81 research outputs found
Wayne Pullan interview, October 1, 2012
No full textMr. Pullan talks about his work with on the Central Utah Project for the Bureau of Reclamation and the CUCPA office, including BoR Contracts and Repayment Specialist, Planning and Water Resource Group, Program Manager with CUPCA, including Ute Tribal liaison, and Bureau of ReclamationAssistant Area Manager and Pilot Program Manager
Simple ingredients leading to very efficient heuristics for the maximum clique problem
No full textStarting from an algorithm recently proposed by Pullan and Hoos, we formulate and analyze iterated local search algorithms for the maximum clique problem. The basic components of such algorithms are a fast neighbourhood search (not based on node evaluation but on completely random selection) and simple, yet very effective, diversification techniques and restart rules. A detailed computational study is performed in order to identify strengths and weaknesses of the proposed algorithms and the role of the different components on several classes of instances. The tested algorithms are very fast and reliable: most of the DIMACS benchmark instances are solved within very short CPU times. For one of the hardest tests, a new putative optimum was discovered by one of our algorithms. Very good performances were also shown on recently proposed and more difficult instances. It is important to remark that the heuristics tested in this paper are basically parameter free (the appropriate value for the unique parameter is easily identified and was, in fact, the same value for all problem instances used in this paper).No Full Tex
Local search for the maximumk-plex problem
No full textThe maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121 dense graphs and 61 large real-life (sparse) graphs. Comparisons with three recent algorithms on the more difficult graphs show that KLS performed better or as well as in 93% of 332 significant k-plex problem instances investigated achieving either larger average k-plex sizes (including some new results) or, when these were equivalent, lower CPU requirements.Full Tex
Evaluation of Tailored Mutation Operator in a Parallel Genetic Algorithm for Pavement Maintenance Treatment Scheduling
No full textThe maintenance of an existing large road network is a key focus area for road authorities around the world. The pressures associated with the ever-increasing road network and often shrinking budgets means that it is essential that road authorities invest maintenance budgets wisely. In line with this objective, most road authorities’ employee a Pavement Management System (PMS) to assist in making maintenance decisions. PMSs must solve a very large optimization problem involving thousands of road segments with multiple possible treatments. There is a wide range in the cost of these treatments and also in the magnitude and duration of their improvement. The optimization problem is to identify a minimum cost, 20-year maintenance program that ensures all segments are maintained at an acceptable level (which varies depending on factors such as the amount of traffic and the type of traffic). In addition to the 20-year overall budget, there are yearly budgets constraints which must be met and many other constraints such as the availability of staff and machinery.
Previous research has shown significant benefit arises from the adoption of a genetic algorithm-based PMS. This paper builds on this research through the application and evaluation of a tailored, parallel genetic algorithm within a PMS. A tailored genetic algorithm is evaluated using a real-world road network of 1,335 road segments executed using 12 processing units with annual budgets ranging between 50 million. Over a total of 174 trials, the tailored genetic algorithm was 46% more successful than a standard genetic algorithm at producing an optimised program of works that satisfied all budget constraints, typically with a lower overspendFull Tex
Heuristic Based Optimisation of Pavement Management Scheduling
No full textThe issue of effectively scheduling pavement maintenance and rehabilitation treatments over a multi-year planning horizon plagues road authorities around the world with the significance of this issue being amplified by both an ageing pavement network and the trend towards insufficient fund allocation. The scope of the problem can be quantified as follows: if only a single treatment is able to be applied to each individual road segment in a single year, then the total number of possible programmed maintenance and rehabilitation schedule alternatives for a moderate-sized network of 1,000 road segments, with eight different treatments possible, over a twenty year anaysis period is ((1.0 × 103)8)20 = 1.0 × 10480. Assuming that a computer can build and evaluate 100 complete maintenance and rehabilitation schedules a second, to identify the optimal schedule for this 1,000 segment road network would take 3.17 × 10471 years. The overall goal of this study is to investigate the benefits of applying modern heuristic optimisation techniques to the problem of pavement main- tenance and rehabilitation scheduling over a multi-year planning horizon. To address this goal, a four stage approach was utilised using a real road network with real pavement condition data as the test benchmark.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)Griffith School of EngineeringScience, Environment, Engineering and TechnologyFull Tex
Non-Iterative Three-Dimensional Reconstruction and Representation
No full textThree-dimensional reconstruction algorithms generate 3D data from twodimensional
image or video data. The current focus of this research area is
on iterative algorithms such as: feature matching/RANSAC, Iterative Closest
Point, and other non-linear optimization strategies. These strategies tend
to fail in scenes with few features or scenes which contain feature confusion.
In 2D image registration research, feature matching is dominant but closed
form solution based Fourier registration techniques have been proven to outperform
them with increased robustness to under textured scenes and image
noise. This thesis investigates the application of Fourier Volume Registration
to 3D reconstruction. Results are compared between Fourier Volume
Registration, and several current techniques both quantitatively and qualitatively
to nd out if Fourier based techniques outperform iterative ones.
Results show that the Fourier Volume Registration Technique often outperforms
other methods in terms of minimizing registration error prior to
optimization. Furthermore it is a closed form solution which works well with parallel processing architectures. 3D data representations for 3D reconstruction
data are also explored to improve storage and transmission of such data.
