1,934 research outputs found
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Protection of Future Electricity Systems
The electrical energy industry is undergoing dramatic changes: massive deployment of renewables, increasing share of DC networks at transmission and distribution levels, and at the same time, a continuing reduction in conventional synchronous generation, all contribute to a situation where a variety of technical and economic challenges emerge. As the society’s reliance on electrical power continues to increase as a result of international decarbonisation commitments, the need for secure and uninterrupted delivery of electrical energy to all customers has never been greater. Power system protection plays an important enabling role in future decarbonized energy systems. This book includes ten papers covering a wide range of topics related to protection system problems and solutions, such as adaptive protection, protection of HVDC and LVDC systems, unconventional or enhanced protection methods, protection of superconducting transmission cables, and high voltage lightning protection. This volume has been edited by Adam Dyśko, Senior Lecturer at the University of Strathclyde, UK, and Dimitrios Tzelepis, Research Fellow at the University of Strathclyde
Exploring unknown environments with multi-modal locomotion swarm
International audienceSwarm robotics is focused on creating intelligent systems from large number of simple robots. The majority of nowadays robots are bound to operations within mono-modal locomotion (i.e. land, air or water). However, some animals have the capacity to alter their locomotion modalities to suit various terrains, operating at high levels of competence in a range of substrates. One of the most significant challenges in bio-inspired robotics is to determine how to use multi-modal locomotion to help robots perform a variety of tasks. In this paper, we investigate the use of multi-modal locomotion on a swarm of robots through a multi-target search algorithm inspired from the behavior of flying ants. Features of swarm intelligence such as distributivity, robustness and scalability are ensured by the proposed algorithm. Although the simplicity of movement policies of each agent, complex and efficient exploration is achieved at the team level
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Fault tolerant and dynamic evolutionary optimization engines
Mimicking natural evolution to solve hard optimization problems has played an important
role in the artificial intelligence arena. Such techniques are broadly classified
as Evolutionary Algorithms (EAs) and have been investigated for around four decades
during which important contributions and advances have been made.
One main evolutionary technique which has been widely investigated is the Genetic
Algorithm (GA). GAs are stochastic search techniques that follow the Darwinian
principle of evolution. Their application in the solution of hard optimization problems
has been very successful. Indeed multi-dimensional problems presenting difficult search
spaces with characteristics such as multi-modality, epistasis, non regularity, deceptiveness,
etc., have all been effectively tackled by GAs.
In this research, a competitive form of GAs known as fine or cellular GAs (cGAs)
are investigated, because of their suitability for System on Chip (SoC) implementation
when tackling real-time problems. Cellular GAs have also attracted the attention
of researchers due to their high performance, ease of implementation and massive
parallelism. In addition, cGAs inherently possess a number of structural configuration
parameters which make them capable of sustaining diversity during evolution and
therefore of promoting an adequate balance between exploitative and explorative stages
of the search.
The fast technological development of Integrated Circuits (ICs) has allowed a considerable
increase in compactness and therefore in density. As a result, it is nowadays
possible to have millions of gates and transistor based circuits in very small silicon
areas. Operational complexity has also significantly increased and consequently other
setbacks have emerged, such as the presence of faults that commonly appear in the
form of single or multiple bit flips. Tough environmental or time dependent operating
conditions can trigger faults in registers and memory allocations due to induced radiation, electron migration and dielectric breakdown. These kinds of faults are known as
Single Event Effects (SEEs).
Research has shown that an effective way of dealing with SEEs consists of a combination
of hardware and software mitigation techniques to overcome faulty scenarios.
Permanent faults known as Single Hard Errors (SHEs) and temporary faults known
as Single Event Upsets (SEUs) are common SEEs. This thesis aims to investigate the
inherent abilities of cellular GAs to deal with SHEs and SEUs at algorithmic level. A
hard real-time application is targeted: calculating the attitude parameters for navigation
in vehicles using Global Positioning System (GPS) technology. Faulty critical
data, which can cause a system’s functionality to fail, are evaluated. The proposed
mitigation techniques show cGAs ability to deal with up to 40% stuck at zero and 30%
stuck at one faults in chromosomes bits and fitness score cells.
Due to the non-deterministic nature of GAs, dynamic on-the-fly algorithmic and
parametric configuration has also attracted the attention of researchers. In this respect,
the structural properties of cellular GAs provide a valuable attribute to influence their
selection pressure. This helps to maintain an adequate exploitation-exploration tradeoff,
either from a pure topological perspective or through genetic operations that also
make use of structural characteristics in cGAs. These properties, unique to cGAs, are
further investigated in this thesis through a set of middle to high difficulty benchmark
problems. Experimental results show that the proposed dynamic techniques enhance
the overall performance of cGAs in most benchmark problems.
