic hardware are its high-speed com作者: BROW 時間: 2025-3-22 06:40 作者: 傾聽 時間: 2025-3-22 08:44
Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deee on damage detection and identification tasks. The main advantage of finite element (FE)-generated data is the substitution of costly and sometimes impossible experiments to acquire data for different healthy and damaged states. On the other hand, numerically generated data is strongly limited on t作者: 做作 時間: 2025-3-22 14:48
Machine Learning-Based Condition Monitoring with Multibody Dynamics Models for Gear Transmission Faata is used to train a convolutional neural network (CNN) which performs damage identification on two experimental damaged states. The multibody dynamics (MBD) model of a two-stage helical gear transmission is first developed and used to model the healthy and the damaged state of the problem. Data i作者: 做作 時間: 2025-3-22 20:42
Structural Damage Detection Framework Using Metaheuristic Algorithms and Optimal Finite Element Moda structure. The recent trends show that there is an increasing interest in the use of machine learning (ML) for SHM systems that rely on the experimentally measured data or artificially collected data to properly train the ML model for classification. The proposed method however is taking another a作者: 媒介 時間: 2025-3-22 23:53 作者: Robust 時間: 2025-3-23 04:25 作者: 種族被根除 時間: 2025-3-23 08:32
Data-Driven Structural Identification for Turbomachinery Blisks,ece of material with uniform sector-to-sector material properties and geometry. However, due to manufacturing tolerances, blisks contain sector-to-sector perturbations in material properties and geometry known as mistuning, which can result in increased response amplitudes due to energy localization作者: 組成 時間: 2025-3-23 11:14 作者: 身心疲憊 時間: 2025-3-23 15:46 作者: 和平主義 時間: 2025-3-23 20:58
On a Description of Aeroplanes and Aeroplane Components Using Irreducible Element Models,e improved by using transfer learning. The transfer learning was aided by generating abstract representations of the components; these abstract representations are called . (IE) models. Such IE models have been applied previously for real-world bridge structures, encoding expert knowledge on the con作者: amputation 時間: 2025-3-24 01:02 作者: PAEAN 時間: 2025-3-24 02:29
Simulation-Based Damage Detection for Composite Structures with Machine Learning Techniques,ealth monitoring (SHM) and damage detection methods for these components. Nondestructive testing (NDT) techniques such as laser Doppler vibrometry (LDV) provide a valuable experimental setting for making measurements with dense grids of points without mass loading the structure. The use of machine l作者: Aerate 時間: 2025-3-24 06:45
Synthesizing Dynamic Time-Series Data for Structures Under Shock Using Generative Adversarial Netwognals, both uni- and multi-variate. However, experimental testing of high-value structures can be cost and time prohibitive. While finite element modeling can generate additional datasets, it lacks the fidelity to reproduce the non-stationarities present in the signal, particularly at the higher end作者: Pelago 時間: 2025-3-24 12:36 作者: Amorous 時間: 2025-3-24 18:46 作者: Indolent 時間: 2025-3-24 21:12 作者: nascent 時間: 2025-3-25 02:50
Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models,om measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort.作者: 分解 時間: 2025-3-25 04:28
2191-5644 s.Deep Learning Gaussian Process Analysis.Real-time Video-based Analysis.Applications to Nonlinear Dynamics and Damage Detection.High-rate Structural Monitoring and Prognostics.978-3-031-04124-2978-3-031-04122-8Series ISSN 2191-5644 Series E-ISSN 2191-5652 作者: 心神不寧 時間: 2025-3-25 07:41 作者: 食物 時間: 2025-3-25 15:11
https://doi.org/10.1007/978-3-319-16598-1 geometry, from simple rigid transformations to fibre bundles. The main aim of the chapter is to consider similarity in data using distance metrics with a special focus on transfer learning and data standardisation/normalisation.作者: laparoscopy 時間: 2025-3-25 19:04 作者: 多余 時間: 2025-3-25 22:21 作者: Ibd810 時間: 2025-3-26 02:56
