Multi-vehicles interaction graph model for cooperative collision warning system

Cooperative collision warning system for road vehicles, enabled by recent advances in positioning systems and wireless communication technologies, can potentially reduce traffic accident significantly. To improve the system, we propose a graph model to represent interactions between multiple road vehicles in a specific region and at a specific time. Given a list of vehicles in vicinity, we can generate the interaction graph using several rules that consider vehicle's properties such as position, speed, heading, etc. Safety applications can use the model to improve emergency warning accuracy and optimize wireless channel usage. The model allows us to develop some congestion control strategies for an efficient multi-hop broadcast protocol.

Assessing the energy efficiency of gear oils via the FZG test machine

Energy efficient lubricants are becoming increasingly popular. This is due to a global increase in environmental awareness combined with the potential of reducing operating costs. A new test method of evaluating the energy efficiency of gear oils has been described in this report. The method involves measuring the power required by an FZG test rig to run while using a particular test lubricant. For each oil that was being evaluated, the rig was run for 10 minutes at a load stage of 10. Six extreme pressure (EP) industrial gear oils of mineral base were tested. The difference in power requirements between the best and the worst performing oils was 2.77 and 3.24 kW, respectively. This equates to a 14.6% reduction in power, a significant amount if considered in relation to a high powered industrial machine. The oils of superior performance were noticed to run at reduced temperatures. They were also more expensive than the other products of lesser performance.

Health, Death, and Indigenous Australians in the Coronial system

This paper details research conducted in Queensland during the first year of operation of the new Coroners Act 2003. Information was gathered from all completed investigations between December 2003 and December 2004 across five categories of death: accidental, suicide, natural, medical and homicide. It was found that 25 percent of the total number of Indigenous deaths recorded in 2004 were reported to, and investigated by, the Coroner, in comparison to 9.4 percent of non-Indigenous deaths. Moreover, Indigenous people were found to be over-represented in each category of death, except in death in a medical setting, where they were absent.

Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

A Vision Based Target Detection System for Docking of an Autonomous Underwater Vehicle

This paper describes the development and preliminary experimental evaluation of a visionbased docking system to allow an Autonomous Underwater Vehicle (AUV) to identify and attach itself to a set of uniquely identifiable targets. These targets, docking poles, are detected using Haar rectangular features and rotation of integral images. A non-holonomic controller allows the Starbug AUV to orient itself with respect to the target whilst maintaining visual contact during the manoeuvre. Experimental results show the proposed vision system is capable of robustly identifying a pair of docking poles simultaneously in a variety of orientations and lighting conditions. Experiments in an outdoor pool show that this vision system enables the AUV to dock autonomously from a distance of up to 4m with relatively low visibility.

The susceptibility of digital signatures to fraud in the National Electronic Conveyancing System : an analysis

Public key cryptography, and with it,the ability to compute digital signatures, have made it possible for electronic commerce to flourish. It is thus unsurprising that the proposed Australian NECS will also utilise digital signatures in its system so as to provide a fully automated process from the creation of electronic land title instrument to the digital signing, and electronic lodgment of these instruments. This necessitates an analysis of the fraud risks raised by the usage of digital signatures because a compromise of the integrity of digital signatures will lead to a compromise of the Torrens system itself. This article will show that digital signatures may in fact offer greater security against fraud than handwritten signatures; but to achieve this, digital signatures require an infrastructure whereby each component is properly implemented and managed.

Groundwater flow model of the Logan river alluvial aquifer system Josephville, South East Queensland

The study focuses on an alluvial plain situated within a large meander of the Logan River at Josephville near Beaudesert which supports a factory that processes gelatine. The plant draws water from on site bores, as well as the Logan River, for its production processes and produces approximately 1.5 ML per day (Douglas Partners, 2004) of waste water containing high levels of dissolved ions. At present a series of treatment ponds are used to aerate the waste water reducing the level of organic matter; the water is then used to irrigate grazing land around the site. Within the study the hydrogeology is investigated, a conceptual groundwater model is produced and a numerical groundwater flow model is developed from this. On the site are several bores that access groundwater, plus a network of monitoring bores. Assessment of drilling logs shows the area is formed from a mixture of poorly sorted Quaternary alluvial sediments with a laterally continuous aquifer comprised of coarse sands and fine gravels that is in contact with the river. This aquifer occurs at a depth of between 11 and 15 metres and is overlain by a heterogeneous mixture of silts, sands and clays. The study investigates the degree of interaction between the river and the groundwater within the fluvially derived sediments for reasons of both environmental monitoring and sustainability of the potential local groundwater resource. A conceptual hydrogeological model of the site proposes two hydrostratigraphic units, a basal aquifer of coarse-grained materials overlain by a thick semi-confining unit of finer materials. From this, a two-layer groundwater flow model and hydraulic conductivity distribution was developed based on bore monitoring and rainfall data using MODFLOW (McDonald and Harbaugh, 1988) and PEST (Doherty, 2004) based on GMS 6.5 software (EMSI, 2008). A second model was also considered with the alluvium represented as a single hydrogeological unit. Both models were calibrated to steady state conditions and sensitivity analyses of the parameters has demonstrated that both models are very stable for changes in the range of ± 10% for all parameters and still reasonably stable for changes up to ± 20% with RMS errors in the model always less that 10%. The preferred two-layer model was found to give the more realistic representation of the site, where water level variations and the numerical modeling showed that the basal layer of coarse sands and fine gravels is hydraulically connected to the river and the upper layer comprising a poorly sorted mixture of silt-rich clays and sands of very low permeability limits infiltration from the surface to the lower layer. The paucity of historical data has limited the numerical modelling to a steady state one based on groundwater levels during a drought period and forecasts for varying hydrological conditions (e.g. short term as well as prolonged dry and wet conditions) cannot reasonably be made from such a model. If future modelling is to be undertaken it is necessary to establish a regular program of groundwater monitoring and maintain a long term database of water levels to enable a transient model to be developed at a later stage. This will require a valid monitoring network to be designed with additional bores required for adequate coverage of the hydrogeological conditions at the Josephville site. Further investigations would also be enhanced by undertaking pump testing to investigate hydrogeological properties in the aquifer.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Queensland Emergency Medical System : a structural and organizational model for the emergency medical system in Australia

