Motivating domestic energy conservation through comparative feedback in mobile applications and social networking sites

The progress of technology has led to the increased adoption of energy monitors among household energy consumers. While the monitors available on the market deliver real-time energy usage feedback to the consumer, the form of this data is usually unengaging and mundane. Moreover, it fails to address consumers with different motivations and needs to save and compare energy. This master‟s thesis project presents a study that seeks to inform design guidelines for differently motivated energy consumers. The focus of the research is on comparative feedback supported by a community of energy consumers. In particular, the discussed comparative feedback types are explanatory comparison, temporal self-comparison, norm comparison, one-on-one comparison and ranking, whereby the last three support exploring the potential of socialising energy-related feedback in social networking sites, such as Facebook. These feedback types were integrated in EnergyWiz – a mobile application that enables users to compare with their past performance, neighbours, contacts from social networking sites and other EnergyWiz users. The application was developed through a theory-driven approach and evaluated in personal, semi-structured interviews which provided insights on how motivation-related comparative feedback should be designed. It was also employed in expert focus group discussions which resulted in defining opportunities and challenges before mobile, social energy monitors. The findings have unequivocally shown that users with different motivations to compare and to conserve energy have different preferences for comparative feedback types and design. It was established that one of the most influential factors determining design factors is the people users compare to. In addition, the research found that even simple communication strategies in Facebook, such as wall posts and groups can contribute to engagement with energy conservation practices. The concept of mobility of the application was evaluated as positive since it provides place and time-independent access to the energy consumption data.

Traffic state estimation from partial Bluetooth and volume observations : case study in the Brisbane metropolitan area

The application of the Bluetooth (BT) technology to transportation has been enabling researchers to make accurate travel time observations, in freeway and arterial roads. The Bluetooth traffic data are generally incomplete, for they only relate to those vehicles that are equipped with Bluetooth devices, and that are detected by the Bluetooth sensors of the road network. The fraction of detected vehicles versus the total number of transiting vehicles is often referred to as Bluetooth Penetration Rate (BTPR). The aim of this study is to precisely define the spatio-temporal relationship between the quantities that become available through the partial, noisy BT observations; and the hidden variables that describe the actual dynamics of vehicular traffic. To do so, we propose to incorporate a multi- class traffic model into a Sequential Montecarlo Estimation algorithm. Our framework has been applied for the empirical travel time investigations into the Brisbane Metropolitan region.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.

A state-wide systems approach to embedding the learning and teaching of sustainability in teacher education

Education systems have a key role to play in preparing future citizens to engage in sustainable living practices and help create a more sustainable world. Many schools throughout Australia have begun to develop whole-school approaches to sustainability education that are supported by national and state policies and curriculum frameworks. Preservice teacher education, however, lags behind in building the capacity of new teachers to initiate and implement such approaches (ARIES, 2010). This proposed project seeks to develop a state-wide systems approach to embedding Education for Sustainability (EfS) in teacher education that is aligned with the Australian National Curriculum and the aspirations for EfS in the Melbourne Declaration and other national documents. Representatives from all teacher education institutions and other agents of change in the Queensland education system will be engaged in a multilevel systems approach, involving collaboration at the state, institutional and course levels, to develop curriculum practices that reflect a shared vision of EfS.

The Current State of BPM Education in Australia : Teaching and Research Challenges

As business processes, services and relationships, are now recognized as key organizational assets, the demand for the so-called boundaryspanning roles and process-aware professionals is continuing to grow. The world-wide demand for these roles will continue to increase, fueled by the unprecedented interest in Business Process Management (BPM) and the other emerging cross-functional disciplines. This, in turn, creates new opportunities, as well as some unforeseeable challenges for BPM education, both in university and industry. This paper reports on an analysis of the current BPM offerings of Australian universities. It presents a critical review of what is taught and how it is taught, and identifies a series of gaps and concerns. Explanations and recommendations are proposed and a call made for BPM educators worldwide, for urgent action.

