577,018 research outputs found
Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation
This paper presents adaptive link selection algorithms for distributed
estimation and considers their application to wireless sensor networks and
smart grids. In particular, exhaustive search--based
least--mean--squares(LMS)/recursive least squares(RLS) link selection
algorithms and sparsity--inspired LMS/RLS link selection algorithms that can
exploit the topology of networks with poor--quality links are considered. The
proposed link selection algorithms are then analyzed in terms of their
stability, steady--state and tracking performance, and computational
complexity. In comparison with existing centralized or distributed estimation
strategies, key features of the proposed algorithms are: 1) more accurate
estimates and faster convergence speed can be obtained; and 2) the network is
equipped with the ability of link selection that can circumvent link failures
and improve the estimation performance. The performance of the proposed
algorithms for distributed estimation is illustrated via simulations in
applications of wireless sensor networks and smart grids.Comment: 14 figure
Shrewd Selection Speeds Surfing: Use Smart EXP3!
In this paper, we explore the use of multi-armed bandit online learning
techniques to solve distributed resource selection problems. As an example, we
focus on the problem of network selection. Mobile devices often have several
wireless networks at their disposal. While choosing the right network is vital
for good performance, a decentralized solution remains a challenge. The
impressive theoretical properties of multi-armed bandit algorithms, like EXP3,
suggest that it should work well for this type of problem. Yet, its real-word
performance lags far behind. The main reasons are the hidden cost of switching
networks and its slow rate of convergence. We propose Smart EXP3, a novel
bandit-style algorithm that (a) retains the good theoretical properties of
EXP3, (b) bounds the number of switches, and (c) yields significantly better
performance in practice. We evaluate Smart EXP3 using simulations, controlled
experiments, and real-world experiments. Results show that it stabilizes at the
optimal state, achieves fairness among devices and gracefully deals with
transient behaviors. In real world experiments, it can achieve 18% faster
download over alternate strategies. We conclude that multi-armed bandit
algorithms can play an important role in distributed resource selection
problems, when practical concerns, such as switching costs and convergence
time, are addressed.Comment: Full pape
Genetic algorithms applied to the scheduling of the Hubble Space Telescope
A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better
Smart Meter Devices and The Effect of Feedback on Residential Electricity Consumption: Evidence from a Natural Experiment in Northern Ireland
Using a unique set of data and exploiting a large-scale natural experiment, we estimate the effect of real-time usage information on residential electricity consumption in Northern Ireland. Starting in April 2002, the utility replaced prepayment meters with “smart” meters that allow the consumer to track usage in real-time. We rely on this event, account for the endogeneity of price and plan with consumption through a plan selection correction term, and find that the provision of information is associated with a decline in electricity consumption of up to 20%. We find that the reduction is robust to different specifications, selection-bias correction methods and subsamples of the original data. At £15-17 per tonne of CO2e (2009£), the smart meter program delivers cost-effective reductions in carbon dioxide emissions.Residential Energy, Electricity Demand, Feedback, Smart Meter, Information
The role of intelligent systems in delivering the smart grid
The development of "smart" or "intelligent" energy networks has been proposed by both EPRI's IntelliGrid initiative and the European SmartGrids Technology Platform as a key step in meeting our future energy needs. A central challenge in delivering the energy networks of the future is the judicious selection and development of an appropriate set of technologies and techniques which will form "a toolbox of proven technical solutions". This paper considers functionality required to deliver key parts of the Smart Grid vision of future energy networks. The role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. In addition to that functionality, the paper considers the role of intelligent systems, in particular multi-agent systems, in providing flexible and extensible architectures for deploying intelligence within the Smart Grid. Beyond exploiting intelligent systems as architectural elements of the Smart Grid, with the purpose of meeting a set of engineering requirements, the role of intelligent systems as a tool for understanding what those requirements are in the first instance, is also briefly discussed
Delay-Optimal Relay Selection in Device-to-Device Communications for Smart Grid
The smart grid communication network adopts a hierarchical structure which consists of three kinds of networks which are Home Area Networks (HANs), Neighborhood Area Networks (NANs), and Wide Area Networks (WANs). The smart grid NANs comprise of the communication infrastructure used to manage the electricity distribution to the end users. Cellular technology with LTE-based standards is a widely-used and forward-looking technology hence becomes a promising technology that can meet the requirements of different applications in NANs. However, the LTE has a limitation to cope with the data traffic characteristics of smart grid applications, thus require for enhancements. Device-to-Device (D2D) communications enable direct data transmissions between devices by exploiting the cellular resources, which could guarantee the improvement of LTE performances. Delay is one of the important communication requirements for the real-time smart grid applications. In this paper, the application of D2D communications for the smart grid NANs is investigated to improve the average end-to-end delay of the system. A relay selection algorithm that considers both the queue state and the channel state of nodes is proposed. The optimization problem is formulated as a constrained Markov decision process (CMDP) and a linear programming method is used to find the optimal policy for the CMDP problem. Simulation results are presented to prove the effectiveness of the proposed scheme
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
Smart premise selection is essential when using automated reasoning as a tool
for large-theory formal proof development. A good method for premise selection
in complex mathematical libraries is the application of machine learning to
large corpora of proofs. This work develops learning-based premise selection in
two ways. First, a newly available minimal dependency analysis of existing
high-level formal mathematical proofs is used to build a large knowledge base
of proof dependencies, providing precise data for ATP-based re-verification and
for training premise selection algorithms. Second, a new machine learning
algorithm for premise selection based on kernel methods is proposed and
implemented. To evaluate the impact of both techniques, a benchmark consisting
of 2078 large-theory mathematical problems is constructed,extending the older
MPTP Challenge benchmark. The combined effect of the techniques results in a
50% improvement on the benchmark over the Vampire/SInE state-of-the-art system
for automated reasoning in large theories.Comment: 26 page
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