Objective: The emergency medical system (EMS) can be defined as a comprehensive, coordinated and integrated system of care for patients suffering acute illness and injury. The aim of the present paper is to describe the evolution of the Queensland Emergency Medical System (QEMS) and to recommend a strategic national approach to EMS development. Methods: Following the formation of the Queensland Ambulance Service in 1991, a state EMS committee was formed. This committee led the development and approval of the cross portfolio QEMS policy framework that has resulted in dynamic policy development, system monitoring and evaluation. This framework is led by the Queensland Emergency Medical Services Advisory Committee. Results: There has been considerable progress in the development of all aspects of the EMS in Queensland. These developments have derived from the improved coordination and leadership that QEMS provides and has resulted in widespread satisfaction by both patients and stakeholders. Conclusions: The strategic approach outlined in the present paper offers a model for EMS arrangements throughout Australia. We propose that the Council of Australian Governments should require each state and Territory to maintain an EMS committee. These state EMS committees should have a broad portfolio of responsibilities. They should provide leadership and direction to the development of the EMS and ensure coordination and quality of outcomes. A national EMS committee with broad representation and broad scope should be established to coordinate the national development of Australia's EMS.

A decision support system for sustainable urban development : the integrated land use and transportation indexing model

The broad definition of sustainable development at the early stage of its introduction has caused confusion and hesitation among local authorities and planning professionals. The main difficulties are experience in employing loosely-defined principles of sustainable development in setting policies and goals. The question of how this theory/rhetoric-practice gap could be filled will be the theme of this study. One of the widely employed sustainability accounting approaches by governmental organisations, triple bottom line, and applicability of this approach to sustainable urban development policies will be examined. When incorporating triple bottom line considerations with the environmental impact assessment techniques, the framework of GIS-based decision support system that helps decision-makers in selecting policy option according to the economic, environmental and social impacts will be introduced. In order to embrace sustainable urban development policy considerations, the relationship between urban form, travel pattern and socio-economic attributes should be clarified. This clarification associated with other input decision support systems will picture the holistic state of the urban settings in terms of sustainability. In this study, grid-based indexing methodology will be employed to visualise the degree of compatibility of selected scenarios with the designated sustainable urban future. In addition, this tool will provide valuable knowledge about the spatial dimension of the sustainable development. It will also give fine details about the possible impacts of urban development proposals by employing disaggregated spatial data analysis (e.g. land-use, transportation, urban services, population density, pollution, etc.). The visualisation capacity of this tool will help decision makers and other stakeholders compare and select alternative of future urban developments.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Experimenting with an intrusion detection system for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) analyse network traffic to detect instances of malicious activity. Typically, this is only possible when the network traffic is accessible for analysis. With the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer access this crucial audit data. In this paper, we present an implementation and evaluation of our approach proposed in Goh et al. (2009). It is based on Shamir's secret-sharing scheme and allows a NIDS to function normally in a VPN without any modifications and without compromising the confidentiality afforded by the VPN.

Scheduling trains as a blocking parallel-machine job shop scheduling problem

In this paper, the train scheduling problem is modelled as a blocking parallel-machine job shop scheduling (BPMJSS) problem. In the model, trains, single-track sections and multiple-track sections, respectively, are synonymous with jobs, single machines and parallel machines, and an operation is regarded as the movement/traversal of a train across a section. Due to the lack of buffer space, the real-life case should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold the train until next section on the routing becomes available. Based on literature review and our analysis, it is very hard to find a feasible complete schedule directly for BPMJSS problems. Firstly, a parallel-machine job-shop-scheduling (PMJSS) problem is solved by an improved shifting bottleneck procedure (SBP) algorithm without considering blocking conditions. Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models. The proposed algorithms have been implemented and validated using real-world data from Queensland Rail. Sensitivity analysis has been applied by considering train length, upgrading track sections, increasing train speed and changing bottleneck sections. The outcomes show that the proposed methodology would be a very useful tool for the real-life train scheduling problems