An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

How does access to development finance shape our cities?

In market economies the built environment is largely the product of private sector property development. Property development is a high-risk entrepreneurial activity executing expensive projects with long gestation periods in an uncertain environment and into an uncertain future. Risk lies at the core of development: the developer manages the multiple risks of development and it is the capital injection and financing that is placed at risk. From the developer's perspective the search for development capital is a quest: to access more finance, over a longer term, with fewer conditions and at lower rates. From the supply angle, capital of various sources - banks, insurance companies, superannuation funds, accumulated firm profits, retail investors and private equity - is always seeking above market returns for limited risk. Property development presents one potentially lucrative, but risky, investment opportunity. Competition for returns on capital produces a continual dynamic evolution of methods for funding property developments. And thus the relationship between capital and development and the outcomes for the built environment are in a restless continual evolution. Little is documented about the ways development is financed in Australia and even less of the consequences for cities. Using publicly available data sources and examples of different development financing from Australian practice, this paper argues that different methods of financing development have different outcomes and consequences for the built environment. This paper also presents an agenda for further research into these themes.

Co-movements in commodity prices : a note based on network analysis

This article analyses co-movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co-movement of the commodity prices in our time sample, however from mid-2008 to the end of 2009 co-movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co-movement during the sampled period.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

Resolving the conflicts of sustainable world heritage landscapes in cities : fully open or limited access for visitors?

World Heritage Landscapes (WHLs) are receiving increased attention from researchers, urban planners, managers, and policy makers and many heritage values and resources are becoming irreversibly lost. This phenomenon is especially prominent for WHLs located in cities, where greater development opportunities are involved. Decision making for sustainable urban landscape planning, conservation and management of WHLs often takes place from an economic perspective, especially in developing countries. This, together with the uncertain source of funding to cover WHL operating and maintenance costs, has resulted in many urban managers seeking private sector funding either in the form of visitor access fees or leasing part of the site for high-rental facilities such as five star hotels, clubs and expensive restaurants. For the former, this can result in low-income urban citizens being unable to afford the access fees and hence contradicting the principle of equal access for all; while, for the latter, the principle of open access for all is equally violated. To resolve this conflict, a game model is developed to determine how urban managers should allocate WHL spaces to maximize the combination of economic, social and ecological benefits and cultural values. A case study is provided of the Hangzhou's West Lake Scenic Area, a WHL located at the centre of Hangzhou city, in which several high-rental facilities have recently been closed down by the local authorities due to charges of elitism and misuse of public funds by government officials. The result shows that the best solution is to lease a small space with high rents and leave the remainder of the site to the public. This solution is likely to be applicable only in cities with a strong economy.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

Better health outcomes for people with physical disabilities in China through audits of mobility related access : a research and implementation need

China has 85 million people with disabilities, 30% of whom have a physical disability(1). Up to 2006, overall disability rates increased by 0.5% per year, more for males and in rural areas, and rates of physical disability increased by 11.2% per year(2). With population ageing the proportion of people with disability will increase even faster. In May 2014 the 67th World Health Assembly adopted a resolution endorsing the WHO Global Disability Action Plan 2014–2021. One of its three objectives is “to remove barriers and improve access to health services and programmes”. Access to transport contributes to positive health outcomes both directly and indirectly (e.g. access to economic opportunities, which is associated with better health)(3). However, once people with physical disabilities leave their dwellings they are confronted with physical barriers to their mobility, ranging from the condition/provision of paths to the cost/availability of transport and access to buildings. In addition, their mobility restrictions increase their vulnerability as road users, exposing them to a higher risk of injury through road crashes. QUT's School of Public Health and Social Work (PHSW) and and Centre for Accident Research and Road Safety-Queensland (CARRS-Q) CARRS-Q have been collaborating on development of a combined disability audit and road safety access tool that can identify transport barriers and safety issues along the routes taken by people with disabilities, to enable prioritisation of actions to address these issues. There are also spin-off benefits for other road users from addressing the rising toll of disability through road crashes in China(4). The tool has undergone initial proof-of-concept testing in India and Viet Nam, and is currently being assessed in Cambodia and Laos. Given the rapid development of China, increases in rates of physical disability and the impacts of an ageing population, it is proposed to establish collaborative research through the Australia-China Centre for Public Health to (1) tailor the combined road safety audit and disability access tool for use in China; (2) evaluate its use on a sample of routes; (3) develop plans for changes to the routes in consultation with local authorities; (4) evaluate the effectiveness of implemented changes in terms of access and health. 1. Zheng, Q, et al, 2014. Health and Quality of Life Outcomes, 12:25. 2. Zheng, X, et al, 2011. Bull World Health Org, 89:788–797. 3. Götschi, T & Kahlmeier, S, 2011. Integrated Transport, Health, and Sustainability Assessment (INTHESA): Final Report. Institute of Social and Preventive Medicine, University of Zurich. 4. Lin, T, et al, 2013. J Public Health, 35:541–547.