196,942 research outputs found
A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions
It is expected, and regionally observed, that energy demand will soon be
covered by a widespread deployment of renewable energy sources. However, the
weather and climate driven energy sources are characterized by a significant
spatial and temporal variability. One of the commonly mentioned solutions to
overcome the mismatch between demand and supply provided by renewable
generation is a hybridization of two or more energy sources in a single power
station (like wind-solar, solar-hydro or solar-wind-hydro). The operation of
hybrid energy sources is based on the complementary nature of renewable
sources. Considering the growing importance of such systems and increasing
number of research activities in this area this paper presents a comprehensive
review of studies which investigated, analyzed, quantified and utilized the
effect of temporal, spatial and spatio-temporal complementarity between
renewable energy sources. The review starts with a brief overview of available
research papers, formulates detailed definition of major concepts, summarizes
current research directions and ends with prospective future research
activities. The review provides a chronological and spatial information with
regard to the studies on the complementarity concept.Comment: 34 pages 7 figures 3 table
Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations
The development of molecular signatures for the prediction of time-to-event
outcomes is a methodologically challenging task in bioinformatics and
biostatistics. Although there are numerous approaches for the derivation of
marker combinations and their evaluation, the underlying methodology often
suffers from the problem that different optimization criteria are mixed during
the feature selection, estimation and evaluation steps. This might result in
marker combinations that are only suboptimal regarding the evaluation criterion
of interest. To address this issue, we propose a unified framework to derive
and evaluate biomarker combinations. Our approach is based on the concordance
index for time-to-event data, which is a non-parametric measure to quantify the
discrimatory power of a prediction rule. Specifically, we propose a
component-wise boosting algorithm that results in linear biomarker combinations
that are optimal with respect to a smoothed version of the concordance index.
We investigate the performance of our algorithm in a large-scale simulation
study and in two molecular data sets for the prediction of survival in breast
cancer patients. Our numerical results show that the new approach is not only
methodologically sound but can also lead to a higher discriminatory power than
traditional approaches for the derivation of gene signatures.Comment: revised manuscript - added simulation study, additional result
Sleep Mode Analysis via Workload Decomposition
The goal of this paper is to establish a general approach for analyzing
queueing models with repeated inhomogeneous vacations. The server goes on for a
vacation if the inactivity prolongs more than the vacation trigger duration.
Once the system enters in vacation mode, it may continue for several
consecutive vacations. At the end of a vacation, the server goes on another
vacation, possibly with a different probability distribution; if during the
previous vacation there have been no arrivals. However the system enters in
vacation mode only if the inactivity is persisted beyond defined trigger
duration. In order to get an insight on the influence of parameters on the
performance, we choose to study a simple M/G/1 queue (Poisson arrivals and
general independent service times) which has the advantage of being tractable
analytically. The theoretical model is applied to the problem of power saving
for mobile devices in which the sleep durations of a device correspond to the
vacations of the server. Various system performance metrics such as the frame
response time and the economy of energy are derived. A constrained optimization
problem is formulated to maximize the economy of energy achieved in power save
mode, with constraints as QoS conditions to be met. An illustration of the
proposed methods is shown with a WiMAX system scenario to obtain design
parameters for better performance. Our analysis allows us not only to optimize
the system parameters for a given traffic intensity but also to propose
parameters that provide the best performance under worst case conditions
Optimization of Discrete-parameter Multiprocessor Systems using a Novel Ergodic Interpolation Technique
Modern multi-core systems have a large number of design parameters, most of
which are discrete-valued, and this number is likely to keep increasing as chip
complexity rises. Further, the accurate evaluation of a potential design choice
is computationally expensive because it requires detailed cycle-accurate system
simulation. If the discrete parameter space can be embedded into a larger
continuous parameter space, then continuous space techniques can, in principle,
be applied to the system optimization problem. Such continuous space techniques
often scale well with the number of parameters.
We propose a novel technique for embedding the discrete parameter space into
an extended continuous space so that continuous space techniques can be applied
to the embedded problem using cycle accurate simulation for evaluating the
objective function. This embedding is implemented using simulation-based
ergodic interpolation, which, unlike spatial interpolation, produces the
interpolated value within a single simulation run irrespective of the number of
parameters. We have implemented this interpolation scheme in a cycle-based
system simulator. In a characterization study, we observe that the interpolated
performance curves are continuous, piece-wise smooth, and have low statistical
error. We use the ergodic interpolation-based approach to solve a large
multi-core design optimization problem with 31 design parameters. Our results
indicate that continuous space optimization using ergodic interpolation-based
embedding can be a viable approach for large multi-core design optimization
problems.Comment: A short version of this paper will be published in the proceedings of
IEEE MASCOTS 2015 conferenc
A Learning Theoretic Approach to Energy Harvesting Communication System Optimization
A point-to-point wireless communication system in which the transmitter is
equipped with an energy harvesting device and a rechargeable battery, is
studied. Both the energy and the data arrivals at the transmitter are modeled
as Markov processes. Delay-limited communication is considered assuming that
the underlying channel is block fading with memory, and the instantaneous
channel state information is available at both the transmitter and the
receiver. The expected total transmitted data during the transmitter's
activation time is maximized under three different sets of assumptions
regarding the information available at the transmitter about the underlying
stochastic processes. A learning theoretic approach is introduced, which does
not assume any a priori information on the Markov processes governing the
communication system. In addition, online and offline optimization problems are
studied for the same setting. Full statistical knowledge and causal information
on the realizations of the underlying stochastic processes are assumed in the
online optimization problem, while the offline optimization problem assumes
non-causal knowledge of the realizations in advance. Comparing the optimal
solutions in all three frameworks, the performance loss due to the lack of the
transmitter's information regarding the behaviors of the underlying Markov
processes is quantified
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