Mining specific features for acquiring user information needs

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

State Estimation in Smart Power Grids

In this chapter, the role of State Estimation (SE) in smart power grids is presented. The trend of SE error with respect to the increasing of the smart grids implementation investigated. The observability analysis as a prior task of SE is demonstrated and an analytical method to consider the impedance values of the branches is developed and discussed by examples. Since most principles of smart power grids are appropriate to distribution networks, the Distribution SE (DSE)considering load correlation is argued and illustrated by an example. The main features of smart grid SE, which is here named as “Smart Distributed SE” (SDSE), are discussed. Some characteristics of proposed SDES are distributed, hybrid, multi-micro grid and islanding support, Harmonic State Estimation (HSE), observability analysis and restore, error processing, and network parameter estimation. Distribution HSE (DHSE) and meter placement for SDSE are also presented.

Distribution harmonic state estimation based on a modified PSO considering parameters uncertainty

This paper presents a new algorithm based on a Modified Particle Swarm Optimization (MPSO) to estimate the harmonic state variables in a distribution networks. The proposed algorithm performs the estimation for both amplitude and phase of each injection harmonic currents by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as the uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WTs). The main features of the proposed MPSO algorithm are usage of a primary and secondary PSO loop and applying the mutation function. The simulation results on 34-bus IEEE radial and a 70-bus realistic radial test networks are presented. The results demonstrate that the speed and the accuracy of the proposed Distribution Harmonic State Estimation (DHSE) algorithm are very excellent compared to the algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO, and Honey Bees Mating Optimization (HBMO).

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

Using pulse transit delay in Z-scan to discriminate between excited-state absorption and other nonlinear processes in ZnO nanocones

We report a new approach that uses the single beam Z-scan technique, to discriminate between excited state absorption (ESA) and two and three photon nonlinear absorption. By measuring the apparent delay or advance of the pulse in reaching the detector, the nonlinear absorption can be unambiguously identified as either instantaneous or transient. The simple method does not require a large range of input fluences or sophisticated pulse-probe experimental apparatus. The technique is easily extended to any absorption process dependent on pulse width and to nonlinear refraction measurements. We demonstrate in particular, that the large nonlinear absorption in ZnO nanocones when exposed to nanosecond 532 nm pulses, is due mostly to ESA, not pure two-photon absorption.

Stagnancy in Indonesia’s reformed state asset management policies and practices : a wicked problem?

The policies and regulations governing the practice of state asset management have emerged as an urgent question among many countries worldwide for there is heightened awareness of the complex and crucial role that state assets play in public service provision. Indonesia is an example of such country, introducing a ‘big-bang’ reform in state asset management laws, policies, regulations, and technical guidelines. Indonesia exemplified its enthusiasm in reforming state asset management policies and practices through the establishment of the Directorate General of State Assets in 2006. The Directorate General of State Assets have stressed the new direction that it is taking state asset management laws and policies through the introduction of Republic of Indonesia Law Number 38 Year 2008, which is an amended regulation overruling Republic of Indonesia Law Number 6 Year 2006 on Central/Regional Government State Asset Management. Law number 38/2008 aims to further exemplify good governance principles and puts forward a ‘the highest and best use of assets’ principle in state asset management. The purpose of this study is to explore and analyze specific contributing influences to state asset management practices, answering the question why innovative state asset management policy implementation is stagnant. The methodology of this study is that of qualitative case study approach, utilizing empirical data sample of four Indonesian regional governments. Through a thematic analytical approach this study provides an in-depth analysis of each influencing factors to state asset management reform. Such analysis suggests the potential of an ‘excuse rhetoric’; whereby the influencing factors identified are a smoke-screen, or are myths that public policy makers and implementers believe in, as a means to ex-plain stagnant implementation of innovative state asset management practice. Thus this study offers deeper insights of the intricate web that influences state as-set management innovative policies to state asset management policy makers; to be taken into consideration in future policy writing.