Foveation Control of a Robotic Eye Using Deep Reinforcement Learning

2018 ◽  
Author(s):  
Sunil Kumar Rajendran ◽  
Qi Wei ◽  
Feitian Zhang
Keyword(s):  
Reinforcement Learning ◽  
Control Design ◽  
Machine Learning Techniques ◽  
Artificial Muscles ◽  
Ocular Motility ◽  
One Dimensional ◽  
Learning Techniques ◽  
The Us ◽  
And Control ◽  
First Time

Deficit of the extraocular muscle is known as a key cause of ocular motility disorders that affect eye movement and complicate daily activities of millions of people in the US. A physical model mimicking the biomechanics of the oculomotor plant can improve the understanding of functionality and control of extraocular muscles and provide a tool for researchers to gain insights into binocular misalignment. This paper will present, for the first time, the design and development of a robotic eye system driven by antagonistic super coiled polymer (SCP) based artificial muscles and the motion control design by leveraging machine learning techniques. The dynamic model of the robotic eye will be presented. Deep reinforcement learning is used for control design of the robotic eye system, demonstrated by simulation of one-dimensional foveation control.

scholarly journals SoK: Privacy-Preserving Collaborative Tree-based Model Learning

2021 ◽  
Vol 2021 (3) ◽  
pp. 182-203
Author(s):  
Sylvain Chatel ◽  
Apostolos Pyrgelis ◽  
Juan Ramón Troncoso-Pastoriza ◽  
Jean-Pierre Hubaux
Keyword(s):  
Learning Algorithm ◽  
Information Leakage ◽  
Privacy Preserving ◽  
Machine Learning Techniques ◽  
Model Learning ◽  
Protection Mechanism ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Efficient Machine ◽  
First Time

Abstract Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

scholarly journals Automation and control of laser wakefield accelerators using Bayesian optimization

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
R. J. Shalloo ◽  
S. J. D. Dann ◽  
J.-N. Gruse ◽  
C. I. D. Underwood ◽  
A. F. Antoine ◽  
...  
Keyword(s):  
Pulse Length ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Laser Wakefield ◽  
Accelerating Structure ◽  
Automation And Control ◽  
Laser Pulse Shape ◽  
And Control ◽  
Wakefield Accelerators

AbstractLaser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

Controlling a Simulated Robot Using Machine Learning Techniques

2010 ◽  
Author(s):  
Jonathan Becker ◽  
Aveek Purohit ◽  
Zheng Sun
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Linear Regression ◽  
Pid Controller ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Gaming Environment ◽  
Using Data

USARSim group at NIST developed a simulated robot that operated in the Unreal Tournament 3 (UT3) gaming environment. They used a software PID controller to control the robot in UT3 worlds. Unfortunately, the PID controller did not work well, so NIST asked us to develop a better controller using machine learning techniques. In the process, we characterized the software PID controller and the robot’s behavior in UT3 worlds. Using data collected from our simulations, we compared different machine learning techniques including linear regression and reinforcement learning (RL). Finally, we implemented a RL based controller in Matlab and ran it in the UT3 environment via a TCP/IP link between Matlab and UT3.

scholarly journals Delving into Android Malware Families with a Novel Neural Projection Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rafael Vega Vega ◽  
Héctor Quintián ◽  
Carlos Cambra ◽  
Nuño Basurto ◽  
Álvaro Herrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Projection Method ◽  
Hebbian Learning ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Learning Techniques ◽  
First Time

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT

2003 ◽  
Vol 06 (03) ◽  
pp. 405-426 ◽  
Author(s):  
PAUL DARBYSHIRE
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Abilities ◽  
Complex Adaptive System ◽  
Machine Learning Techniques ◽  
Simulation Techniques ◽  
Learning Rates ◽  
Learning Techniques ◽  
Complex Adaptive ◽  
Rate Of Learning ◽  
Multi Agent

Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of learning is not sufficient for agents to adapt to this hostile environment. However, by allowing the agents to communicate their respective rewards for actions performed as the simulation progresses, the rate of learning can be increased sufficiently to significantly increase the teams chances of survival. This paper presents the results of trials to measure the success of a team-based approach to the reinforcement-learning problem in a distillation, using reward communication to increase learning rates.

scholarly journals Q-Learning based Routing Protocol to Enhance Network Lifetime in WSNs

2021 ◽  
Vol 13 (2) ◽  
pp. 57-80
Author(s):  
Arunita Kundaliya ◽  
D.K. Lobiyal
Keyword(s):  
Reinforcement Learning ◽  
Network Lifetime ◽  
Residual Energy ◽  
Efficient Solutions ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Learning Techniques ◽  
Aodv Protocol ◽  
Optimal Action ◽  
Additional Memory

In resource constraint Wireless Sensor Networks (WSNs), enhancement of network lifetime has been one of the significantly challenging issues for the researchers. Researchers have been exploiting machine learning techniques, in particular reinforcement learning, to achieve efficient solutions in the domain of WSN. The objective of this paper is to apply Q-learning, a reinforcement learning technique, to enhance the lifetime of the network, by developing distributed routing protocols. Q-learning is an attractive choice for routing due to its low computational requirements and additional memory demands. To facilitate an agent running at each node to take an optimal action, the approach considers node’s residual energy, hop length to sink and transmission power. The parameters, residual energy and hop length, are used to calculate the Q-value, which in turn is used to decide the optimal next-hop for routing. The proposed protocols’ performance is evaluated through NS3 simulations, and compared with AODV protocol in terms of network lifetime, throughput and end-to-end delay.

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