31,782 research outputs found
SAFS: A Deep Feature Selection Approach for Precision Medicine
In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others
Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques
Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P) and UPV/EHU (UFI 11/29). The authors would also like to thank Euskampus and ONA-EDM for their support in this project
PYRO-NN: Python Reconstruction Operators in Neural Networks
Purpose: Recently, several attempts were conducted to transfer deep learning
to medical image reconstruction. An increasingly number of publications follow
the concept of embedding the CT reconstruction as a known operator into a
neural network. However, most of the approaches presented lack an efficient CT
reconstruction framework fully integrated into deep learning environments. As a
result, many approaches are forced to use workarounds for mathematically
unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to
embed known operators into the prevalent deep learning framework Tensorflow.
The current status includes state-of-the-art parallel-, fan- and cone-beam
projectors and back-projectors accelerated with CUDA provided as Tensorflow
layers. On top, the framework provides a high level Python API to conduct FBP
and iterative reconstruction experiments with data from real CT systems.
Results: The framework provides all necessary algorithms and tools to design
end-to-end neural network pipelines with integrated CT reconstruction
algorithms. The high level Python API allows a simple use of the layers as
known from Tensorflow. To demonstrate the capabilities of the layers, the
framework comes with three baseline experiments showing a cone-beam short scan
FDK reconstruction, a CT reconstruction filter learning setup, and a TV
regularized iterative reconstruction. All algorithms and tools are referenced
to a scientific publication and are compared to existing non deep learning
reconstruction frameworks. The framework is available as open-source software
at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the
prevalent deep learning framework Tensorflow and allows to setup end-to-end
trainable neural networks in the medical image reconstruction context. We
believe that the framework will be a step towards reproducible researchComment: V1: Submitted to Medical Physics, 11 pages, 7 figure
A Precise Electrical Disturbance Generator for Neural Network Training with Real Level Output
Power Quality is defined as the study of the quality of electric power
lines. The detection and classification of the different disturbances which cause
power quality problems is a difficult task which requires a high level of engineering
expertise. Thus, neural networks are usually a good choice for the detection
and classification of these disturbances. This paper describes a powerful
tool, developed by the Institute for Natural Resources and Agrobiology at the
Scientific Research Council (CSIC) and the Electronic Technology Department
at the University of Seville, which generates electrical patterns of disturbances
for the training of neural networks for PQ tasks. This system has been expanded
to other applications (as comparative test between PQ meters, or test of effects
of power-line disturbances on equipment) through the addition of a specifically
developed high fidelity power amplifier, which allows the generation of disturbed
signals at real levels.Ministerio de Ciencia y TecnologÃa DPI2006-15467-C02-0
A Wiener-Laguerre model of VIV forces given recent cylinder velocities
Slender structures immersed in a cross flow can experience vibrations induced
by vortex shedding (VIV), which cause fatigue damage and other problems. VIV
models in engineering use today tend to operate in the frequency domain. A time
domain model would allow to capture the chaotic nature of VIV and to model
interactions with other loads and non-linearities. Such a model was developed
in the present work: for each cross section, recent velocity history is
compressed using Laguerre polynomials. The compressed information is used to
enter an interpolation function to predict the instantaneous force, allowing to
step the dynamic analysis. An offshore riser was modeled in this way: Some
analyses provided an unusually fine level of realism, while in other analyses,
the riser fell into an unphysical pattern of vibration. It is concluded that
the concept is promissing, yet that more work is needed to understand orbit
stability and related issues, in order to further progress towards an
engineering tool
Why I tense up when you watch me: inferior parietal cortex mediates an audience’s influence on motor performance
The presence of an evaluative audience can alter skilled motor performance through changes in force output. To investigate how this is mediated within the brain, we emulated real-time social monitoring of participants’ performance of a fine grip task during functional magnetic resonance neuroimaging. We observed an increase in force output during social evaluation that was accompanied by focal reductions in activity within bilateral inferior parietal cortex. Moreover, deactivation of the left inferior parietal cortex predicted both inter- and intra-individual differences in socially-induced change in grip force. Social evaluation also enhanced activation within the posterior superior temporal sulcus, which conveys visual information about others’ actions to the inferior parietal cortex. Interestingly, functional connectivity between these two regions was attenuated by social evaluation. Our data suggest that social evaluation can vary force output through the altered engagement of inferior parietal cortex; a region implicated in sensorimotor integration necessary for object manipulation, and a component of the action-observation network which integrates and facilitates performance of observed actions. Social-evaluative situations may induce high-level representational incoherence between one’s own intentioned action and the perceived intention of others which, by uncoupling the dynamics of sensorimotor facilitation, could ultimately perturbe motor output
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
Abnormal foot postures during gait are common sources of pain and pathologies of the
lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect
these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure
measurement system is developed to identify areas with higher or lower pressure load. This system
is composed of an embedded system placed in the insole and a user application. The instrumented
insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth
module. The user application receives and shows the insole pressure information in real-time and,
finally, provides information about the foot posture. In order to identify the different pressure states
and obtain the final information of the study with greater accuracy, a Deep Learning neural network
system has been integrated into the user application. The neural network can be trained using a
stored dataset in order to obtain the classification results in real-time. Results prove that this system
provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users.Ministerio de EconomÃa y Competitividad TEC2016-77785-
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