Improving daylighting performance of mirrored light pipes

This paper assesses and compares the performances of two daylight collection strategies, one passive and one active, for large-scale mirrored light pipes (MLP) illuminating deep plan buildings. Both strategies use laser cut panels (LCP) as the main component of the collection system. The passive system comprises LCPs in pyramid form, whereas the active system uses a tiled LCP on a simple rotation mechanism that rotates 360° in 24 hours. Performance is assessed using scale model testing under sunny sky conditions and mathematical modelling. Results show average illuminance levels for the pyramid LCP ranging from 50 to 250 lux and 150 to 200 lux for the rotating LCPs. Both systems improve the performance of a MLP. The pyramid LCP increases the performance of a MLP by 2.5 times and the rotating LCP by 5 times, when compared to an open pipe particularly for low sun elevation angles.

Experimental comparison of odometry approaches

Odometry is an important input to robot navigation systems, and we are interested in the performance of vision-only techniques. In this paper we experimentally evaluate and compare the performance of wheel odometry, monocular feature-based visual odometry, monocular patch-based visual odometry, and a technique that fuses wheel odometry and visual odometry, on a mobile robot operating in a typical indoor environment.

Building Large Scale Traversability Maps Using Vehicle Experience

Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a bounded working environment, such as a repeated delivery task.We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.

Inside the pleasure dome : cultural tourism on Australia’s Gold Coast

The City of the Gold Coast in Queensland, Australia, will host the Commonwealth Games in 2018. In advance of the Games, the City is beginning to reposition the traditional marketing programs that were based around the four S’s- ‘sun, sand, surf and sex.’ There is a new emphasis on urban sophistication, sport, science, education and the environment. At the same time, local communities are asking for renewed attention to residential issues, particularly relating to recognising the importance of culture to the region. In this paper I explore the development of integrated computer technologies (ICTs) as a way of linking tourism, culture and place in the experience economy of the Gold Coast. The discussion is framed by theories of the post-tourist, contemporary cultural tourism and the role of mobile technologies, and the figure of the ‘referential tourist.’ An examination of stakeholder responses to changing business and social frameworks on the Gold Coast shows how discussions about a range of issues coalesce around cultural tourism. Local communities have the opportunity to engage with the new tourist as they move quickly between leisure and cultural experiences, at once connected to tourist expectations but increasingly self-directed. The Surfers Paradise Nights campaign, which is based around social media, is a case in point. This campaign aims to interest visitors in becoming a part of a familiar third place, an online space, but one that will sustain an emotive connection to the physical location and events. The paper also draws on research carried out in Brisbane, Queensland, in relation to building connections between place and culture on designated, self-directed journeys via iPhone technology. Participant responses indicate the importance of narrative to developing cultural frameworks.

Deriving construction features from an IFC model

In recent years, there has been a growing interest from the design and construction community to adopt Building Information Models (BIM). BIM provides semantically-rich information models that explicitly represent both 3D geometric information (e.g., component dimensions), along with non-geometric properties (e.g., material properties). While the richness of design information offered by BIM is evident, there are still tremendous challenges in getting construction-specific information out of BIM, limiting the usability of these models for construction. In this paper, we describe our approach for extracting construction-specific design conditions from a BIM model based on user-defined queries. This approach leverages an ontology of features we are developing to formalize the design conditions that affect construction. Our current implementation analyzes the component geometry and topological relationships between components in a BIM model represented using the Industry Foundation Classes (IFC) to identify construction features. We describe the reasoning process implemented to extract these construction features, and provide a critique of the IFC’s to support the querying process. We use examples from two case studies to illustrate the construction features, the querying process, and the challenges involved in deriving construction features from an IFC model.

Steps towards the validation of the Trendelenburg test : The effect of experimentally reduced hip abductor muscle function on frontal plane mechanics

