14,715 research outputs found
Information driven self-organization of complex robotic behaviors
Information theory is a powerful tool to express principles to drive
autonomous systems because it is domain invariant and allows for an intuitive
interpretation. This paper studies the use of the predictive information (PI),
also called excess entropy or effective measure complexity, of the sensorimotor
process as a driving force to generate behavior. We study nonlinear and
nonstationary systems and introduce the time-local predicting information
(TiPI) which allows us to derive exact results together with explicit update
rules for the parameters of the controller in the dynamical systems framework.
In this way the information principle, formulated at the level of behavior, is
translated to the dynamics of the synapses. We underpin our results with a
number of case studies with high-dimensional robotic systems. We show the
spontaneous cooperativity in a complex physical system with decentralized
control. Moreover, a jointly controlled humanoid robot develops a high
behavioral variety depending on its physics and the environment it is
dynamically embedded into. The behavior can be decomposed into a succession of
low-dimensional modes that increasingly explore the behavior space. This is a
promising way to avoid the curse of dimensionality which hinders learning
systems to scale well.Comment: 29 pages, 12 figure
BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)
We describe a binding schema markup language (BSML) for describing data
interchange between scientific codes. Such a facility is an important
constituent of scientific problem solving environments (PSEs). BSML is designed
to integrate with a PSE or application composition system that views model
specification and execution as a problem of managing semistructured data. The
data interchange problem is addressed by three techniques for processing
semistructured data: validation, binding, and conversion. We present BSML and
describe its application to a PSE for wireless communications system design
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising
technology for massive Machine Type Communication (mMTC). Given that its
deployment is rapidly progressing worldwide, measurement campaigns and
performance analyses are needed to better understand the system and move toward
its enhancement. With this aim, this paper presents a large scale measurement
campaign and empirical analysis of NB-IoT on operational networks, and
discloses valuable insights in terms of deployment strategies and radio
coverage performance. The reported results also serve as examples showing the
potential usage of the collected dataset, which we make open-source along with
a lightweight data visualization platform.Comment: Accepted for publication in IEEE Communications Magazine (Internet of
Things and Sensor Networks Series
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Scaling Analysis of Affinity Propagation
We analyze and exploit some scaling properties of the Affinity Propagation
(AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe
that a divide and conquer strategy, used on a large data set hierarchically
reduces the complexity to , for a
data-set of size and a depth of the hierarchical strategy. For a
data-set embedded in a -dimensional space, we show that this is obtained
without notably damaging the precision except in dimension . In fact, for
larger than 2 the relative loss in precision scales like
. Finally, under some conditions we observe that there is a
value of the penalty coefficient, a free parameter used to fix the number
of clusters, which separates a fragmentation phase (for ) from a
coalescent one (for ) of the underlying hidden cluster structure. At
this precise point holds a self-similarity property which can be exploited by
the hierarchical strategy to actually locate its position. From this
observation, a strategy based on \AP can be defined to find out how many
clusters are present in a given dataset.Comment: 28 pages, 14 figures, Inria research repor
Importance Sampling in Rigid Body Diffusion Monte Carlo
We present an algorithm for rigid body diffusion Monte Carlo with importance
sampling, which is based on a rigorous short-time expansion of the Green's
function for rotational motion in three dimensions. We show that this
short-time approximation provides correct sampling of the angular degrees of
freedom, and provides a general way to incorporate importance sampling for all
degrees of freedom. The full importance sampling algorithm significantly
improves both calculational efficiency and accuracy of ground state properties,
and allows rotational and bending excitations in molecular van der Waals
clusters to be studied directly.Comment: Accepted for publication in Computer Physics Communication
- …