27,612 research outputs found
A method for Bayesian regression modelling of composition data
Many scientific and industrial processes produce data that is best analysed
as vectors of relative values, often called compositions or proportions. The
Dirichlet distribution is a natural distribution to use for composition or
proportion data. It has the advantage of a low number of parameters, making it
the parsimonious choice in many cases. In this paper we consider the case where
the outcome of a process is Dirichlet, dependent on one or more explanatory
variables in a regression setting. We explore some existing approaches to this
problem, and then introduce a new simulation approach to fitting such models,
based on the Bayesian framework. We illustrate the advantages of the new
approach through simulated examples and an application in sport science. These
advantages include: increased accuracy of fit, increased power for inference,
and the ability to introduce random effects without additional complexity in
the analysis.Comment: 10 pages, 1 figure, 2 table
Evaluation of advanced optimisation methods for estimating Mixed Logit models
The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi-Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi-Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used
State of the Art in the Optimisation of Wind Turbine Performance Using CFD
Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
A Petri Nets-based Scheduling Methodology forMultipurpose Batch Plants.
This article presents an optimization methodology of batch production processes assembled by shared resources which rely on a mapping of state-events into time-events allowing in this way the straightforward use of a well consolidated scheduling policies developed for manufacturing systems. A technique to generate the timed Petri net representation from a continuous dynamic representation (Differential-Algebraic Equations systems (DAEs)) of the production system is presented together with the main characteristics of a Petri nets-based tool implemented for optimization purposes. This paper describes also how the implemented tool generates the coverability tree and how it can be pruned by a general purpose heuristic. An example of a distillation process with two shared batch resources is used to illustrate the optimization methodology proposed
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
Efficient Quantum Algorithms for State Measurement and Linear Algebra Applications
We present an algorithm for measurement of -local operators in a quantum
state, which scales logarithmically both in the system size and the output
accuracy. The key ingredients of the algorithm are a digital representation of
the quantum state, and a decomposition of the measurement operator in a basis
of operators with known discrete spectra. We then show how this algorithm can
be combined with (a) Hamiltonian evolution to make quantum simulations
efficient, (b) the Newton-Raphson method based solution of matrix inverse to
efficiently solve linear simultaneous equations, and (c) Chebyshev expansion of
matrix exponentials to efficiently evaluate thermal expectation values. The
general strategy may be useful in solving many other linear algebra problems
efficiently.Comment: 17 pages, 3 figures (v2) Sections reorganised, several clarifications
added, results unchange
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Mitigating ground effect on mini quadcopters with model reference adaptive control
Mitigating ground effect becomes a big challenge for autonomous aerial vehicles when they are flying in close proximity to the ground. This paper aims to develop a precise model of ground effect on mini quadcopters, provide an advanced control algorithm to counter the model uncertainty and, as a result, improves the command tracking performance when the vehicle is in the ground effect region. The mathematical model of ground effect has been established through a series of experiments and validated by a flight test. The experiments show that the total thrust generated by rotors increases linearly as the vehicle gets closer to the ground, which is different from the commonly-used ground effect model for a single rotor vehicle. In addition, the model switches from a piecewise linear to a quadratic function when the rotor to rotor distance is increased. A control architecture that utilizes the model reference adaptive controller (MRAC) has also been designed, where MRAC is added to the altitude loop. The performance of the proposed control algorithm has been evaluated through a set of flight tests on a mini quadcopter platform and compared with a traditional proportional–integral–derivative (PID) controller. The results demonstrate that MRAC dramatically improves the tracking performance of altitude command and can reduce the rise time by 80 % under the ground effect
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