A crash avoidance framework for heavy equipment control systems using 3D imaging sensors

This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.

Local adjustment and global adaptation of control periods for QoC management of control systems

Linking real-time schedulability directly to the Quality of Control (QoC), the ultimate goal of a control system, a hierarchical feedback QoC management framework with the Fixed Priority (FP) and the Earliest-Deadline-First (EDF) policies as plug-ins is proposed in this paper for real-time control systems with multiple control tasks. It uses a task decomposition model for continuous QoC evaluation even in overload conditions, and then employs heuristic rules to adjust the period of each of the control tasks for QoC improvement. If the total requested workload exceeds the desired value, global adaptation of control periods is triggered for workload maintenance. A sufficient stability condition is derived for a class of control systems with delay and period switching of the heuristic rules. Examples are given to demonstrate the proposed approach.

In situ preparation and protein delivery of silicate/alginate composite microspheres with core-shell structure

It is predicted that with increased life expectancy in the developed world, there will be a greater demand for synthetic materials to repair or regenerate lost, injured or diseased bone (Hench & Thompson 2010). There are still few synthetic materials having true bone inductivity, which limits their application for bone regeneration, especially in large-size bone defects. To solve this problem, growth factors, such as bone morphogenetic proteins (BMPs), have been incorporated into synthetic materials in order to stimulate de novo bone formation in the center of large-size bone defects. The greatest obstacle with this approach is that the rapid diffusion of the protein from the carrier material, leading to a precipitous loss of bioactivity; the result is often insufficient local induction or failure of bone regeneration (Wei et al. 2007). It is critical that the protein is loaded in the carrier material in conditions which maintains its bioactivity (van de Manakker et al. 2009). For this reason, the efficient loading and controlled release of a protein from a synthetic material has remained a significant challenge. The use of microspheres as protein/drug carriers has received considerable attention in recent years (Lee et al. 2010; Pareta & Edirisinghe 2006; Wu & Zreiqat 2010). Compared to macroporous block scaffolds, the chief advantage of microspheres is their superior protein-delivery properties and ability to fill bone defects with irregular and complex shapes and sizes. Upon implantation, the microspheres are easily conformed to the irregular implant site, and the interstices between the particles provide space for both tissue and vascular ingrowth, which are important for effective and functional bone regeneration (Hsu et al. 1999). Alginates are natural polysaccharides and their production does not have the implicit risk of contamination with allo or xeno-proteins or viruses (Xie et al. 2010). Because alginate is generally cytocompatible, it has been used extensively in medicine, including cell therapy and tissue engineering applications (Tampieri et al. 2005; Xie et al. 2010; Xu et al. 2007). Calcium cross-linked alginate hydrogel is considered a promising material as a delivery matrix for drugs and proteins, since its gel microspheres form readily in aqueous solutions at room temperature, eliminating the need for harsh organic solvents, thereby maintaining the bioactivity of proteins in the process of loading into the microspheres (Jay & Saltzman 2009; Kikuchi et al. 1999). In addition, calcium cross-linked alginate hydrogel is degradable under physiological conditions (Kibat PG et al. 1990; Park K et al. 1993), which makes alginate stand out as an attractive candidate material for the protein carrier and bone regeneration (Hosoya et al. 2004; Matsuno et al. 2008; Turco et al. 2009). However, the major disadvantages of alginate microspheres is their low loading efficiency and also rapid release of proteins due to the mesh-like networks of the gel (Halder et al. 2005). Previous studies have shown that a core-shell structure in drug/protein carriers can overcome the issues of limited loading efficiencies and rapid release of drug or protein (Chang et al. 2010; Molvinger et al. 2004; Soppimath et al. 2007). We therefore hypothesized that introducing a core-shell structure into the alginate microspheres could solve the shortcomings of the pure alginate. Calcium silicate (CS) has been tested as a biodegradable biomaterial for bone tissue regeneration. CS is capable of inducing bone-like apatite formation in simulated body fluid (SBF) and its apatite-formation rate in SBF is faster than that of Bioglass® and A-W glass-ceramics (De Aza et al. 2000; Siriphannon et al. 2002). Titanium alloys plasma-spray coated with CS have excellent in vivo bioactivity (Xue et al. 2005) and porous CS scaffolds have enhanced in vivo bone formation ability compared to porous β-tricalcium phosphate ceramics (Xu et al. 2008). In light of the many advantages of this material, we decided to prepare CS/alginate composite microspheres by combining a CS shell with an alginate core to improve their protein delivery and mineralization for potential protein delivery and bone repair applications

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

Variable structure control for power systems stabilization

The application of variable structure control (VSC) for power systems stabilization is studied in this paper. It is the application, aspects and constraints of VSC which are of particular interest. A variable structure control methodology has been proposed for power systems stabilization. The method is implemented using thyristor controlled series compensators. A three machine power system is stabilized using a switching line control for large disturbances which becomes a sliding control as the disturbance becomes smaller. The results demonstrate the effectiveness of the methodology proposed as an useful tool to suppress the oscillations in power systems.

Comparison of scoring systems for invasive pests using ROC analysis and Monte Carlo simulation

Different international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which experts are asked to score several items using an ordinal scale. The scores are then combined using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful pests and identify ones that are not. As the quality of a pest risk assessment can depend on the characteristics of the scoring system used by the risk assessors (i.e., on the number of points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we proposed a new method for assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a case study where several scoring systems were compared. Data for this analysis were generated using a probabilistic model describing the pest introduction process. The generated data were then used to simulate the outcome of scoring systems and to assess the accuracy of the decisions about positive and negative introduction. The results showed that ordinal scales with at most 5 or 6 points were sufficient and that the multiplication-based scoring systems performed better than their sum-based counterparts. The proposed method could be used in the future to assess a great diversity of scoring systems.