Many current methods make use of Signed Distance Functions, volumetric
occupancy grids or octrees. In the work presented here, lossy octree compression
is analysed to pave the way for new storage and transmission rates
of e ciency. A novel method, called the Plane-Tree, is proposed based on
the octree compression method. This Plane-Tree data structure was inspired
by work completed in the author's honours thesis. When compared to the
original octree data structure, the Plane-Tree is optimal in terms of compression
performance. The ndings presented on both the Fourier Volume
Registration method and the Plane-Tree indicate improvements over existing
methods.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Info & Comm TechScience, Environment, Engineering and TechnologyFull Tex
Facilitators and Barriers to User Adoption of Electronic Health Record Systems
No full textInformation Technology (IT) applications have brought massive changes in healthcare and health providers have shifted from paper-based systems to computerized ones. The electronic medical record (EMR) and personal health record (PHR) are good examples of the application of IT in healthcare settings. Despite the enormous benefits of the available applications in healthcare, the adoption of EMR in primary care has been identified at 38.4 percent in the U.S., in Denmark, almost 62 percent of doctors use EMR, while only 55 percent of Australian physicians apply EMR systems (Sicotte et al. 2016; Venkatesh et al. 2011). Furthermore, with regard to the PHR system, the Australian government’s development of a national PHR system (personally controlled electronic health record (PCEHR) system) in 2010 was a part of their national e-health strategy to overcome common challenges such as medication errors, fragmented sources of health information, repetition of tests, an increase in chronic illness, workforce resource constraints, and individuals’ changing expectations of technology. The Australian government expected that 500,000 users would register at the first release of the national PHR system; however, only 400,000 users have signed up to this system and of those, many registered but their records remain empty.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of information and Communication TechnologyScience, Environment, Engineering and TechnologyFull Tex
Structural Alignments for Similarity Detection in Bioinformatics
No full textThis thesis addresses problems involving structural alignments for similarity detection between entities. In the general computational context, a structural alignment is defined as an optimization problem where representative inputs are assigned to relative positions subject to the minimization of some objective function. The output is an inferred relationship based upon the resultant value of the objective function, and/or the arrangement of aligned positions. Two bioinformatics similarity detection applications were used as case studies within this work, the structural alignment of biomolecular proteins and the document similarity detection problem in biomedical literature.
The structural alignment of protein biomolecules involves generating residue pair correspondences of maximal overlap with minimal geometric divergence using each protein’s set of three-dimensional atomic coordinates. As protein structure decides its functionality, similarity in structure usually implies similarity in function. During the investigation of this structural alignment problem, it became apparent that a fast and approximate asymmetric linear sum assignment algorithm was required. Accordingly, a new heuristic algorithm, Asymmetric Greedy Search (AGS), was developed. Extensive computational experiments using a range of model graphs demonstrated the effectiveness of the algorithm. In addition, a new type of deterministic model graph that is suitable for reproducible benchmarking of these types of algorithms was also developed. Incorporating AGS, a new non-sequential protein structure alignment method, SPalignNS, was then developed. As compared to existing methods, SPalignNS achieved greater alignment accuracy with commonly used protein alignment test datasets, and also achieved the highest agreement with manually curated reference alignments.
The document similarity detection problem is a fundamental application of natural language processing, and constitutes the basis of information retrieval systems. Document matching systems for locating relevant literature have mostly relied on methods developed over a decade ago, largely due to the unavailability of a common evaluation framework. A database of relevance annotations for over 180,000 PubMed-listed document pairs was developed with a subsequent application in training a sentence-based transferred learning model, HuBERT (Hierarchical PubMed BERT). When applied to relevant biomedical literature searches in PubMed, the new HuBERT method produced superior results compared to those attained by the baseline methods from existing document matching systems.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Info & Comm TechScience, Environment, Engineering and TechnologyFull Tex
Impact of additional hardware resources on a parallel genetic algorithm
No full textAll road authorities are required to make sound maintenance investment decisions to maximise value from available budgets. As an indication of the complexity of this task, the schedule of pavement maintenance and rehabilitation for a small pavement network consisting of 200 segments, with four treatment alternatives over a planning period of five years has (2004)5 = 1.05 * 1046 possible alternatives. This study investigates the number and quality of solutions obtained by adding additional computing resources to a budget constrained implementation of a Parallel Genetic Algorithm based pavement management treatment scheduling system.No Full Tex
A Population Based Hybrid Meta-heuristic for the Uncapacitated Facility Location Problem
No full textThe uncapacitated facility location problem is one of finding the minimum cost subset of m facilities, where each facility has an associated establishment cost, to satisfy the demands of n users where the cost of satisfying each user from all possible facilities is known. In this paper, PBS, a population based metaheuristic for the uncapacitated facility location problem is introduced. PBS uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover and directed mutation operators, to generate new starting points for a local search. For larger uncapacitated facility location instances, PBS is able to effectively utilise a number of computer processors. It is shown empirically that PBS achieves state-of-the-art performance for a wide range of uncapacitated facility location benchmark instances.Griffith Sciences, School of Information and Communication TechnologyNo Full Tex
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