5,305 research outputs found
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Urbanism is no longer planned on paper thanks to powerful models and 3D
simulation platforms. However, current work is not open to the public and lacks
an optimisation agent that could help in decision making. This paper describes
the creation of an open-source simulation based on an existing Dutch
liveability score with a built-in AI module. Features are selected using
feature engineering and Random Forests. Then, a modified scoring function is
built based on the former liveability classes. The score is predicted using
Random Forest for regression and achieved a recall of 0.83 with 10-fold
cross-validation. Afterwards, Exploratory Factor Analysis is applied to select
the actions present in the model. The resulting indicators are divided into 5
groups, and 12 actions are generated. The performance of four optimisation
algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three
established criteria of quality: cardinality, the spread of the solutions,
spacing, and the resulting score and number of turns. Although all four
algorithms show different strengths, eps-MOEA is selected to be the most
suitable for this problem. Ultimately, the simulation incorporates the model
and the selected AI module in a GUI written in the Kivy framework for Python.
Tests performed on users show positive responses and encourage further
initiatives towards joining technology and public applications.Comment: 16 page
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications
Business optimization is becoming increasingly important because all business
activities aim to maximize the profit and performance of products and services,
under limited resources and appropriate constraints. Recent developments in
support vector machine and metaheuristics show many advantages of these
techniques. In particular, particle swarm optimization is now widely used in
solving tough optimization problems. In this paper, we use a combination of a
recently developed Accelerated PSO and a nonlinear support vector machine to
form a framework for solving business optimization problems. We first apply the
proposed APSO-SVM to production optimization, and then use it for income
prediction and project scheduling. We also carry out some parametric studies
and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page
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
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
Machine Learning tools for global PDF fits
The use of machine learning algorithms in theoretical and experimental
high-energy physics has experienced an impressive progress in recent years,
with applications from trigger selection to jet substructure classification and
detector simulation among many others. In this contribution, we review the
machine learning tools used in the NNPDF family of global QCD analyses. These
include multi-layer feed-forward neural networks for the model-independent
parametrisation of parton distributions and fragmentation functions, genetic
and covariance matrix adaptation algorithms for training and optimisation, and
closure testing for the systematic validation of the fitting methodology.Comment: 12 pages, 9 figures, to appear in the proceedings of the XXIIIth
Quark Confinement and the Hadron Spectrum conference, 1-6 August 2018,
University of Maynooth, Irelan
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
A multi-objective genetic algorithm for the design of pressure swing adsorption
Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA
processes would be beneficial for the development of the technology, but their development is
a difficult task due to the complexity of the simulation of PSA cycles and the computational
effort needed to detect the performance at cyclic steady state.
We present a preliminary investigation of the performance of a custom multi-objective genetic
algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of
air for N2 production. The simulation requires a detailed diffusion model, which involves coupled
nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA
to handle this complex problem has been assessed by comparison with direct search methods.
An analysis of the effect of MOGA parameters on the performance is also presented
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