On Aspects of Geometry in SHM and Population-Based SHM, geometry, from simple rigid transformations to fibre bundles. The main aim of the chapter is to consider similarity in data using distance metrics with a special focus on transfer learning and data standardisation/normalisation.作者: 航海太平洋 時間: 2025-3-26 05:36
Input Estimation of Four-DOF Nonlinear Building Using Probabilistic Recurrent Neural Network, frame building with elastic perfectly plastic springs is considered to evaluate the applicability of the proposed input estimation method to nonlinear dynamic systems. The performance of the network is evaluated on fifteen testing ground motions, and the input estimation is accomplished with high accuracy.作者: integral 時間: 2025-3-26 11:23 作者: 星球的光亮度 時間: 2025-3-26 16:08 作者: 引起痛苦 時間: 2025-3-26 16:50
Deep Reinforcement Learning for Active Structure Stabilization,une, they can struggle to control high-order underactuated systems (which any high-fidelity structure model is guaranteed to be), and they rely on simple formulations of error or cost to minimize. Reinforcement learning provides a framework to learn high-performance control strategies directly from 作者: 過分 時間: 2025-3-27 00:19
Estimation of Structural Vibration Modal Properties Using a Spike-Based Computing Paradigm,alysis using Nengo, a large-scale neural network simulation package. In this work, we implement output-only modal identification techniques that rely on solving the blind source separation problem using spike neural networks to extract the natural frequencies, mode shapes, and damping ratios of a si作者: –scent 時間: 2025-3-27 02:11 作者: 漂浮 時間: 2025-3-27 07:31
Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deehe corresponding areas of the selected damaged cases. The simulated dataset is used after for training of a Deep Learning (DL) Convolutional Neural Network (CNN) classifier. The presented methodology is tested on a lab scale CFRP truss structure for which different health scenarios are considered in作者: 噴出 時間: 2025-3-27 10:52 作者: Brocas-Area 時間: 2025-3-27 14:05
A Robust PCA-Based Framework for Long-Term Condition Monitoring of Civil Infrastructures,deemed to minimize the effect of the EOV. As such, extracting the mapped data from the original data, termed error signals, will remove the EOV effects and can be further used for damage detection. To this end, the Mahalanobis distances of the errors in the test set from the distribution of the erro作者: 瘙癢 時間: 2025-3-27 21:38 作者: 漂泊 時間: 2025-3-27 23:05
Classification of Rail Irregularities from Axle Box Accelerations Using Random Forests and Convolut bear tremendous potential for offering temporally and spatially dense diagnostics of railway infrastructure. While the potential of such a monitoring scheme has been proven, the generalization has been limited due to the small sample sizes in existing studies..We propose a methodology to recognize 作者: OTHER 時間: 2025-3-28 04:16 作者: 我不怕犧牲 時間: 2025-3-28 10:05
On a Description of Aeroplanes and Aeroplane Components Using Irreducible Element Models,ic to aeroplanes, will be developed. Additionally, aeroplanes feature various materials, geometries, and functional components that are not seen in bridges. By attempting to describe an aeroplane, the list of valid geometric, material and contextual labels within the PBSHM is expanded.作者: keloid 時間: 2025-3-28 13:55
Simulation-Based Damage Detection for Composite Structures with Machine Learning Techniques,ning framework. Simulation data is easier to generate than experimental, meaning any added value provided with simulation data is advantageous. A description of the obtained results of damage detection is presented, along with a comparative overview of the different techniques.作者: 沉積物 時間: 2025-3-28 17:35
Synthesizing Dynamic Time-Series Data for Structures Under Shock Using Generative Adversarial Netwos chapter presents a methodology for synthesizing statistically indistinguishable time-series data for a structure under shock. Results show that generative adversarial networks are capable of producing material reminiscent of that obtained through experimental testing. The generated data is compare作者: Epidural-Space 時間: 2025-3-28 21:34 作者: 打算 時間: 2025-3-29 01:02 作者: 輕打 時間: 2025-3-29 06:12
