Sharpening our tools – translating research into practice

Providing culturally appropriate health communication tools at a community level, whilst meeting funding objectives set by Government led initiatives, can be challenging. Literature states that a translational research framework fostering community communication can encourage the development of appropriate communication tools to facilitate transfer of health information between community and researchers. Reflections from initial Need for Feed cooking and nutrition education program trials in remote Indigenous communities across Cape York indicated program resources were neither meeting community nor researchers needs. In response, a translational research framework was modelled with collaborative partnerships formed between researchers and community with the aim of modifying current resources. Local working groups were established to facilitate communication and guide continual remodelling and retrial of resources for successive programs. Feedback from working groups indicated community members wanted resources with more pictures and less words. Partnership with Chronic Disease Resources Online (CDRO) led to the development of pictorial resources including 3 evaluation tools, 27 recipe sets and 10 education support materials. Between June to December 2012 resources were trialled across 4 Cape York communities with 69 school aged children and 4 community elders. Qualitative data has indicated high satisfaction with modified pictorial resources, proving pictorial resources to be an effective and culturally appropriate method to both communicate health messages to community and facilitate flow of evaluation data to researchers. A translational research framework fostering communication between community and researchers can potentially enhance the quality of health communication tools.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information.

Development of descriptor tools for the characterisation of Australian sugar mill evaporator scale

Cleaning of sugar mill evaporators is an expensive exercise. Identifying the scale components assists in determining which chemical cleaning agents would result in effective evaporator cleaning. The current methods (based on x-ray diffraction techniques, ion exchange/high performance liquid chromatography and thermogravimetry/differential thermal analysis) used for scale characterisation are difficult, time consuming and expensive, and cannot be performed in a conventional analytical laboratory or by mill staff. The present study has examined the use of simple descriptor tests for the characterisation of Australian sugar mill evaporator scales. Scale samples were obtained from seven Australian sugar mill evaporators by mechanical means. The appearance, texture and colour of the scale were noted before the samples were characterised using x-ray fluorescence and x-ray powder diffraction to determine the compounds present. A number of commercial analytical test kits were used to determine the phosphate and calcium contents of scale samples. Dissolution experiments were carried out on the scale samples with selected cleaning agents to provide relevant information about the effect the cleaning agents have on different evaporator scales. Results have shown that by simply identifying the colour and the appearance of the scale, the elemental composition and knowing from which effect the scale originates, a prediction of the scale composition can be made. These descriptors and dissolution experiments on scale samples can be used to provide factory staff with an on-site rapid process to predict the most effective chemicals for chemical cleaning of the evaporators.

Tools for Mobile Multimedia Programming and Development

Mobile devices are rapidly developing into the primary technology for users to work, socialize, and play in a variety of settings and contexts. Their pervasiveness has provided researchers with the means to investigate innovative solutions to ever more complex user demands. Tools for Mobile Multimedia Programming and Development investigates the use of mobile platforms for research projects, focusing on the development, testing, and evaluation of prototypes rather than final products, which enables researchers to better understand the needs of users through image processing, object recognition, sensor integration, and user interactions. This book benefits researchers and professionals in multiple disciplines who utilize such techniques in the creation of prototypes for mobile devices and applications. This book is part of the Advances in Wireless Technologies and Telecommunication series collection.

Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.

Using Participatory Design (PD) as youth engagement strategy to inform design and development of evidence-based wellbeing e-tools

The paper presents a participatory design research framework as a primary method for structuring youth engagement, participation and contribution to the design, development and usability evaluation of three evidencebased e-tools for wellbeing, which include smart phone mobile apps as well as e-health websites. The three projects are part of a series of six e-tools part of Safe and Supportive program under Young and Well CRC. The participatory design method, developed by Zelenko (2012) for application in design of online health promoting technologies, was further piloted in partnership with Inspire USA for specific application within the CRC, deploying a combination of creative design workshops and speculative design activities in developing e-tool prototypes with young people. This paper presents the resulting participatory research framework as it was implemented across the e-tool projects to facilitate active youth participation in co-designing the e-tools and ensuring the final designs are relevant to young people and deliver health messages in engaging ways. The principles of Participatory Design (PD) that inform the new framework include a high degree of participant agency in creative decisionmaking and a commitment to the process of co-designing, with young people working alongside designers and developers. The paper will showcase how the PD framework was applied across three projects to increase young people’s contribution to final design outcome.

Can the use of quality assurance tools reduce the impact of surgical complications on the well-being of obstetricians and gynaecologists in Australia and New Zealand?

Background While adverse events primarily affect the patient, surgeons involved can also experience considerable distress. Aims The aim of the survey was to assess the impact of complications on the day-to-day life, work and health of Australian and New Zealand obstetricians and gynaecologists and to evaluate existing support systems and coping strategies. Methods A 43-question survey on self-assessment, quality assurance (QA) tools, impact of complications on individuals’ health and relationships, and support available was emailed to fellows, trainees, subspecialists and subspecialty trainees of the Royal Australian and New Zealand College of Obstetrics and Gynaecology (RANZCOG). We collected 606 responses from a target population of 2296 (response rate 26.3%). Results When complications occur, sleep was affected of 80%, family and social relationships of 55% and physical health of 48% of respondents. The major sources of support were from colleagues (83%), family (82%) and medical defence organisations (73%), with professional bodies perceived as providing least support. Nearly 80% of respondents felt the need to talk to someone they trust during times of complications. Overall, 100% of respondents used at least one QA tool (62% used two, 26% three and 9% four QA strategies). There were significant differences between respondent groups in use of the QA tools. Conclusions Surgical complications have a significant impact on the well-being of Australian and New Zealand obstetricians and gynaecologists. Existing support comes from colleagues and family, but structured, unbiased support for surgeons from a professional source is urgently warranted.

