University of Wollongong

About: University of Wollongong is a education organization based out in Wollongong, New South Wales, Australia. It is known for research contribution in the topics: Population & Graphene. The organization has 15674 authors who have published 46658 publications receiving 1197471 citations. The organization is also known as: UOW & Wollongong University.

Action Recognition From Depth Maps Using Deep Convolutional Neural Networks

Abstract: This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the 3-D points of the captured depth maps. This not only synthesizes more data, but also makes the trained ConvNets view-tolerant. Second, WHDMMs at several temporal scales are constructed to encode the spatiotemporal motion patterns of actions into 2-D spatial structures. The 2-D spatial structures are further enhanced for recognition by converting the WHDMMs into pseudocolor images. Finally, the three ConvNets are initialized with the models obtained from ImageNet and fine-tuned independently on the color-coded WHDMMs constructed in three orthogonal planes. The proposed algorithm was evaluated on the MSRAction3D, MSRAction3DExt, UTKinect-Action, and MSRDailyActivity3D datasets using cross-subject protocols. In addition, the method was evaluated on the large dataset constructed from the above datasets. The proposed method achieved 2–9% better results on most of the individual datasets. Furthermore, the proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions.

Mitigation of Rooftop Solar PV Impacts and Evening Peak Support by Managing Available Capacity of Distributed Energy Storage Systems

Abstract: A high penetration of rooftop solar photovoltaic (PV) resources into low-voltage (LV) distribution networks creates reverse power-flow and voltage-rise problems This generally occurs when the generation from PV resources substantially exceeds the load demand during high insolation period This paper has investigated the solar PV impacts and developed a mitigation strategy by an effective use of distributed energy storage systems integrated with solar PV units in LV networks The storage is used to consume surplus solar PV power locally during PV peak, and the stored energy is utilized in the evening for the peak-load support A charging/discharging control strategy is developed taking into account the current state of charge (SoC) of the storage and the intended length of charging/discharging period to effectively utilize the available capacity of the storage The proposed strategy can also mitigate the impact of sudden changes in PV output, due to unstable weather conditions, by putting the storage into a short-term discharge mode The charging rate is adjusted dynamically to recover the charge drained during the short-term discharge to ensure that the level of SoC is as close to the desired SoC as possible A comprehensive battery model is used to capture the realistic behavior of the distributed energy storage units in a distribution feeder The proposed PV impact mitigation strategy is tested on a practical distribution network in Australia and validated through simulations

Influence of initial severity of depression on effectiveness of low intensity interventions: meta-analysis of individual patient data

Abstract: Objective To assess how initial severity of depression affects the benefit derived from low intensity interventions for depression.

ANS binding reveals common features of cytotoxic amyloid species

Abstract: Oligomeric assemblies formed from a variety of disease-associated peptides and proteins have been strongly associated with toxicity in many neurodegenerative conditions, such as Alzheimer’s disease. The precise nature of the toxic agents, however, remains still to be established. We show that prefibrillar aggregates of E22G (arctic) variant of the Aβ1−42 peptide bind strongly to 1-anilinonaphthalene 8-sulfonate and that changes in this property correlate significantly with changes in its cytotoxicity. Moreover, we show that this phenomenon is common to other amyloid systems, such as wild-type Aβ1–42, the I59T variant of human lysozyme and an SH3 domain. These findings are consistent with a model in which the exposure of hydrophobic surfaces as a result of the aggregation of misfolded species is a crucial and common feature of these pathogenic species.

The effect of morphological modification on the electrochemical properties of SnO2 nanomaterials

Abstract: The electrochemical performances of 1D SnO2 nanomaterials, nanotubes, nanowires, and nanopowders, are compared to define the most favorable morphology when SnO2 nanomaterials are adopted as the electrode material for lithium-ion batteries. Changes in the morphology of SnO2 are closely related with its electrochemical performance. Some SnO2 nanomaterials feature not only an increased energy density but also enhanced Li+ transfer. The correlation between the morphological characteristics and the electrochemical properties of SnO2 nanomaterials is discussed. The interesting electrochemical results obtained here on SnO2 nanomaterials indicate the possibility of designing and fabricating attractive nanostructured materials for lithium-ion batteries.