Vlogging campus community stories

Student voice is a powerful signifier for sharing the institutional habitus of a campus. With our new Caboolture Campus Community Stories initiative, we place students in the role of vloggers (video bloggers) to capture and distribute the stories, activities and events of the QUT environment. These stories present visual narratives through the eyes of students about university experience, academic practice and the transition from High School to first year, all intending to promote a sense of community and belonging, normalize academic practices and build an inclusive institutional habitus. These stories are placed on community websites and digital signage around campus as resources for first year students and prospective students.

Monitoring charged particles in indoor air using a neutral cluster and air ion spectrometer

While there are sources of ions both outdoors and indoors, ventilation systems can introduce as well as remove ions from the air. As a result, indoor ion concentrations are not directly related to air exchange rates in buildings. In this study, we attempt to relate these quantities with the view of understanding how charged particles may be introduced into indoor spaces.

Articulation in property programs : an international multi-campus tertiary model

This paper will present program developers and institutional administrators with a program delivery model suitable for cross cultural international delivery developing students from industry through to master’s level tertiary qualifications. The model was designed to meet the needs of property professionals from an industry where technical qualifications are the norm and tertiary qualifications are emerging. A further need was to develop and deliver a program that enhanced the University’s current program profile in both the domestic and international arenas. Early identification of international educational partners, industry need and the ability to service the program were vital to the successful development of Master of Property program. The educational foundations of the program rest in educational partners, local tutorial support, international course management, cultural awareness of and in content, online communication fora, with a delivery focus on problem-based learning, self-directed study, teamwork and the development of a global understanding and awareness of the international property markets. In enrolling students from a diverse cultural background with technical qualifications and/or extensive work experience there are a number of educational barriers to be overcome for all students to successfully progress and complete the program. These barriers disappear when the following mechanisms are employed: individual student pathways, tutorial support by qualified peers, enculturation into tertiary practice, assessment tasks that recognise cultural norms and values, and finally that value is placed on the experiential knowledge, cultural practices and belief systems of the students.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Uninav : a context-aware mobile application for university campus maps

The smart phones we carry with us are becoming ubiquitous with everyday life and the sensing capabilities of these devices allow us to provide context-aware services. In this paper, we discuss the development of UniNav, a context-aware mobile application that delivers personalised campus maps for universities. The application utilises university students’ details to provide information and services that are relevant and important to them. It helps students to navigate within the campus and become familiar with their university environment quickly. A study was undertaken to evaluate the acceptability and usefulness of the campus map, as well as the impact on a users’ navigation efficiency by utilising the personal and environmental contexts. The result indicates the integration of personal and environmental contexts on digital maps can improve its usefulness and navigation efficiency.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health 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 which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Underground power cable environment on-line monitoring and analysis

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit

Monitoring smartphones for anomaly detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80 of the amount of monitored features.

‘Hot chicks’, ‘better parties’ or academic stuff : perceptions of a regional university campus using Repertory Grid Analysis and Importance-performance Analysis

In the increasingly competitive Australian tertiary education market, a consumer orientation is essential. This is particularly so for small regional campuses competing with larger universities in the state capitals. Campus management need to carefully monitor both the perceptions of prospective students within the catchment area, and the (dis)satisfaction levels of current students. This study reports the results of an exploratory investigation into the perceptions held of a regional campus, using two techniques that have arguably been underutilised in the education marketing literature. Repertory Grid Analysis, a technique developed almost fifty years ago, was used to identify attributes deemed salient to year 12 high school students at the time they were applying for university places. Importance-performance analysis (IPA), developed three decades ago, was then used to identify attributes that were determinant for a new cohort of first year undergraduate students. The paper concludes that group applications of Repertory Grid offer education market researchers a useful means for identifying attributes used by high school students to differentiate universities, and that IPA is a useful technique for guiding promotional decision making. In this case, the two techniques provided a quick, economical and effective snapshot of market perceptions, which can be used as a foundation for the development of an ongoing market research programme.

Real-time risk monitoring in business processes : a sensor-based approach

This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.

Performance characterization of VGCF/epoxy nanocomposite sensors under static load cycles and in static structural health monitoring

Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.