Search results for "ARN"
showing 10 items of 8344 documents
Training Secondary Education Teachers through the Prism of Sustainability: The Case of the Universitat de València
2018
Designing the training of future teachers through holistic and interdisciplinary visions is vital to developing coherent contents, epistemologies, and methodologies that put Education for Sustainability into action. The research presented here analyzes the teaching guides from the curriculum for the Master&rsquo
Towards Intelligent IoT Networks: Reinforcement Learning for Reliable Backscatter Communications
2019
Backscatter communication is becoming the focal point of research for low-powered Internet of things (IoT). However, the intelligence aspect of the backscattering devices is not well-defined. Since future IoT networks are going to be a formidable platform of intelligent sensing devices operating in a self-organizing manner, it is necessary to incorporate learning capabilities in backscatter devices. Motivated by this objective, this paper aims to employ reinforcement learning for improving the performance of backscatter networks. In particular, a multicluster backscatter communication model is developed for shortrange information sharing. This is followed by a power allocation algorithm usi…
Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm
2019
International audience; This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms.The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law t…
Improving the Training Methods for Designers of Flexible Production Cells in Factories of the Future
2020
This work proposes a design method for flexible manufacturing systems (FMS). The method reduces the learning curve by helping employees to solve problems related to the design and optimization of the layout, operation and control of FMS, avoiding the drawbacks of current tools. The approach uses Domain Specific Modeling Languages (DSML) for specification of FMS. The paper presents the definition of the DSML and the implementation of the graphical modeling and simulation tool bringing important contributions to development of the domain through the use of constructions from categories theory for DSML specifications. This mathematical basis allows the definition of constraints to avoid supple…
Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons
2016
The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) — often through guided feature reductions or parameter optimizations.
A class of third order iterative Kurchatov–Steffensen (derivative free) methods for solving nonlinear equations
2019
Abstract In this paper we show a strategy to devise third order iterative methods based on classic second order ones such as Steffensen’s and Kurchatov’s. These methods do not require the evaluation of derivatives, as opposed to Newton or other well known third order methods such as Halley or Chebyshev. Some theoretical results on convergence will be stated, and illustrated through examples. These methods are useful when the functions are not regular or the evaluation of their derivatives is costly. Furthermore, special features as stability, laterality (asymmetry) and other properties can be addressed by choosing adequate nodes in the design of the methods.
Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review
2020
Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published…
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations
2020
[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element me…
End-to-end congestion control protocols for remote programming of robots, using heterogeneous networks: A comparative analysis
2008
There are many interesting aspects of Internet Telerobotics within the network robotics context, such as variable bandwidth and time-delays. Some of these aspects have been treated in the literature from the control point of view. Moreover, only a little work is related to the way Internet protocols can help to minimize the effect of delay and bandwidth fluctuation on network robotics. In this paper, we present the capabilities of TCP, UDP, TCP Las Vegas, TEAR, and Trinomial protocols, when performing a remote experiment within a network robotics application, the UJI Industrial Telelaboratory. Comparative analysis is presented through simulations within the NS2 platform. Results show how th…