21,920 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Forecasting of commercial sales with large scale Gaussian Processes
This paper argues that there has not been enough discussion in the field of
applications of Gaussian Process for the fast moving consumer goods industry.
Yet, this technique can be important as it e.g., can provide automatic feature
relevance determination and the posterior mean can unlock insights on the data.
Significant challenges are the large size and high dimensionality of commercial
data at a point of sale. The study reviews approaches in the Gaussian Processes
modeling for large data sets, evaluates their performance on commercial sales
and shows value of this type of models as a decision-making tool for
management.Comment: 1o pages, 5 figure
Polyhedral Predictive Regions For Power System Applications
Despite substantial improvement in the development of forecasting approaches,
conditional and dynamic uncertainty estimates ought to be accommodated in
decision-making in power system operation and market, in order to yield either
cost-optimal decisions in expectation, or decision with probabilistic
guarantees. The representation of uncertainty serves as an interface between
forecasting and decision-making problems, with different approaches handling
various objects and their parameterization as input. Following substantial
developments based on scenario-based stochastic methods, robust and
chance-constrained optimization approaches have gained increasing attention.
These often rely on polyhedra as a representation of the convex envelope of
uncertainty. In the work, we aim to bridge the gap between the probabilistic
forecasting literature and such optimization approaches by generating forecasts
in the form of polyhedra with probabilistic guarantees. For that, we see
polyhedra as parameterized objects under alternative definitions (under
and norms), the parameters of which may be modelled and predicted.
We additionally discuss assessing the predictive skill of such multivariate
probabilistic forecasts. An application and related empirical investigation
results allow us to verify probabilistic calibration and predictive skills of
our polyhedra.Comment: 8 page
Dynamical inference from a kinematic snapshot: The force law in the Solar System
If a dynamical system is long-lived and non-resonant (that is, if there is a
set of tracers that have evolved independently through many orbital times), and
if the system is observed at any non-special time, it is possible to infer the
dynamical properties of the system (such as the gravitational force or
acceleration law) from a snapshot of the positions and velocities of the tracer
population at a single moment in time. In this paper we describe a general
inference technique that solves this problem while allowing (1) the unknown
distribution function of the tracer population to be simultaneously inferred
and marginalized over, and (2) prior information about the gravitational field
and distribution function to be taken into account. As an example, we consider
the simplest problem of this kind: We infer the force law in the Solar System
using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for
the eight major planets. We consider purely radial acceleration laws of the
form a_r = -A [r/r_0]^{-\alpha}, where r is the distance from the Sun. Using a
probabilistic inference technique, we infer 1.989 < \alpha < 2.052 (95 percent
interval), largely independent of any assumptions about the distribution of
energies and eccentricities in the system beyond the assumption that the system
is phase-mixed. Generalizations of the methods used here will permit, among
other things, inference of Milky Way dynamics from Gaia-like observations
Addressing Uncertainty in TMDLS: Short Course at Arkansas Water Resources Center 2001 Annual Conference
Management of a critical natural resource like water requires information on the status of that resource. The US Environmental Protection Agency (EPA) reported in the 1998 National Water Quality Inventory that more than 291,000 miles of assessed rivers and streams and 5 million acres of lakes do not meet State water quality standards. This inventory represents a compilation of State assessments of 840,000 miles of rivers and 17.4 million acres of lakes; a 22 percent increase in river miles and 4 percent increase in lake acres over their 1996 reports. Siltation, bacteria, nutrients and metals were the leading pollutants of impaired waters, according to EPA. The sources of these pollutants were presumed to be runoff from agricultural lands and urban areas. EPA suggests that the majority of Americans-over 218 million-live within ten miles of a polluted waterbody. This seems to contradict the recent proclamations of the success of the Clean Water Act, the Nation\u27s water pollution control law. EPA also claims that, while water quality is still threatened in the US, the amount of water safe for fishing and swimming has doubled since 1972, and that the number of people served by sewage treatment plants has more than doubled
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