Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

Teachers reporting suspected child sexual abuse : results of a three-state study

Over 3000 cases of child sexual abuse are identified every year in Australia, but the real incidence is higher still. As a strategy to identify child sexual abuse, Australian States and Territories have enacted legislation requiring members of selected professions, including teachers, to report suspected cases. In addition, policy-based reporting obligations have been developed by professions, including the teaching profession. These legislative and industry-based developments have occurred in a context of growing awareness of the incidence and consequences of child sexual abuse. Teachers have frequent contact and close relationships with children, and possess expertise in monitoring changes in children’s behaviour. Accordingly, teachers are seen as being well-placed to detect and report suspected child sexual abuse. To date, however, there has been little empirical research into the operation of these reporting duties. The extent of teachers’ awareness of their duties to report child sexual abuse is unknown. Further, there is little evidence about teachers’ past reporting practice. Teachers’ duties to report sexual abuse, especially those in legislation, differ between States, and it is not known whether or how these differences affect reporting practice. This article presents results from the first large-scale Australian survey of teachers in three States with different reporting laws: New South Wales, Queensland, and Western Australia. The results indicate levels of teacher knowledge of reporting duties, reveal evidence about past reporting practice, and provide insights into anticipated future reporting practice and legal compliance. The findings have implications for reform of legislation and policy, training of teachers about the reporting of child sexual abuse, and enhancement of child protection.

The true cost and performance of individual residential property investment and the implication on real estate agency practice

Investment in residential property in Australia is not dominated by the major investment institutions in to the same degree as the commercial, industrial and retail property markets. As at December 2001, the Property Council of Australia Investment Performance Index contained residential property with a total value of $235 million, which represents only 0.3% of the total PCA Performance Index value. The majority of investment in the Australian residential property market is by small investment companies and individual investors. The limited exposure of residential property in the institutional investment portfolios has also limited the research that has been undertaken in relation to residential property performance. However the importance of individual investment in residential property is continuing to gain importance as both individuals are now taking control of their own superannuation portfolios and the various State Governments of Australia are decreasing their involvement in the construction of public housing by subsidizing low-income families into the private residential property market. This paper will: • Provide a comparison of the cost to initially purchase residential property in the various capital city residential property markets in Australia, and • Analyse the true cost and investment performance of residential property in the main residential property markets in Australia based on a standard investment portfolio in each of the State capital cities and relate these results to real estate marketing and agency practice.

Accelerated computation of cyclic steady state for simulated-moving-bed processes

Industrial applications of the simulated-moving-bed (SMB) chromatographic technology have brought an emergent demand to improve the SMB process operation for higher efficiency and better robustness. Improved process modelling and more-efficient model computation will pave a path to meet this demand. However, the SMB unit operation exhibits complex dynamics, leading to challenges in SMB process modelling and model computation. One of the significant problems is how to quickly obtain the steady state of an SMB process model, as process metrics at the steady state are critical for process design and real-time control. The conventional computation method, which solves the process model cycle by cycle and takes the solution only when a cyclic steady state is reached after a certain number of switching, is computationally expensive. Adopting the concept of quasi-envelope (QE), this work treats the SMB operation as a pseudo-oscillatory process because of its large number of continuous switching. Then, an innovative QE computation scheme is developed to quickly obtain the steady state solution of an SMB model for any arbitrary initial condition. The QE computation scheme allows larger steps to be taken for predicting the slow change of the starting state within each switching. Incorporating with the wavelet-based technique, this scheme is demonstrated to be effective and efficient for an SMB sugar separation process. Moreover, investigations are also carried out on when the computation scheme should be activated and how the convergence of the scheme is affected by a variable stepsize.

Robust real time multi-layer foreground segmentation

Many surveillance applications (object tracking, abandoned object detection) rely on detecting changes in a scene. Foreground segmentation is an effective way to extract the foreground from the scene, but these techniques cannot discriminate between objects that have temporarily stopped and those that are moving. We propose a series of modifications to an existing foreground segmentation system\cite{Butler2003} so that the foreground is further segmented into two or more layers. This yields an active layer of objects currently in motion and a passive layer of objects that have temporarily ceased motion which can itself be decomposed into multiple static layers. We also propose a variable threshold to cope with variable illumination, a feedback mechanism that allows an external process (i.e. surveillance system) to alter the motion detectors state, and a lighting compensation process and a shadow detector to reduce errors caused by lighting inconsistencies. The technique is demonstrated using outdoor surveillance footage, and is shown to be able to effectively deal with real world lighting conditions and overlapping objects.

Small and medium enterprises’ solution to improve on-site waste management in office building retrofit projects

Office building retrofit projects are increasingly more intensified as existing buildings are aging. At the same time, building owners and occupants are looking for environmentally sustainable products. These retrofit projects usually take place in center business district (CBDs) with on-site waste becoming one of the critical issues. Small and Medium Enterprises (SMEs) carry out most of the work in retrofit projects as subcontractors. Despite their large involvement, they often do not have adequate resources to deal with the specific technical challenges and project risks related to waste. Few research has been done on their performance of waste management operations. This paper identifies characteristics of on-site waste in office building retrofit projects. It examines the specific requirements for contractors to manage waste in the projects before exploring the existing performance of SMEs. By comparing requirements for SMEs and their potential areas for improvement, a framework is established for performance promotion of SMEs in on-site waste management of office building retrofit projects. The paper will raise the consciousness and commitment of SMEs as sub-contractors to waste management. It also explores ways of supporting SMEs for experience accumulation, performance promotion and project culture establishment towards effective and efficient on-site waste management in the growing sector of office building retrofit and upgrade.

