DefectNET: multi-class fault detection on highly-imbalanced datasets

As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias toward larger classes, which tend to be the background in the semantic segmentation task. This becomes a major problem for fault detection, where the targets appear very small on the images and vary in both types and sizes. In this paper we propose a new network architecture, DefectNet, that offers multi-class (including but not limited to) defect detection on highly-imbalanced datasets. DefectNet consists of two parallel paths, which are a fully convolutional network and a dilated convolutional network to detect large and small objects respectively. We propose a hybrid loss maximising the usefulness of a dice loss and a cross entropy loss, and we also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence of some targets in training batches. The prediction results show that our DefectNet outperforms state-of-the-art networks for detecting multi-class defects with the average accuracy improvement of approximately 10% on a wind turbine

Growing Pains: Getting past the complexities of scaling social impact

In communities across the UK, organisations develop new ideas to improve the lives of those around them. And yet despite growing demand for charity services, concerted attempts to take proven approaches to scale are few and far between, and successful examples are rarer still. This paper aims to bring about a change in tack by proposing a way of assessing the viability of scaling in different contexts

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Playing catch-up: investigating public and institutional policies for OER practices in Australia

This article explores many of the most well-known Open Educational Resource (OER) initiatives worldwide and then reports on OER developments in Australia. It also discusses a current research project funded by the Australian Learning and Teaching Council (ALTC), including its design and methods of data collection and analysis. Although the study reported here is ongoing, a survey of the tertiary sector to establish current 'state of play' of OERs in Australia has been completed. The authors examine a preliminary analysis that focuses mostly on OER policies at governmental and institutional levels. The analysis shows that the OER movement remains relatively immature in Australia. Also, according to the survey's participants, the government and educational institutions need to give much greater consideration to a regulatory framework in which the use of OER and Open Educational Practices (OEP) can be fostered and encouraged. Isolated OER activities exist, but there appears to be a great deal of catching up required if Australia is to have coordinated initiatives to foster innovation and a culture of more OEPs