16,651 research outputs found
Comparison between mirror Langmuir probe and gas puff imaging measurements of intermittent fluctuations in the Alcator C-Mod scrape-off layer
Statistical properties of the scrape-off layer (SOL) plasma fluctuations are
studied in ohmically heated plasmas in the Alcator C-Mod tokamak. For the first
time, plasma fluctuations as well as parameters that describe the fluctuations
are compared across measurements from a mirror Langmuir probe (MLP) and from
gas-puff imaging (GPI) that sample the same plasma discharge. This comparison
is complemented by an analysis of line emission time-series data, synthesized
from the MLP electron density and temperature measurements. The fluctuations
observed by the MLP and GPI typically display relative fluctuation amplitudes
of order unity together with positively skewed and flattened probability
density functions. Such data time series are well described by an established
stochastic framework which model the data as a superposition of uncorrelated,
two-sided exponential pulses. The most important parameter of the process is
the intermittency parameter, {\gamma} = {\tau}d / {\tau}w where {\tau}d denotes
the duration time of a single pulse and {\tau}w gives the average waiting time
between consecutive pulses. Here we show, using a new deconvolution method,
that these parameters can be consistently estimated from different statistics
of the data. We also show that the statistical properties of the data sampled
by the MLP and GPI diagnostic are very similar. Finally, a comparison of the
GPI signal to the synthetic line-emission time series suggests that the
measured emission intensity can not be explained solely by a simplified model
which neglects neutral particle dynamics
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Endowing the robotic systems with cognitive capabilities for recognizing
daily activities of humans is an important challenge, which requires
sophisticated and novel approaches. Most of the proposed approaches explore
pattern recognition techniques which are generally based on hand-crafted
features or learned features. In this paper, a novel Hierarchal Multichannel
Deep Residual Network (HMResNet) model is proposed for robotic systems to
recognize daily human activities in the ambient environments. The introduced
model is comprised of multilevel fusion layers. The proposed Multichannel 1D
Deep Residual Network model is, at the features level, combined with a
Bottleneck MLP neural network to automatically extract robust features
regardless of the hardware configuration and, at the decision level, is fully
connected with an MLP neural network to recognize daily human activities.
Empirical experiments on real-world datasets and an online demonstration are
used for validating the proposed model. Results demonstrated that the proposed
model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study
The study and monitoring of wildlife has always been a subject of great
interest. Studying the behavior of wildlife animals is a very complex task due to
the difficulties to track them and classify their behaviors through the collected
sensory information. Novel technology allows designing low cost systems that
facilitate these tasks. There are currently some commercial solutions to this problem;
however, it is not possible to obtain a highly accurate classification due to the
lack of gathered information. In this work, we propose an animal behavior recognition,
classification and monitoring system based on a smart collar device provided
with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron
(MLP) to classify the possible animal behavior based on the collected sensory
information. Experimental results over horse gaits case study show that the recognition
system achieves an accuracy of up to 95.6%.Junta de AndalucĂa P12-TIC-130
A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery
Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe
A pragmatic approach to multi-class classification
We present a novel hierarchical approach to multi-class classification which
is generic in that it can be applied to different classification models (e.g.,
support vector machines, perceptrons), and makes no explicit assumptions about
the probabilistic structure of the problem as it is usually done in multi-class
classification. By adding a cascade of additional classifiers, each of which
receives the previous classifier's output in addition to regular input data,
the approach harnesses unused information that manifests itself in the form of,
e.g., correlations between predicted classes. Using multilayer perceptrons as a
classification model, we demonstrate the validity of this approach by testing
it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015,
Bruges, Belgium. 201
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