The embodied hybrid space : designing ubiquitous computing towards an amplification of situated real world experiences

The emergence of mobile and ubiquitous computing technology has created what is often referred to as the hybrid space – a virtual layer of digital information and interaction opportunities that sit on top of and augment the physical environment. Embodied media materialise digital information as observable and sometimes interactive parts of the physical environment. The aim of this work is to explore ways to enhance people’s situated real world experience, and to find out what the role and impact of embodied media in achieving this goal can be. The Edge, an initiative of the State Library of Queensland in Brisbane, Australia, and case study of this thesis, envisions to be a physical place for people to meet, explore, experience, learn and teach each other creative practices in various areas related to digital technology and arts. Guided by an Action Research approach, this work applies Lefebvre’s triad of space (1991) to investigate the Edge as a social space from a conceived, perceived and lived point of view. Based on its creators’ vision and goals on the conceived level, different embodied media are iteratively designed, implemented and evaluated towards shaping and amplifying the Edge’s visitor experience on the perceived and lived level.

What are the real costs of major trauma?

Background This economic evaluation reports the results of a detailed study of the cost of major trauma treated at Princess Alexandra Hospital (PAH), Australia. Methods A bottom-up approach was used to collect and aggregate the direct and indirect costs generated by a sample of 30 inpatients treated for major trauma at PAH in 2004. Major trauma was defined as an admission for Multiple Significant Trauma with an Injury Severity Score >15. Direct and indirect costs were amalgamated from three sources, (1) PAH inpatient costs, (2) Medicare Australia, and (3) a survey instrument. Inpatient costs included the initial episode of inpatient care including clinical and outpatient services and any subsequent representations for ongoing-related medical treatment. Medicare Australia provided an itemized list of pharmaceutical and ambulatory goods and services. The survey instrument collected out-of-pocket expenses and opportunity cost of employment forgone. Inpatient data obtained from a publically funded trauma registry were used to control for any potential bias in our sample. Costs are reported in Australian dollars for 2004 and 2008. Results The average direct and indirect costs of major trauma incurred up to 1-year postdischarge were estimated to be A$78,577 and A$24,273, respectively. The aggregate costs, for the State of Queensland, were estimated to range from A$86.1 million to $106.4 million in 2004 and from A$135 million to A$166.4 million in 2008. Conclusion These results demonstrate that (1) the costs of major trauma are significantly higher than previously reported estimates and (2) the cost of readmissions increased inpatient costs by 38.1%.

Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

A methodology to assess the competitiveness of real estate developers in China

Real estate, or property development, is considered one of the pillar industries of the Chinese economy. As a result of the opening up of the economy as well as the "macro-control" policy of the Central Chinese Government to moderate the frenetic pace of growth of the economy, the real estate industry has faced fierce competition and ongoing change. Real estate firms in China must improve their competitiveness in order to maintain market share or even survive in this brutally competitive environment. This study developed a methodology to evaluate the competitiveness of real estate developers in the China and then used a case study to illustrate the effectiveness of the evaluation method. Four steps were taken to achieve this. The first step was to conduct a thorough literature review which included a review of the characteristics of real estate industry, theories about competitiveness and the competitive characteristics of real estate developers. Following this literature review, the competitive model was developed based on seven key competitive factors (the 'level 1') identified in the literature. They include: (1) financial competency; (2) market share; (3) management competency; (4) social responsibility; (5) organisational competency; (6) technological capabilities; and, (7) regional competitiveness. In the next step of research, the competitive evaluation criteria (the 'level 2') under each of competitive factors (the 'level 1') were evaluated. Additionally, there were identified a set of competitive attributes (the 'level 3') under each competitive criteria (the 'level 2'). These attributes were initially recognised during the literature review and then expanded upon through interviews with multidisciplinary experts and practitioners in various real estate-related industries. The final step in this research was to undertake a case study using the proposed evaluation method and attributes. Through the study of an actual real estate development company, the procedures and effectiveness of the evaluation method were illustrated and validated. Through the above steps, this research investigates and develops an analytical system for determining the corporate competitiveness of real estate developers in China. The analytical system is formulated to evaluate the "state of health" of the business from different competitive perspectives. The result of empirical study illustrates that a systematic and structured evaluation can effectively assist developers in identifying their strengths and highlighting potential problems. This is very important for the development of an overall corporate strategy and supporting key strategic decisions. This study also provides some insights, analysis and suggestions for improving the competitiveness of real estate developers in China from different perspectives, including: management competency, organisational competency, technological capabilities, financial competency, market share, social responsibility and regional competitiveness. In the case study, problems were found in each of these areas, and they appear to be common in the industry. To address these problems and improve the competitiveness and effectiveness of Chinese real estate developers, a variety of suggestions are proposed. The findings of this research provide an insight into the factors that influence competitiveness in the Chinese real estate industry while also assisting practitioners to formulate strategies to improve their competitiveness. References for studying the competitiveness of real estate developers in other countries are also provided.

Assessing the real value in residential property investment

One of the major fall outs from the Global Financial Crisis has been the decline in residential property construction, home lending and residential property prices. This has lead to some extent to a reduction in the number of small investors willing to commit funds to an investment market that is not seen to perform as well as other investment assets, particularly in relation to income return.With a decreasing supply of rental accommodation in the housing markets, less public housing being constructed by both State and Commonwealth Governments, there is the potential for the residential property market to provide more substantial returns than previous years.This paper will analyse the current residential housing market in Brisbane, Australia to determine if there are sectors in this market that are outperforming the average income and total return for residential investment property and the variation in investment performance across the various housing sub-markets. The results show that property investment in residential property provides opportunities to maximize returns based on geographic location and socio-economic economic status, with lower value areas showing the highest income returns and higher value suburbs showing greater capital returns