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.

Stability boundary analysis of the dynamic voltage restorer in weak systems with dynamic loads

In this contribution, a stability analysis for a dynamic voltage restorer (DVR) connected to a weak ac system containing a dynamic load is presented using continuation techniques and bifurcation theory. The system dynamics are explored through the continuation of periodic solutions of the associated dynamic equations. The switching process in the DVR converter is taken into account to trace the stability regions through a suitable mathematical representation of the DVR converter. The stability regions in the Thevenin equivalent plane are computed. In addition, the stability regions in the control gains space, as well as the contour lines for different Floquet multipliers, are computed. Besides, the DVR converter model employed in this contribution avoids the necessity of developing very complicated iterative map approaches as in the conventional bifurcation analysis of converters. The continuation method and the DVR model can take into account dynamics and nonlinear loads and any network topology since the analysis is carried out directly from the state space equations. The bifurcation approach is shown to be both computationally efficient and robust, since it eliminates the need for numerically critical and long-lasting transient simulations.

Online environmental information systems

In today’s information society, electronic tools, such as computer networks for the rapid transfer of data and composite databases for information storage and management, are critical in ensuring effective environmental management. In particular environmental policies and programs for federal, state, and local governments need a large volume of up-to-date information on the quality of water, air, and soil in order to conserve and protect natural resources and to carry out meteorology. In line with this, the utilization of information and communication technologies (ICTs) is crucial to preserve and improve the quality of life. In handling tasks in the field of environmental protection a range of environmental and technical information is often required for a complex and mutual decision making in a multidisciplinary team environment. In this regard e-government provides a foundation of the transformative ICT initiative which can lead to better environmental governance, better services, and increased public participation in environmental decision- making process.

Online urban information systems

Countless factors affect the inner workings of a city, so in an attempt to gain an understanding of place and making sound decisions, planners need to utilize decision support systems (DSS) or planning support systems (PSS). PSS were originally developed as DSS in academia for experimental purposes, but like many other technologies, they became one of the most innovative technologies in parallel to rapid developments in software engineering as well as developments and advances in networks and hardware. Particularly, in the last decade, the awareness of PSS have been dramatically heightened with the increasing demand for a better, more reliable and furthermore a transparent decision-making process (Klosterman, Siebert, Hoque, Kim, & Parveen, 2003). Urban planning as an act has quite different perspective from the PSS point of view. The unique nature of planning requires that spatial dimension must be considered within the context of PSS. Additionally, the rapid changes in socio-economic structure cannot be easily monitored or controlled without an effective PSS.

Conceptualising use for information systems (IS) success

This thesis conceptualises Use for IS (Information Systems) success. While Use in this study describes the extent to which an IS is incorporated into the user’s processes or tasks, success of an IS is the measure of the degree to which the person using the system is better off. For IS success, the conceptualisation of Use offers new perspectives on describing and measuring Use. We test the philosophies of the conceptualisation using empirical evidence in an Enterprise Systems (ES) context. Results from the empirical analysis contribute insights to the existing body of knowledge on the role of Use and demonstrate Use as an important factor and measure of IS success. System Use is a central theme in IS research. For instance, Use is regarded as an important dimension of IS success. Despite its recognition, the Use dimension of IS success reportedly suffers from an all too simplistic definition, misconception, poor specification of its complex nature, and an inadequacy of measurement approaches (Bokhari 2005; DeLone and McLean 2003; Zigurs 1993). Given the above, Burton-Jones and Straub (2006) urge scholars to revisit the concept of system Use, consider a stronger theoretical treatment, and submit the construct to further validation in its intended nomological net. On those considerations, this study re-conceptualises Use for IS success. The new conceptualisation adopts a work-process system-centric lens and draws upon the characteristics of modern system types, key user groups and their information needs, and the incorporation of IS in work processes. With these characteristics, the definition of Use and how it may be measured is systematically established. Use is conceptualised as a second-order measurement construct determined by three sub-dimensions: attitude of its users, depth, and amount of Use. The construct is positioned in a modified IS success research model, in an attempt to demonstrate its central role in determining IS success in an ES setting. A two-stage mixed-methods research design—incorporating a sequential explanatory strategy—was adopted to collect empirical data and to test the research model. The first empirical investigation involved an experiment and a survey of ES end users at a leading tertiary education institute in Australia. The second, a qualitative investigation, involved a series of interviews with real-world operational managers in large Indian private-sector companies to canvass their day-to-day experiences with ES. The research strategy adopted has a stronger quantitative leaning. The survey analysis results demonstrate the aptness of Use as an antecedent and a consequence of IS success, and furthermore, as a mediator between the quality of IS and the impacts of IS on individuals. Qualitative data analysis on the other hand, is used to derive a framework for classifying the diversity of ES Use behaviour. The qualitative results establish that workers Use IS in their context to orientate, negotiate, or innovate. The implications are twofold. For research, this study contributes to cumulative IS success knowledge an approach for defining, contextualising, measuring, and validating Use. For practice, research findings not only provide insights for educators when incorporating ES for higher education, but also demonstrate how operational managers incorporate ES into their work practices. Research findings leave the way open for future, larger-scale research into how industry practitioners interact with an ES to complete their work in varied organisational environments.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.