Ajay Krishan Gairola,Vidit Kumaralysis using Nengo, a large-scale neural network simulation package. In this work, we implement output-only modal identification techniques that rely on solving the blind source separation problem using spike neural networks to extract the natural frequencies, mode shapes, and damping ratios of a si作者: 同時發(fā)生 時間: 2025-3-29 10:47 作者: Delude 時間: 2025-3-29 12:37
Ritika Mehra,Phayung Meesad,Dhajvir S. Raihe corresponding areas of the selected damaged cases. The simulated dataset is used after for training of a Deep Learning (DL) Convolutional Neural Network (CNN) classifier. The presented methodology is tested on a lab scale CFRP truss structure for which different health scenarios are considered in作者: Ferritin 時間: 2025-3-29 18:34
Monika Mital,Ashis K. Pani,Suma Damodaranaged condition. A parametric area is inserted into the FE model, changing stiffness and mas to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed Damage Detection Framework. For effective damage localizat作者: ADAGE 時間: 2025-3-29 22:37
René Palacios,Victor Morales-Rochadeemed to minimize the effect of the EOV. As such, extracting the mapped data from the original data, termed error signals, will remove the EOV effects and can be further used for damage detection. To this end, the Mahalanobis distances of the errors in the test set from the distribution of the erro作者: ALB 時間: 2025-3-30 03:52
https://doi.org/10.1007/978-3-030-73819-8modulus which vary randomly from blade to blade. To identify the mistuning within each sector, this approach only uses physical-response data from an individual sector as well as forcing information from traveling-wave excitations. Unlike most previous approaches for blisk mistuning identification, 作者: ONYM 時間: 2025-3-30 07:08 作者: 改變立場 時間: 2025-3-30 11:23 作者: 單純 時間: 2025-3-30 13:29 作者: 漫步 時間: 2025-3-30 17:00 作者: 調(diào)情 時間: 2025-3-30 23:13
Anila Baby,Akshada Shinde,Komal Dandges chapter presents a methodology for synthesizing statistically indistinguishable time-series data for a structure under shock. Results show that generative adversarial networks are capable of producing material reminiscent of that obtained through experimental testing. The generated data is compare作者: 糾纏,纏繞 時間: 2025-3-31 04:03
Remote Production Monitoring Systemverse set of geometries and boundary conditions in its simulation-generated training set to ensure generalizability in the CNN. Using the U-net CNN architecture for image segmentation with a pretrained ResNet model as an encoder, a model was trained, validated, and tested which exhibits superior per作者: Neutral-Spine 時間: 2025-3-31 08:35
A Study on Development of PKL Powerlied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted fr作者: oblique 時間: 2025-3-31 13:04
M. Nagaraju,Priyanka Chawla,Rajeev Tiwarie must implement control methods to protect both the structure and any contents from a wide variety of loadings, ranging from typical operational use to extreme cases such as natural disasters, including hurricanes or earthquakes. Current structures depend almost entirely on passive control systems;作者: SOW 時間: 2025-3-31 15:13
Ajay Krishan Gairola,Vidit Kumar combine spike-based computing and machine-learning-based neural networks that emulate the operation of the human brain. Spiking neural networks have the ability to be easily integrated into neuromorphic hardware, such as Intel’s . chip. The advantages of neuromorphic hardware are its high-speed com作者: 煩憂 時間: 2025-3-31 20:55 作者: CLASH 時間: 2025-4-1 01:33
Ritika Mehra,Phayung Meesad,Dhajvir S. Raie on damage detection and identification tasks. The main advantage of finite element (FE)-generated data is the substitution of costly and sometimes impossible experiments to acquire data for different healthy and damaged states. On the other hand, numerically generated data is strongly limited on t
