A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

Soil–pile interaction of pile embedded in deep-layered marine sediment under seismic excitation

This paper investigates the soil–pile interaction of a pile embedded in a deep multi-layered soil under seismic excitation considering both kinematic and inertial interaction effects. A comprehensive three-dimensional finite element model is developed and validated using existing results in the literature. The response of the pile in the deep multi-layered soil profile is investigated with respect to pile head response, deflection modes and maximum deflections along the pile. Results show that the pile exhibits complex deflection patterns and that the pile response is influenced by the properties of both the soil profile and the seismic excitation. It is also evident that kinematic interaction effects have a greater influence on the pile response than the inertial interaction effects.

Automated melanoma diagnosis system

An automated melanoma diagnosis system, the so-called Skin Polar-probe, was developed to improve the chances of early detection of skin cancers and help save the lives of melanoma victims. The system will offer unique benefits to aid early detection of melanoma - the key to reducing deaths caused by this cancer.

Probabilistic power system contingency analysis considering wind

This thesis was a step forward in developing probabilistic assessment of power system response to faults subject to intermittent generation by renewable energy. It has investigated the wind power fluctuation effect on power system stability, and the developed fast estimation process has demonstrated the feasibility for real-time implementation. A better balance between power network security and efficiency can be achieved based on this research outcome.

Smart coordinated distribution system control to enable high level penetration of rooftop PVs

This thesis addresses voltage violation problem, the most critical issue associated with high level penetration of photovoltaic (PV) in electricity distribution network. A coordinated control algorithm using the reactive power from PV inverter and integrated battery energy storage has been developed and investigated in different network scenarios in the thesis. Probable variations associated with solar generation, end-user participation and network parameters are also considered. Furthermore, a unified data model and well-defined communication protocol to ensure the smooth coordination between all the components during the operation of the algorithm is described. Finally this thesis incorporated the uncertainties of solar generation using probabilistic load flow analysis.

A Likelihood-Maximizing Framework for Enhanced In-Car Speech Recognition Based on Speech Dialog System Interaction

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating noncritical in-car systems. Under such conditions, however, speech recognition accuracy degrades significantly, and techniques such as speech enhancement are required to improve these accuracies. Likelihood-maximizing (LIMA) frameworks optimize speech enhancement algorithms based on recognized state sequences rather than traditional signal-level criteria such as maximizing signal-to-noise ratio. LIMA frameworks typically require calibration utterances to generate optimized enhancement parameters that are used for all subsequent utterances. Under such a scheme, suboptimal recognition performance occurs in noise conditions that are significantly different from that present during the calibration session – a serious problem in rapidly changing noise environments out on the open road. In this chapter, we propose a dialog-based design that allows regular optimization iterations in order to track the ever-changing noise conditions. Experiments using Mel-filterbank noise subtraction (MFNS) are performed to determine the optimization requirements for vehicular environments and show that minimal optimization is required to improve speech recognition, avoid over-optimization, and ultimately assist with semireal-time operation. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session only.

Impact of PV units in LV feeders on distribution system reliability

Re-supplying loads on outage through cross-connect from adjacent feeders in a distribution system may cause voltage drop and hence require load shedding. However, the surplus PV generated in some of the LV feeders can prevent load shedding, and improve reliability. In order to measure these effects, this paper proposes the application of Direct Load Flow method[1] in reliability evaluation of distribution systems with PV units. As part of this study, seasonal impacts on load consumption together with surplus PV output power injection to higher voltage networks are also considered. New indices are proposed to measure yearly expected energy export, from LV to MV and from MV to higher voltage network.