30,562 research outputs found
Neural networks in virtual reference tuning
This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch inputoutput data, by particularising nonlinear VRT and suitably computing gradients backpropagating in time. The flexibility of gradient computation with neural networks also allows alternative block diagrams with extra inputs to be considered. The neural approach to VRT in a closed-loop setup is compared to the linear VRFT one in a simulated crane example. © 2011 Elsevier Ltd. All rights reserved.A. Esparza is grateful to the project GVPRE/2008/116 financed by Generalitat Valenciana. The authors are also grateful to the financial support of Grants dpi2008-06731-c02-01, dpi2011-27845-c02-01 (Spanish Government) and prometeo/2008/088 (Generalitat Valenciana).Esparza Peidro, A.; Sala, A.; Albertos Pérez, P. (2011). Neural networks in virtual reference tuning. Engineering Applications of Artificial Intelligence. 24(6):983-995. https://doi.org/10.1016/j.engappai.2011.04.00398399524
Visual Servoing from Deep Neural Networks
We present a deep neural network-based method to perform high-precision,
robust and real-time 6 DOF visual servoing. The paper describes how to create a
dataset simulating various perturbations (occlusions and lighting conditions)
from a single real-world image of the scene. A convolutional neural network is
fine-tuned using this dataset to estimate the relative pose between two images
of the same scene. The output of the network is then employed in a visual
servoing control scheme. The method converges robustly even in difficult
real-world settings with strong lighting variations and occlusions.A
positioning error of less than one millimeter is obtained in experiments with a
6 DOF robot.Comment: fixed authors lis
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning
Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources
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