55 research outputs found
Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images
Wind energy is expected to be one of the leading ways to achieve the goals of
the Paris Agreement but it in turn heavily depends on effective management of
its operations and maintenance (O&M) costs. Blade failures account for
one-third of all O&M costs thus making accurate detection of blade damages,
especially cracks, very important for sustained operations and cost savings.
Traditionally, damage inspection has been a completely manual process thus
making it subjective, error-prone, and time-consuming. Hence in this work, we
bring more objectivity, scalability, and repeatability in our damage inspection
process, using deep learning, to miss fewer cracks. We build a deep learning
model trained on a large dataset of blade damages, collected by our drone-based
inspection, to correctly detect cracks. Our model is already in production and
has processed more than a million damages with a recall of 0.96. We also focus
on model interpretability using class activation maps to get a peek into the
model workings. The model not only performs as good as human experts but also
better in certain tricky cases. Thus, in this work, we aim to increase wind
energy adoption by decreasing one of its major hurdles - the O\&M costs
resulting from missing blade failures like cracks.Comment: NeurIPS 2021 Workshop on Tackling Climate Change with Machine
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Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
In this work, we develop a neural network-based method to convert a noisy
motion signal generated from segmenting rebinned list-mode cardiac SPECT
images, to that of a high-quality surrogate signal, such as those seen from
external motion tracking systems (EMTs). This synthetic surrogate will be used
as input to our pre-existing motion correction technique developed for EMT
surrogate signals. In our method, we test two families of neural networks to
translate noisy internal motion to external surrogate: 1) fully connected
networks and 2) convolutional neural networks. Our dataset consists of cardiac
perfusion SPECT acquisitions for which cardiac motion was estimated (input:
center-of-count-mass - COM signals) in conjunction with a respiratory surrogate
motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT
signals). We obtained an average R-score of 0.76 between the predicted
surrogate and the EMT signal. Our goal is to lay a foundation to guide the
optimization of neural networks for respiratory motion correction from SPECT
without the need for an EMT.Comment: Medical Imaging Meets NeurIP
Wide-ranging functions of E2F4 in transcriptional activation and repression revealed by genome-wide analysis
The E2F family of transcription factors has important roles in cell cycle progression. E2F4 is an E2F family member that has been proposed to be primarily a repressor of transcription, but the scope of its binding activity and functions in transcriptional regulation is not fully known. We used ChIP sequencing (ChIP-seq) to identify around 16 000 E2F4 binding sites which potentially regulate 7346 downstream target genes with wide-ranging functions in DNA repair, cell cycle regulation, apoptosis, and other processes. While half of all E2F4 binding sites (56%) occurred near transcription start sites (TSSs), ∼20% of sites occurred more than 20 kb away from any annotated TSS. These distal sites showed histone modifications suggesting that E2F4 may function as a long-range regulator, which we confirmed by functional experimental assays on a subset. Overexpression of E2F4 and its transcriptional cofactors of the retinoblastoma (Rb) family and its binding partner DP-1 revealed that E2F4 acts as an activator as well as a repressor. E2F4 binding sites also occurred near regulatory elements for miRNAs such as let-7a and mir-17, suggestive of regulation of miRNAs by E2F4. Taken together, our genome-wide analysis provided evidence of versatile roles of E2F4 and insights into its functions
Fully Bayesian inference for latent variable Gaussian process models
Real engineering and scientific applications often involve one or more
qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly
accommodate qualitative inputs. The recently introduced latent variable
Gaussian process (LVGP) overcomes this issue by first mapping each qualitative
factor to underlying latent variables (LVs), and then uses any standard GP
covariance function over these LVs. The LVs are estimated similarly to the
other GP hyperparameters through maximum likelihood estimation, and then
plugged into the prediction expressions. However, this plug-in approach will
not account for uncertainty in estimation of the LVs, which can be significant
especially with limited training data. In this work, we develop a fully
Bayesian approach for the LVGP model and for visualizing the effects of the
qualitative inputs via their LVs. We also develop approximations for scaling up
LVGPs and fully Bayesian inference for the LVGP hyperparameters. We conduct
numerical studies comparing plug-in inference against fully Bayesian inference
over a few engineering models and material design applications. In contrast to
previous studies on standard GP modeling that have largely concluded that a
fully Bayesian treatment offers limited improvements, our results show that for
LVGP modeling it offers significant improvements in prediction accuracy and
uncertainty quantification over the plug-in approach
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but
is challenging due to the prohibitive cost of searching the vast design space
of chemistry, structure, and synthesis methods. Bayesian Optimization (BO)
employs uncertainty-aware machine learning models to select promising designs
to evaluate, hence reducing the cost. However, BO with mixed numerical and
categorical variables, which is of particular interest in materials design, has
not been well studied. In this work, we survey frequentist and Bayesian
approaches to uncertainty quantification of machine learning with mixed
variables. We then conduct a systematic comparative study of their performances
in BO using a popular representative model from each group, the random
forest-based Lolo model (frequentist) and the latent variable Gaussian process
model (Bayesian). We examine the efficacy of the two models in the optimization
of mathematical functions, as well as properties of structural and functional
materials, where we observe performance differences as related to problem
dimensionality and complexity. By investigating the machine learning models'
predictive and uncertainty estimation capabilities, we provide interpretations
of the observed performance differences. Our results provide practical guidance
on choosing between frequentist and Bayesian uncertainty-aware machine learning
models for mixed-variable BO in materials design
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