Finally, being structurally attached, the dimensionality of cellular GAs is another
line of investigation. 1D and 2D structures have normally been used to test cGAs at
algorithm and implementation levels. Although 3D-cGAs are an immediate extension,
not enough attention has been paid to them, and so a comparative study on the dimensionality
of cGAs is carried out. Having shorter radii, 3D-cGAs present a faster
dissemination of solutions and have denser neighbourhoods. Empirical results reported
in this thesis show that 3D-cGAs achieve better efficiency when solving multi-modal
and epistatic problems. In future, the performance improvements of 3D-cGAs will
merge with the latest benefits that 3D integration technology has demonstrated, such
as reductions in routing length, in interconnection delays and in power consumption
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Distribution System Disturbance Analysis and Outage Management Using Hybrid Data-Driven and Physics-Based Approaches
Securing cyber-power distribution systems (DS) against malicious events is critical with the integration of distributed energy resources (DERs), supporting automation and increasing vulnerabilities. Situational awareness utilizing power data (e.g., data from distribution phasor measurement units (D-PMUs)) and cyber data (e.g., network packets data) is the main focus of this dissertation by means of which, an opportunity for real-time monitoring and decision-making is provided. To further improve the DS’s reliable and resilient operation, in this Ph.D. work, the aim has been put towards the development of an automated tool consisting of multiple modules to precisely investigate any type of data anomalies, followed by root cause finding. For this purpose, a data aggregation scheme is developed to synchronize the resolution and time stamp of multiple metering sources throughout the DS, using an enhancement of the conventional Kalman Filter, named Ensemble Extended Kalman Filter (EEKF). EEKF is implemented as an automated module by exploiting the real-time measurements as well as deriving the system physics. Furthermore, this dissertation develops online cyber-physical event detection and classification as well as proposal of the novel Outage Root Cause Analysis (ORCA) system. Different sections of the work have been tested on IEEE and OPAL-RT test systems as well as real-filed measurements from installed actual hardwares
Doctor of Philosophy
dissertationImage segmentation entails the partitioning of an image domain, usually two or three dimensions, so that each partition or segment has some meaning that is relevant to the application at hand. Accurate image segmentation is a crucial challenge in many disciplines, including medicine, computer vision, and geology. In some applications, heterogeneous pixel intensities; noisy, ill-defined, or diffusive boundaries; and irregular shapes with high variability can make it challenging to meet accuracy requirements. Various segmentation approaches tackle such challenges by casting the segmentation problem as an energy-minimization problem, and solving it using efficient optimization algorithms. These approaches are broadly classified as either region-based or edge (surface)-based depending on the features on which they operate. The focus of this dissertation is on the development of a surface-based energy model, the design of efficient formulations of optimization frameworks to incorporate such energy, and the solution of the energy-minimization problem using graph cuts. This dissertation utilizes a set of four papers whose motivation is the efficient extraction of the left atrium wall from the late gadolinium enhancement magnetic resonance imaging (LGE-MRI) image volume. This dissertation utilizes these energy formulations for other applications, including contact lens segmentation in the optical coherence tomography (OCT) data and the extraction of geologic features in seismic data. Chapters 2 through 5 (papers 1 through 4) explore building a surface-based image segmentation model by progressively adding components to improve its accuracy and robustness. The first paper defines a parametric search space and its discrete formulation in the form of a multilayer three-dimensional mesh model within which the segmentation takes place. It includes a generative intensity model, and we optimize using a graph formulation of the surface net problem. The second paper proposes a Bayesian framework with a Markov random field (MRF) prior that gives rise to another class of surface nets, which provides better segmentation with smooth boundaries. The third paper presents a maximum a posteriori (MAP)-based surface estimation framework that relies on a generative image model by incorporating global shape priors, in addition to the MRF, within the Bayesian formulation. Thus, the resulting surface not only depends on the learned model of shapes,but also accommodates the test data irregularities through smooth deviations from these priors. Further, the paper proposes a new shape parameter estimation scheme, in closed form, for segmentation as a part of the optimization process. Finally, the fourth paper (under review at the time of this document) presents an extensive analysis of the MAP framework and presents improved mesh generation and generative intensity models. It also performs a thorough analysis of the segmentation results that demonstrates the effectiveness of the proposed method qualitatively, quantitatively, and clinically. Chapter 6, consisting of unpublished work, demonstrates the application of an MRF-based Bayesian framework to segment coupled surfaces of contact lenses in optical coherence tomography images. This chapter also shows an application related to the extraction of geological structures in seismic volumes. Due to the large sizes of seismic volume datasets, we also present fast, approximate surface-based energy minimization strategies that achieve better speed-ups and memory consumption
Computational intelligence techniques for HVAC systems: a review
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions
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