Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Effects of free flow state highway traffic parameters on the performance of amplify and forward cooperative multi-hop vehicular networks

In this paper we analyse the effects of highway traffic flow parameters like vehicle arrival rate and density on the performance of Amplify and Forward (AF) cooperative vehicular networks along a multi-lane highway under free flow state. We derive analytical expressions for connectivity performance and verify them with Monte-Carlo simulations. When AF cooperative relaying is employed together with Maximum Ratio Combining (MRC) at the receivers the average route error rate shows 10-20 fold improvement compared to direct communication. A 4-8 fold increase in maximum number of traversable hops can also be observed at different vehicle densities when AF cooperative communication is used to strengthen communication routes. However the theorical upper bound of maximum number of hops promises higher performance gains.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Property case competition : multi-stakeholder reflection

Pacific Rim Real Estate Society has conducted four property case competitions from 2009 to 2012. The competition provides opportunities for undergraduate students to present their proposal on a given case study. All students were locked down with their four team members for five hours without external help to ensure a level playing field across participants. Students prepared their presentation and defended their arguments in front of experts in property industry and academia. The aim of this paper is reflecting on the feedback received from stakeholders involved in the case competition. Besides exploring what students have gained from the competitions, this paper provides an insight on the opportunities and challenges for the new format of competition to be introduced in 2013. Over the last four competitions, there were three universities participated in all the four consecutive events, four universities partook in two events and another four universities only competed once. Some universities had a great advantage by having previous experiences by participating in similar international business competitions. Findings show that the students have benefited greatly from the event including improving their ability in problem solving and other non-technical skills. Despite the aforementioned benefits, the PRRES closed-book case competition is proven not viable thus future competition needs to minimise the travel and logistic cost.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

Predicate encryption for multi-inner-products

Predicate encryption is a new primitive that supports flexible control over access to encrypted data. We study predicate encryption systems, evaluating a wide class of predicates. Our systems are more expressive than the existing attribute-hiding systems in the sense that the proposed constructions support not only all existing predicate evaluations but also arbitrary conjunctions and disjunctions of comparison and subset queries. Toward our goal, we propose encryption schemes supporting multi-inner-product predicate and provide formal security analysis. We show how to apply the proposed schemes to achieve all those predicate evaluations.

Development of the palliative care needs assessment tool (PC-NAT) for use by multi-disciplinary health professionals

Needs assessment strategies can facilitate prioritisation of resources. To develop a needs assessment tool for use with advanced cancer patients and caregivers, to prompt early intervation. A convenience sample of 103 health professionals viewed three videotaped consultations involving a simulated patient, his/her caregiver and a health professional, completed the Palliative Care Needs Assessment Tool (PC-NAT) and provided feedback on clarity, content and acceptability of the PC-NAT. Face and content validity, acceptability and feasibility of the PC-NAT were confirmed. Kappa scores indicated adequate inter-rater reliability for the majority of domains; the patient spirituality domain and the caregiver physical and family and relationship domains had low reliability. The PC-NAT can be used by health professionals with a range of clinical expertise to identify individuals' needs, thereby enabling early intervention. Further psychometric testing and an evaluation to assess the impact of the systematic use of the PC-NAT on quality of life, unmet needs and service utilisation of patients and caregivers are underway.

The Cube : a very large-scale interactive engagement space

“The Cube” is a unique facility that combines 48 large multi-touch screens and very large-scale projection surfaces to form one of the world’s largest interactive learning and engagement spaces. The Cube facility is part of the Queensland University of Technology’s (QUT) newly established Science and Engineering Centre, designed to showcase QUT’s teaching and research capabilities in the STEM (Science, Technology, Engineering, and Mathematics) disciplines. In this application paper we describe, the Cube, its technical capabilities, design rationale and practical day-to-day operations, supporting up to 70,000 visitors per week. Essential to the Cube’s operation are five interactive applications designed and developed in tandem with the Cube’s technical infrastructure. Each of the Cube’s launch applications was designed and delivered by an independent team, while the overall vision of the Cube was shepherded by a small executive team. The diversity of design, implementation and integration approaches pursued by these five teams provides some insight into the challenges, and opportunities, presented when working with large distributed interaction technologies. We describe each of these applications in order to discuss the different challenges and user needs they address, which types of interactions they support and how they utilise the capabilities of the Cube facility.

