Alex Stojcevski

Developing a conceptual model for the effective assessment of individual student learning in team-based subjects *

Assessment of student learning in team-based subjects can be challenging, as the inherent complexity in this learning environment can create ambiguity for academic staff and students alike. This five-institution research project gathered data from academic staff and students about their experiences with assessment in team-based settings, data which served as a support for the development of a conceptual model for effective assessment of individual student learning in this highly collaborative setting. This paper reports on the research design of this on-going project, the conceptual assessment model, and the future directions for the project itself.

Implementation of a colorimetric algorithm for portable blood gas analysis

This paper presents the implementation results of a colorimetric algorithm for portable blood gas analysis. The need for diagnostic data to be available from the patients living environment has given rise to the need for portable diagnostic equipment. Blood gas analysis is an important diagnostic tool which provides the medical practitioner with insights in to the health of the respiratory system and kidneys. Present day analysers are big, bulky, and expensive and run on mains supply. The paper discusses the implementation of an algorithm based on the change of colour of an indicator with respect to the change in pH. The algorithm maps the change in colour to a mathematical equation. The equation is implemented using floating point arithmetic architectures. The paper presents the implementation of the algorithm using the Altera Stratix FPGA and 0.35µm CMOS process. The design is aimed low power consumption and size. The design met the timing constraints for an operating speed of 50 MHz and consumes 2.78W of power.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Intelligent frequency regulation in the wind integrated control area

Abstract Reduction in system inertia due to the higher levels of wind plants in a power system inherently necessitates the frequency regulation investigations and motivates new opportunities for the improvement of active power control techniques. This paper focus on the load-frequency regulation analysis of a control area in the presence of a proposed wind energy technology. The proposed variable speed wind turbine generator model incorporates a dynamic dead-band and moving average frequency filter-based algorithm to provide grid-code compatible power-frequency response. The proposed wind plant model provides better frequency response and increased electrical power generation in comparison to the other frequency responsive wind turbine generator models. This paper also highlights the superiority of gain scheduling concept in fuzzy logic controllers as an improved alternative to various other load frequency controllers under the stochastic conditions in a wind integrated control area.

The future of engineering design education – an Australasian perspective and solution

This paper describes a unique initiative towards addressing the engineering skills shortage presently being experienced in Australia that has significance in the global engineering education community since it provides a near radical approach to student centred learning. The Centre for Advanced Design in Engineering Training (CADET) at Deakin University Geelong Waurn Ponds is a teaching and learning facility providing student-centred learning based broadly around a design-based learning pedagogy.