Off-world robotic excavation for Large-scale habitat construction and resource extraction

This paper describes technologies we have developed to perform autonomous large-scale off-world excavation. A scale dragline excavator of size similar to that required for lunar excavation was made capable of autonomous control. Systems have been put in place to allow remote operation of the machine from anywhere in the world. Algorithms have been developed for complete autonomous digging and dumping of material taking into account machine and terrain constraints and regolith variability. Experimental results are presented showing the ability to autonomously excavate and move large amounts of regolith and accurately place it at a specified location.

Three-dimensional imaging for a very large excavator

In this paper we describe the development of a three-dimensional (3D) imaging system for a 3500 tonne mining machine (dragline).Draglines are large walking cranes used for removing the dirt that covers a coal seam. Our group has been developing a dragline swing automation system since 1994. The system so far has been `blind' to its external environment. The work presented in this paper attempts to give the dragline an ability to sense its surroundings. A 3D digital terrain map (DTM) is created from data obtained from a two-dimensional laser scanner while the dragline swings. Experimental data from an operational dragline are presented.

A confirmatory factor analysis of the Toronto Alexithymia Scale (TAS-20) in an alcohol-dependent sample

Confirmatory factor analyses were conducted to evaluate the factorial validity of the Toronto Alexithymia Scale in an alcohol-dependent sample. Several factor models were examined, but all models were rejected given their poor fit. A revision of the TAS-20 in alcohol-dependent populations may be needed.

A connective model for the practice-led research exegesis : an analysis of content and structure

Since the formal recognition of practice-led research in the 1990s, many higher research degree candidates in art, design and media have submitted creative works along with an accompanying written document or ‘exegesis’ for examination. Various models for the exegesis have been proposed in university guidelines and academic texts during the past decade, and students and supervisors have experimented with its contents and structure. With a substantial number of exegeses submitted and archived, it has now become possible to move beyond proposition to empirical analysis. In this article we present the findings of a content analysis of a large, local sample of submitted exegeses. We identify the emergence of a persistent pattern in the types of content included as well as overall structure. Besides an introduction and conclusion, this pattern includes three main parts, which can be summarized as situating concepts (conceptual definitions and theories); precedents of practice (traditions and exemplars in the field); and researcher’s creative practice (the creative process, the artifacts produced and their value as research). We argue that this model combines earlier approaches to the exegesis, which oscillated between academic objectivity, by providing a contextual framework for the practice, and personal reflexivity, by providing commentary on the creative practice. But this model is more than simply a hybrid: it provides a dual orientation, which allows the researcher to both situate their creative practice within a trajectory of research and do justice to its personally invested poetics. By performing the important function of connecting the practice and creative work to a wider emergent field, the model helps to support claims for a research contribution to the field. We call it a connective model of exegesis.

An adaptive local meshfree updated Lagrangian approach for large deformation analysis of metal forming

The large deformation analysis is one of major challenges in numerical modelling and simulation of metal forming. Because no mesh is used, the meshfree methods show good potential for the large deformation analysis. In this paper, a local meshfree formulation, based on the local weak-forms and the updated Lagrangian (UL) approach, is developed for the large deformation analysis. To fully employ the advantages of meshfree methods, a simple and effective adaptive technique is proposed, and this procedure is much easier than the re-meshing in FEM. Numerical examples of large deformation analysis are presented to demonstrate the effectiveness of the newly developed nonlinear meshfree approach. It has been found that the developed meshfree technique provides a superior performance to the conventional FEM in dealing with large deformation problems for metal forming.