CubIT : Large-scale multi-user presentation and collaboration

CubIT is a multi-user, large-scale presentation and collaboration framework installed at the Queensland University of Technology’s (QUT) Cube facility, an interactive facility made up 48 multi-touch screens and very large projected display screens. CubIT was built to make the Cube facility accessible to QUT’s academic and student population. The system allows users to upload, interact with and share media content on the Cube’s very large display surfaces. CubIT implements a unique combination of features including RFID authentication, content management through multiple interfaces, multi-user shared workspace support, drag and drop upload and sharing, dynamic state control between different parts of the system and execution and synchronisation of the system across multiple computing nodes.

A multi-objective optimization approach for coordinating available transfer capability with risk control

In the electricity market environment, coordination of system reliability and economics of a power system is of great significance in determining the available transfer capability (ATC). In addition, the risks associated with uncertainties should be properly addressed in the ATC determination process for risk-benefit maximization. Against this background, it is necessary that the ATC be optimally allocated and utilized within relative security constraints. First of all, the non-sequential Monte Carlo stimulation is employed to derive the probability density distribution of ATC of designated areas incorporating uncertainty factors. Second, on the basis of that, a multi-objective optimization model is formulated to determine the multi-area ATC so as to maximize the risk-benefits. Then, the solution to the developed model is achieved by the fast non-dominated sorting (NSGA-II) algorithm, which could decrease the risk caused by uncertainties while coordinating the ATCs of different areas. Finally, the IEEE 118-bus test system is served for demonstrating the essential features of the developed model and employed algorithm.

Multi-level information fusion for environment aware robotic navigation

This thesis develops the hardware and software framework for an integrated navigation system. Dynamic data fusion algorithms are used to develop a system with a high level of resistance to the typical problems that affect standard navigation systems.

Integrated multi-professional teams in primary care : rhetoric or reality?

Integrated multi-professional teams are crucial to ongoing health system development and need to be responsive to the increasing demands of health care such as the burgeoning rate of chronic diseases. Integrated multi-professional teams also constitute a fundamental pillar of health service delivery in primary care worldwide. The aim of these teams is to deliver care beyond simple co-location of healthcare providers, through implementing integrated practice together, rather than as a group of independent disciplines. The challenges of developing and implementing integrated multi-professional teams in busy primary care clinical environments is addressed in this paper through a conceptual framework specifically designed for primary care and a case study analysis of examples of teamwork in Australian primary care.

Supporting collaboration in large-scale multi-user workspaces

CubIT is a multi-user, large-scale presentation and collaboration framework installed at the Queensland University of Technology’s (QUT) Cube facility, an interactive facility made up 48 multi-touch screens and very large projected display screens. The CubIT system allows users to upload, interact with and share their own content on the Cube’s display surfaces. This paper outlines the collaborative features of CubIT which are implemented via three user interfaces, a large-screen multi-touch interface, a mobile phone and tablet application and a web-based content management system. Each of these applications plays a different role and supports different interaction mechanisms supporting a wide range of collaborative features including multi-user shared workspaces, drag and drop upload and sharing between users, session management and dynamic state control between different parts of the system.

Multi-gene phylogenetic analysis of south-east Asian pest members of the Bactrocera dorsalis species complex (Diptera: Tephritidae) does not support current taxonomy

Bactrocera dorsalis sensu stricto, B. papayae, B. philippinensis and B. carambolae are serious pest fruit fly species of the B. dorsalis complex that predominantly occur in south-east Asia and the Pacific. Identifying molecular diagnostics has proven problematic for these four taxa, a situation that cofounds biosecurity and quarantine efforts and which may be the result of at least some of these taxa representing the same biological species. We therefore conducted a phylogenetic study of these four species (and closely related outgroup taxa) based on the individuals collected from a wide geographic range; sequencing six loci (cox1, nad4-3′, CAD, period, ITS1, ITS2) for approximately 20 individuals from each of 16 sample sites. Data were analysed within maximum likelihood and Bayesian phylogenetic frameworks for individual loci and concatenated data sets for which we applied multiple monophyly and species delimitation tests. Species monophyly was measured by clade support, posterior probability or bootstrap resampling for Bayesian and likelihood analyses respectively, Rosenberg's reciprocal monophyly measure, P(AB), Rodrigo's (P(RD)) and the genealogical sorting index, gsi. We specifically tested whether there was phylogenetic support for the four 'ingroup' pest species using a data set of multiple individuals sampled from a number of populations. Based on our combined data set, Bactrocera carambolae emerges as a distinct monophyletic clade, whereas B. dorsalis s.s., B. papayae and B. philippinensis are unresolved. These data add to the growing body of evidence that B. dorsalis s.s., B. papayae and B. philippinensis are the same biological species, which poses consequences for quarantine, trade and pest management.