Towards the development of 1-to-n human machine interfaces for unmanned aerial vehicles

Multi-touch interfaces across a wide range of hardware platforms are becoming pervasive. This is due to the adoption of smart phones and tablets in both the consumer and corporate market place. This paper proposes a human-machine interface to interact with unmanned aerial systems based on the philosophy of multi-touch hardware-independent high-level interaction with multiple systems simultaneously. Our approach incorporates emerging development methods for multi-touch interfaces on mobile platforms. A framework is defined for supporting multiple protocols. An open source solution is presented that demonstrates: architecture supporting different communications hardware; an extensible approach for supporting multiple protocols; and the ability to monitor and interact with multiple UAVs from multiple clients simultaneously. Validation tests were conducted to assess the performance, scalability and impact on packet latency under different client configurations.

Modeling and sensorless direct torque and flux control of a dual-airgap axial flux permanent-magnet machine with field-weakening operation

This paper presents the modeling and motion-sensorless direct torque and flux control of a novel dual-airgap axial-flux permanent-magnet machine optimized for use in flywheel energy storage system (FESS) applications. Independent closed-loop torque and stator flux regulation are performed in the stator flux ( x-y) reference frame via two PI controllers. This facilitates fast torque dynamics, which is critical as far as energy charging/discharging in the FESS is concerned. As FESS applications demand high-speed operation, a new field-weakening algorithm is proposed in this paper. Flux weakening is achieved autonomously once the y-axis voltage exceeds the available inverter voltage. An inherently speed sensorless stator flux observer immune to stator resistance variations and dc-offset effects is also proposed for accurate flux and speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a machine prototype.

Modeling and position-sensorless control of a dual-airgap axial flux permanent magnet machine for flywheel energy storage systems

This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.

Tristate

"This work forms part of a much larger collaborative album project in progress between Tim Bruniges, Julian Knowles and David Trumpmanis which explores the intersections between traditional rock instrumentation and analogue and digital media. All of the creative team are performers, composers and producers. The material for the album was thus generated by a series of in studio improvisations and performances with each collaborator assuming a range of different and alternating roles – guitars, electronics, drums, percussion, bass, keyboards production. Thematically the work explores the intersection of instrumental (post) rock, ambient music, and historical electro-acoustic tape composition traditions. Over the past 10 years, musical practice has become increasingly hybrid, with the traditional boundaries between genre becoming progressively eroded. At the same time, digital tools have replaced many of the major analogue technologies that dominated music production and performance in the 20th century. The disappearance of analogue media in mainstream musical practice has had a profound effect on the sonic characteristics of contemporary music and the gestural basis for its production. Despite the increasing power of digital technologies, a small but dedicated group of practitioners has continued to prize and use analogue technology for its unique sounds and the non-linearity of the media, aestheticising its inherent limitations and flaws. At the most radical end of this spectrum lie glitch and lo-fi musical forms, seen in part as reactions to the clinical nature of digital media and the perceived lack of character associated with its transparency. Such developments have also problematised the traditional relationships between media and genre, where specific techniques and their associated sounds have become genre markers. Tristate is an investigation into this emerging set of dialogues between analogue and digital media across composition, production and performance. It employs analogue tape loops in performance, where a tape machine ‘performer’ records and hand manipulates loops of an electric guitar performer on ‘destroyed’ tape stock (intentionally damaged tape), processing the output of this analogue system in the digital domain with contemporary sound processors. In doing so it investigates how the most extreme sonic signatures of analogue media – tape dropout and noise – can be employed alongside contemporary digital sound gestures in both compositional and performance contexts and how the extremes of the two media signatures can brought together both compositionally and performatively. In respect of genre, the work established strategies for merging compositional techniques from the early musique concrete tradition of the 1940s with late 60s popular music experimentalism and the laptop glitch electronica movement of the early 2000s. Lastly, the work explores how analogue recording studio technologies can be used as performance tools, thus illuminating and foregrounding the performative/gestural dimensions of traditional analogue studio tools in use."

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Survey : video forensic tools

Due to extension of using CCTVs and the other video security systems in all areas, these sorts of devices have been introduced as the most important digital evidences to search and seizure crimes. Video forensics tools are developed as a part of digital forensics tools to analyze digital evidences and clear vague points of them for presenting in the courts Existing video forensics tools have been facilitated the investigation process by providing different features based on various video editing techniques. In this paper, some of the most popular video forensics tools are discussed and the strengths and shortages of them are compared and consequently, an alternative framework which includes the strengths of existing popular tools is introduced.