Large-scale analysis of soccer matches using spatiotemporal tracking data

Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (approx 400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

Renewing the Sustainable Energy Curriculum : Curriculum Frameworks and Guidance for Course Delivery

This guide is to support institutions in developing and teaching tertiary level programmes for sustainable energy professionals. Ongoing curriculum renewal is more difficult but vital for multidisciplinary courses preparing graduates to work in a specialised rapidly changing field. After more than 15 years of offering tertiary level “sustainable energy” qualifications in Australian Universities there was a clear need to assess how these courses are taught and develop curriculum frameworks to guide Universities designing/redesigning programs and courses to provide graduates with the relevant skills, knowledge and attributes (capabilities) seen by graduates and employers as required to work in this rapidly changing field. This guide presents the sustainable energy curriculum frameworks developed by the “Renewing the sustainable energy curriculum – providing internationally relevant skills for a carbon constrained economy” project, which was conducted over a two-and-a-quarter year period.

A distributed computing framework for all-to-all comparison problems

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity have be achieved in multiple machines through using the approach presented in this paper.

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

Visual predictive control of spiral motion

This paper deals with constrained image-based visual servoing of circular and conical spiral motion about an unknown object approximating a single image point feature. Effective visual control of such trajectories has many applications for small unmanned aerial vehicles, including surveillance and inspection, forced landing (homing), and collision avoidance. A spherical camera model is used to derive a novel visual-predictive controller (VPC) using stability-based design methods for general nonlinear model-predictive control. In particular, a quasi-infinite horizon visual-predictive control scheme is derived. A terminal region, which is used as a constraint in the controller structure, can be used to guide appropriate reference image features for spiral tracking with respect to nominal stability and feasibility. Robustness properties are also discussed with respect to parameter uncertainty and additive noise. A comparison with competing visual-predictive control schemes is made, and some experimental results using a small quad rotor platform are given.

Comparison of the gate drive parameter space for driving power MOSFETs using conventional and cascode configurations

Conventional voltage driven gate drive circuits utilise a resistor to control the switching speed of power MOS-FETs. The gate resistance is adjusted to provide controlled rate of change of load current and voltage. The cascode gate drive configuration has been proposed as an alternative to the conventional resistor-fed gate drive circuit. While cascode drive is broadly understood in the literature the switching characteristics of this topology are not well documented. This paper explores, through both simulation and experimentation, the gate drive parameter space of the cascode gate drive configuration and provides a comparison to the switching characteristics of conventional gate drive.

Designing for collaborative learning spaces : an academic staff development pilot program

To prepare for the delivery of new Bachelor of Science units in collaborative learning spaces, academic and professional staff at Queensland University of Technology piloted an academic development program over the period of a semester. The program was informed by Rogers’ theory of innovation and diffusion (2003) and structured according to Wilson’s framework for faculty development (2007). Through a series of workshops and group mentoring activities, the program modelled inquiry-based learning in a collaborative learning space, and the participants designed and practiced the delivery of teaching activities. This paper provides a preliminary evaluation of the effectiveness of the pilot based on survey responses from participants, notes from the development team who coordinated the program and audience feedback from the final showcase session. The design and structure of the program is discussed as well as possible future directions.

Business transformation management : composition and orchestration of management services

This research develops a better understanding on how large-scale and complex IT-enabled business transformations are managed. Evidence from three global case studies suggest that business transformations can be composed and orchestrated like a jazz band, where improvisation plays a fundamental role to maintain the melody, harmony and rhythm of such initiatives. The thesis details how the jazz metaphor can assist senior management on how to reuse and reconfigure capabilities as services for transforming organizations. To the academic body of knowledge, the thesis provides a study on the use of management services as a theoretical lens to examine Business Transformation Management.

Assembly and disassembly of temporary public spaces : establishing the theoretical needs

Contemporary cities no longer offer the same types of permanent environments that we planned for in the latter part of the twentieth century. Our public spaces are increasingly temporary, transient, and ephemeral. The theories, principles and tactics with which we designed these spaces in the past are no longer appropriate. We need a new theory for understanding the creation, use, and reuse of temporary public space. Moe than a theory, we need new architectural tactics or strategies that can be reliably employed to create successful temporary public spaces. This paper will present ongoing research that starts that process through critical review and technical analysis of existing and historic temporary public spaces. Through the analysis of a number of public spaces, that were either designed for temporary use or became temporary through changing social conditions, this research identifies the tactics and heuristics used in such projects. These tactics and heuristics are then analysed to extract some broader principles for the design of temporary public space. The theories of time related building layers, a model of environmental sustainability, and the recycling of social meaning, are all explored. The paper will go on to identify a number of key questions that need to be explored and addressed by a theory for such developments: How can we retain social meaning in the fabric of the city and its public spaces while we disassemble it and recycle it into new purposes? What role will preservation have in the rapidly changing future; will exemplary temporary spaces be preserved and thereby become no longer temporary? Does the environmental advantage of recycling materials, components and spaces outweigh the removal or social loss of temporary public space? This research starts to identify the knowledge gaps and proposes a number of strategies for making public space in the age of temporary, recyclable, and repurposing of our urban infrastructure; a way of creating lighter, cheaper, quicker, and temporary interventions.

Understanding welding practices on shipyards : an ethnographic study for designing an interactive robot welder

The aim of this ethnographic study was to understand welding practices in shipyard environments with the purpose of designing an interactive welding robot that can help workers with their daily job. The robot is meant to be deployed for automatic welding on jack-up rig structures. The design of the robot turns out to be a challenging task due to several problematic working conditions on the shipyard, such as dust, irregular floor, high temperature, wind variations, elevated working platforms, narrow spaces, and circular welding paths requiring a robotic arm with more than 6 degrees of freedom. Additionally, the environment is very noisy and the workers – mostly foreigners – have a very basic level of English. These two issues need to be taken into account when designing the interactive user interface for the robot. Ideally, the communication flow between the two parties involved should be as frictionless as possible. The paper presents the results of our field observations and welders’ interviews, as well as our robot design recommendation for the next project stage.

Passenger segmentation using smart card data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Translocation of bats as a conservation strategy : previous attempts and potential problems

Translocation is an increasingly popular conservation tool from which a wide range of taxa have benefited. However, to our knowledge, bats have not been translocated successfully. Bats differ behaviourally, morphologically and physiologically from the taxa for which translocation the- ory has been developed, so existing guidelines may not be directly transferable. We review previous translocations of bats and discuss characteristics of bats that may require special consideration dur- ing translocation. Their vagility and homing ability, coloniality, roost requirements, potential ability to transmit diseases, susceptibility to anthropomorphic impacts, and cryptic nature have implications for establishing populations, effects of these populations on the release site, and ability to monitor translocation success following release. We hope that our discussion of potential problems will be able to supplement the existing, more generic guidelines to provide a starting point for the planning of bat translocations.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Stormwater management: An introduction to Green Gully

In recent years there has been increasing interest in the use of water resources generated within the urban boundary for potable supply substitution as a means of augmenting the current supply capacity. These urban water resources include roof and stormwater runoff. Expanding the use of stormwater runoff to add to the water supply and reduce water pollution are important objectives all over Australia. This book presents the background, significance and objectives of the research, as well as the reasons why stormwater plays a significant role as an alternative source of water.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.