2,409 research outputs found
Applied Mathematics and Computational Physics
As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications
Temperature- and Time-Dependent Dielectric Measurements and Modelling on Curing of Polymer Composites
In this book a test set for dielectric measurements at 2.45 GHz during curing of polymer composites is developed. Fast reconstruction is solved using a neural network algorithm. Modeling of the curing process at 2.45 GHz using both dielectric constant and dielectric loss factor results in more accurate model compared to low frequency modelling that only uses the loss factor. Effect of various hardeners and different amount of filler is investigated
A machine learning approach to Structural Health Monitoring with a view towards wind turbines
The work of this thesis is centred around Structural Health Monitoring (SHM) and
is divided into three main parts.
The thesis starts by exploring di�erent architectures of auto-association. These are
evaluated in order to demonstrate the ability of nonlinear auto-association of neural
networks with one nonlinear hidden layer as it is of great interest in terms of reduced
computational complexity. It is shown that linear PCA lacks performance for novelty
detection. The novel key study which is revealed ampli�es that single hidden layer
auto-associators are not performing in a similar fashion to PCA.
The second part of this study concerns formulating pattern recognition algorithms for
SHM purposes which could be used in the wind energy sector as SHM regarding this
research �eld is still in an embryonic level compared to civil and aerospace engineering.
The purpose of this part is to investigate the e�ectiveness and performance of such
methods in structural damage detection. Experimental measurements such as high
frequency responses functions (FRFs) were extracted from a 9m WT blade throughout
a full-scale continuous fatigue test. A preliminary analysis of a model regression of
virtual SCADA data from an o�shore wind farm is also proposed using Gaussian
processes and neural network regression techniques.
The third part of this work introduces robust multivariate statistical methods into
SHM by inclusively revealing how the in
uence of environmental and operational
variation a�ects features that are sensitive to damage. The algorithms that are
described are the Minimum Covariance Determinant Estimator (MCD) and the Minimum Volume Enclosing Ellipsoid (MVEE). These robust outlier methods are
inclusive and in turn there is no need to pre-determine an undamaged condition
data set, o�ering an important advantage over other multivariate methodologies.
Two real life experimental applications to the Z24 bridge and to an aircraft wing
are analysed. Furthermore, with the usage of the robust measures, the data variable
correlation reveals linear or nonlinear connections
Temperature- and Time-Dependent Dielectric Measurements and Modelling on Curing of Polymer Composites
In this book a test set for dielectric measurements at 2.45 GHz during curing of polymer composites is developed. Fast reconstruction is solved using a neural network algorithm. Modeling of the curing process at 2.45 GHz using both dielectric constant and dielectric loss factor results in more accurate model compared to low frequency modelling that only uses the loss factor. Effect of various hardeners and different amount of filler is investigated
An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery
Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
In this paper, we address the problem of Received Signal Strength map
reconstruction based on location-dependent radio measurements and utilizing
side knowledge about the local region; for example, city plan, terrain height,
gateway position. Depending on the quantity of such prior side information, we
employ Neural Architecture Search to find an optimized Neural Network model
with the best architecture for each of the supposed settings. We demonstrate
that using additional side information enhances the final accuracy of the
Received Signal Strength map reconstruction on three datasets that correspond
to three major cities, particularly in sub-areas near the gateways where larger
variations of the average received signal power are typically observed.Comment: 42 pages, 39 figure
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
As the complexity of modern manufacturing technologies increases, traditional
trial-and-error design, which requires iterative and expensive simulations,
becomes unreliable and time-consuming. This difficulty is especially
significant for the design of hot-stamped safety-critical components, such as
ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure
manufacturability, scalar-based Artificial-Intelligence-empowered surrogate
modelling (SAISM) has been investigated and implemented, which can allow
real-time manufacturability-constrained structural design optimisation.
However, SAISM suffers from low accuracy and generalisability, and usually
requires a high volume of training samples. To solve this problem, an
image-based Artificial-intelligence-empowered surrogate modelling (IAISM)
approach is developed in this research, in combination with an
auto-decoder-based blank shape generator. The IAISM, which is based on a
Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field
of the as-formed component given an arbitrary blank shape. Excellent prediction
performance of IAISM is achieved with only 256 training samples, which
indicates the small-data learning nature of engineering AI tasks using
structured data representations. The trained auto-decoder, trained
Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank
optimisation by modifying the latent vector. The optimiser can rapidly find
blank shapes that satisfy manufacturability criteria. As a high-accuracy and
generalisable surrogate modelling and optimisation tool, the proposed pipeline
is promising to be integrated into a full-chain digital twin to conduct
real-time, multi-objective design optimisation.Comment: 32 pages, 11 figure
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