Introduction: The Trendelenburg Test (TT) is used to assess the functional strength of the hip abductor muscles (HABD), their ability to control frontal plane motion of the pelvis, and the ability of the lumbopelvic complex to transfer load into single leg stance. Rationale: Although a standard method to perform the test has been described for use within clinical populations, no study has directly investigated Trendelenburg’s hypotheses. Purpose: To investigate the validity of the TT using an ultrasound guided nerve block (UNB) of the superior gluteal nerve and determine whether the reduction in HABD strength would result in the theorized mechanical compensatory strategies measured during the TT. Methods: Quasi-experimental design using a convenience sample of nine healthy males. Only subjects with no current or previous injury to the lumbar spine, pelvis, or lower extremities, and no previous surgeries were included. Force dynamometry was used to evaluation HABD strength (%BW). 2D mechanics were used to evaluate contralateral pelvic drop (cMPD), change in contralateral pelvic drop (∆cMPD), ipsilateral hip adduction (iHADD) and ipsilateral trunk sway (TRUNK) measured in degrees (°). All measures were collected prior to and following a UNB on the superior gluteal nerve performed by an interventional radiologist. Results: Subjects’ age was median 31yrs (IQR:22-32yrs); and weight was median 73kg (IQR:67-81kg). An average 52% reduction of HABD strength (z=2.36,p=0.02) resulted following the UNB. No differences were found in cMPD or ∆cMPD (z=0.01,p= 0.99, z=-0.67,p=0.49). Individual changes in biomechanics show no consistency between subjects and non-systematic changes across the group. One subject demonstrated the mechanical compensations described by Trendelenburg. Discussion: The TT should not be used as screening measure for HABD strength in populations demonstrating strength greater than 30%BW but reserved for use with populations with marked HABD weakness. Importance: This study presents data regarding a critical level of HABD strength required to support the pelvis during the TT.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Reduction of Permittivity in Epoxy Nanocomposites at Low Nano-filler Loadings

Experimental studies reveal a reduction in the values of permittivity for epoxy nanocomposites; at low filler loadings as compared to neat epoxy over a wide frequency range. This permittivity reduction is attributed to the interaction dynamics between nanoparticles: and epoxy chains at the interface region and interestingly, this interaction has also been found to influence the glass transition temperatures (T-g) of the examined nanocomposite systems. Accordingly, a dual nanolayer interface model for an epoxy based nanocomposite system is analyzed to explain the obtained permittivity characteristics.

Accelerated Multistress Aging of Outdoor Polymeric Insulators

Multistress aging of outdoor composite polymeric insulators continues to be a topic of interest for power transmission research community. Aging due to dry conditions alone at elevated temperatures and electric stress in the presence of UV radiation environment probably has not been explored. This paper deals with long-term accelerated multistress aging under the above conditions on full-scale 11 kV distribution class composite silicone rubber insulators. To evaluate the long-term synergistic effect of electric stress, temperature and UV radiation on insulators, they were subjected to accelerated aging in a specially designed multistress-aging chamber for 12000 hours. Chemical, physical and electrical changes due to degradation have been assessed using various techniques. It has been found that the content of low molecular weight molecules and hydrophobicity reduced significantly. Also, due to oxidation and aging there is appreciable increase in surface roughness and weight percentage of oxygen. Study is under progress and only intermediate results are presented in this paper.

Studies on the Tracking and Erosion Resistance of RTV Silicone Rubber Nanocomposite

One of the problems associated with outdoor polymeric insulators is tracking and erosion of the weathershed which can directly influence the reliability of the power system. Flame retardants are added to the base material to enhance its tracking and erosion resistance. Hydroxide fillers are regarded as the best flame retardants. This paper deals with studies related to nano - sized magnesium dihydroxide (MDH) and micron-sized Alumina Trihydrate (ATH) fillers as flame retardants in RTV silicone rubber. Tracking and erosion resistance studies were carried out on MDH and ATH silicone rubber composites using an inclined plane tracking and erosion (IPT) resistance tester. The MDH filled (5% by wt) composites performed much better than ATH composites in terms of eroded mass, depth of erosion, width and length of erosion. The eroded mass of MDH composite is 49.8 % that of ATH composite which can be attributed to high surface area and higher thermal stability of MDH nanofillers.

Electrical Discharge Resistant Characteristics of Epoxy Nanocomposites

This paper reports the electrical discharge resistant characteristics of epoxy nanocomposite systems with SiO2 and Al2O3 nano-fillers. A comparative study is performed between unfilled epoxy systems, nanoparticle filled epoxy systems and a bimodal system containing both micrometer and nanometer sized fillers of the same material. The samples are exposed to surface discharges and the levels of surface degradation are analyzed through SEM and surface roughness measurements. Significant variations were observed in the electrical discharge resistant characteristics between the different composite systems and it is seen that the introduction of nano-fillers to epoxy is advantageous in improving the electrical discharge resistance of epoxy.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.