24,137 research outputs found
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
This paper presents to the best of our knowledge the first end-to-end object
tracking approach which directly maps from raw sensor input to object tracks in
sensor space without requiring any feature engineering or system identification
in the form of plant or sensor models. Specifically, our system accepts a
stream of raw sensor data at one end and, in real-time, produces an estimate of
the entire environment state at the output including even occluded objects. We
achieve this by framing the problem as a deep learning task and exploit
sequence models in the form of recurrent neural networks to learn a mapping
from sensor measurements to object tracks. In particular, we propose a learning
method based on a form of input dropout which allows learning in an
unsupervised manner, only based on raw, occluded sensor data without access to
ground-truth annotations. We demonstrate our approach using a synthetic dataset
designed to mimic the task of tracking objects in 2D laser data -- as commonly
encountered in robotics applications -- and show that it learns to track many
dynamic objects despite occlusions and the presence of sensor noise.Comment: Published in The Thirtieth AAAI Conference on Artificial Intelligence
(AAAI-16), Video: https://youtu.be/cdeWCpfUGWc, Code:
http://mrg.robots.ox.ac.uk/mrg_people/peter-ondruska
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Learning-based Analysis on the Exploitability of Security Vulnerabilities
The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation
This paper presents an approach for employing artificial neural networks (NN)
to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation.
The assimilation methods are tested in the Simplified Parameterizations
PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation
model (AGCM), using synthetic observational data simulating localization of
balloon soundings. For the data assimilation scheme, the supervised NN, the
multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the
analysis from the local ensemble transform Kalman filter (LETKF). After the
training process, the method using the MLP-NN is seen as a function of data
assimilation. The NN were trained with data from first three months of 1982,
1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle
using MLP-NN were performed with synthetic observations for January 1985. The
numerical results demonstrate the effectiveness of the NN technique for
atmospheric data assimilation. The results of the NN analyses are very close to
the results from the LETKF analyses, the differences of the monthly average of
absolute temperature analyses is of order 0.02. The simulations show that the
major advantage of using the MLP-NN is better computational performance, since
the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN
is 90 times faster than cycle assimilation with LETKF for the numerical
experiment.Comment: 17 pages, 16 figures, monthly weather revie
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