Large margin vector quantization

In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

An investigation of foot and ankle problems experienced by nurses

Relatively little information has been reported about foot and ankle problems experienced by nurses, despite anecdotal evidence which suggests they are common ailments. The purpose of this study was to improve knowledge about the prevalence of foot and ankle musculoskeletal disorders (MSDs) and to explore relationships between these MSDs and proposed risk factors. A review of the literature relating to work-related MSDs, MSDs in nursing, foot and lower-limb MSDs, screening for work-related MSDs, foot discomfort, footwear and the prevalence of foot problems in the community was undertaken. Based on the review, theoretical risk factors were proposed that pertained to the individual characteristics of the nurses, their work activity or their work environment. Three studies were then undertaken. A cross-sectional survey of 304 nurses, working in a large tertiary paediatric hospital, established the prevalence of foot and ankle MSDs. The survey collected information about self-reported risk factors of interest. The second study involved the clinical examination of a subgroup of 40 nurses, to examine changes in body discomfort, foot discomfort and postural sway over the course of a single work shift. Objective measurements of additional risk factors, such as individual foot posture (arch index) and the hardness of shoe midsoles, were performed. A final study was used to confirm the test-retest reliability of important aspects of the survey and key clinical measurements. Foot and ankle problems were the most common MSDs experienced by nurses in the preceding seven days (42.7% of nurses). They were the second most common MSDs to cause disability in the last 12 months (17.4% of nurses), and the third most common MSDs experienced by nurses in the last 12 months (54% of nurses). Substantial foot discomfort (Visual Analogue Scale (VAS) score of 50mm or more) was experienced by 48.5% of nurses at sometime in the last 12 months. Individual risk factors, such as obesity and the number of self-reported foot conditions (e.g., callouses, curled toes, flat feet) were strongly associated with the likelihood of experiencing foot problems in the last seven days or during the last 12 months. These risk factors showed consistent associations with disabling foot conditions and substantial foot discomfort. Some of these associations were dependent upon work-related risk factors, such as the location within the hospital and the average hours worked per week. Working in the intensive care unit was associated with higher odds of experiencing foot problems within the last seven days, foot problems in the last 12 months and foot problems that impaired activity in the last 12 months. Changes in foot discomfort experienced within a day, showed large individual variability. Fifteen of the forty nurses experienced moderate/substantial foot discomfort at the end of their shift (VAS 25+mm). Analysis of the association between risk factors and moderate/substantial foot discomfort revealed that foot discomfort was less likely for nurses who were older, had greater BMI or had lower foot arches, as indicated by higher arch index scores. The nurses’ postural sway decreased over the course of the work shift, suggesting improved body balance by the end of the day. These findings were unexpected. Further clinical studies examining individual nurses on several work shifts are needed to confirm these results, particularly due to the small sample size and the single measurement occasion. There are more than 280,000 nurses registered to practice in Australia. The nursing workforce is ageing and the prevalence of foot problems will increase. If the prevalence estimates from this study are extrapolated to the profession generally, more than 70,000 hospital nurses have experienced substantial foot discomfort and 25-30,000 hospital nurses have been limited in their activity due to foot problems during the last 12 months. Nurses with underlying foot conditions were more likely to report having foot problems at work. Strategies to prevent or manage foot conditions exist and they should be disseminated to nurses. Obesity is a significant risk factor for foot and ankle MSDs and these nurses may need particular assistance to manage foot problems. The risk of foot problems for particular groups of nurses, e.g. obese nurses, may vary depending upon the location within the hospital. Further research is needed to confirm the findings of this study. Similar studies should be conducted in other occupational groups that require workers to stand for prolonged periods.

Overhead electricity line connector tester

Assessment of the condition of connectors in the overhead electricity network has traditionally relied on the heat dissipation or voltage drop from existing load current (50Hz) as a measurable parameter to differentiate between satisfactory and failing connectors. This research has developed a technique which does not rely on the 50Hz current and a prototype connector tester has been developed. In this system a high frequency signal is injected into the section of line under test and measures the resistive voltage drop and the current at the test frequency to yield the resistance in micro-ohms. From the value of resistance a decision as to whether a connector is satisfactory or approaching failure can be made. Determining the resistive voltage drop in the presence of a large induced voltage was achieved by the innovative approach of using a representative sample of the magnetic flux producing the induced voltage as the phase angle reference for the signal processing rather than the phase angle of the current, which can be affected by the presence of nearby metal objects. Laboratory evaluation of the connector tester has validated the measurement technique. The magnitude of the load current (50Hz) has minimal effect on the measurement accuracy. Addition of a suitable battery based power supply system and isolated communications, probably radio and refinement of the printed circuit board design and software are the remaining development steps to a production instrument.