Measuring the impact of information systems in Malaysia

Information Systems researchers have employed a diversity of sometimes inconsistent measures of IS success, seldom explicating the rationale, thereby complicating the choice for future researchers. In response to these and other issues, Gable, Sedera and Chan introduced the IS-Impact measurement model. This model represents “the stream of net benefits from the Information System (IS), to date and anticipated, as perceived by all key-user-groups”. Although the IS-Impact model was rigorously validated in previous research, there is a need to further generalise and validate it in different context. This paper reported the findings of the IS-Impact model revalidation study at four state governments in Malaysia with 232 users of a financial system that is currently being used at eleven state governments in Malaysia. Data was analysed following the guidelines for formative measurement validation using SmartPLS. Based on the PLS results, data supported the IS-Impact dimensions and measures thus confirming the validity of the IS-Impact model in Malaysia. This indicates that the IS-Impact model is robust and can be used across different context.

Exposure of pipe insulation to high temperatures from domestic pumped storage (split) solar water heating systems in Australia and New Zealand

Pipe insulation between the collector and storage tank on pumped storage (commonly called split), solar water heaters can be subject to high temperatures, with a maximum equal to the collector stagnation temperature. The frequency of occurrence of these temperatures is dependent on many factors including climate, hot water demand, system size and efficiency. This paper outlines the findings of a computer modelling study to quantify the frequency of occurrence of pipe temperatures of 80 degrees Celsius or greater at the outlet of the collectors for these systems. This study will help insulation suppliers determine the suitability of their materials for this application. The TRNSYS program was used to model the performance of a common size of domestic split solar system, using both flat plate and evacuated tube, selective surface collectors. Each system was modelled at a representative city in each of the 6 climate zones for Australia and New Zealand, according to AS/NZS4234 - Heat Water Systems - Calculation of energy consumption, and the ORER RECs calculation method. TRNSYS was used to predict the frequency of occurrence of the temperatures that the pipe insulation would be exposed to over an average year, for hot water consumption patterns specified in AS/NZS4234, and for worst case conditions in each of the climate zones. The results show; * For selectively surfaced, flat plate collectors in the hottest location (Alice Sprints) with a medium size hot water demand according to AS/NZS2434, the annual frequency of occurrence of temperatures at and above 80 degrees Celsius was 33 hours. The frequency of temperatures at and above 140 degrees Celsius was insignificant. * For evacuated tube collectors in the hottest location (Alice Springs), the annual frequency of temperatures at and above 80 degrees Celsius was 50 hours. Temperatures at and above 140 degrees Celsius were significant and were estimated to occur for more than 21 hours per year in this climate zone. Even in Melbourne, temperatures at and above 80 degrees can occur for 12 hours per year and at and above 140 degrees for 5 hours per year. * The worst case identified was for evacuated tube collectors in Alice Springs, with mostly afternoon loads in January. Under these conditions, the frequency of temperatures at and above 80 degrees Celsius was 10 hours for this month only. Temperatures at and above 140 degrees Celsius were predicted to occur for 5 hours in January.

Energy and cost savings from pipe insulation on domestic, pumped storage (split), solar water heating systems in Australia and New Zealand

Appropriate pipe insulation on domestic, pumped storage (split), solar water heating systems forms an integral part of energy conservation measures of well engineered systems. However, its importance over the life of the system is often overlooked. This study outlines the findings of computer modelling to quantify the energy and cost savings by using pipe insulation between the collector and storage tank. System sizes of 270 Litre storage tank, together with either selectively surfaced, flat plate collectors (4m2 area), or 30 evacuated tube collectors, were used. Insulation thicknesses of 13mm and 15mm, pipe runs both ways of 10, 15 and 20 metres and both electric and gas boosting of systems were all considered. The TRNSYS program was used to model the system performance at a representative city in each of the 6 climate zones for Australia and New Zealand, according to AS/NZS4234 – Heat Water Systems – Calculation of energy consumption and the ORER RECs calculation method. The results show:  Energy savings from pipe insulation are very significant, even in mild climates such as Rockhampton. Across all climates zones, savings ranged from 0.16 to 3.5GJ per system per year, or about 2 to 23 percent of the annual load.  There is very little advantage in increasing the insulation thickness from 13 to 15mm. For electricity at 19c/kWh and gas at 2 c/MJ, cost savings of between $27 and $100 per year are achieved across the climate zones. Both energy and cost savings would increase in colder climates with increased system size, solar contribution and water temperatures.  The pipe insulation substantially improves the solar contribution (or fraction) and Renewable Energy Certificates (RECs), as well as giving small savings in circulating pump running costs in milder climates. Solar contribution increased by up to 23 percent points and RECs by over 7 in some cases.  The study highlights the need to install and maintain the integrity of appropriate pipe insulation on solar water heaters over their life time in Australia and New Zealand.

Interpreting regional frequencies from synchronized phasor measurement in multi-area power systems

In this paper a new approach is proposed for interpreting of regional frequencies in multi machine power systems. The method uses generator aggregation and system reduction based on coherent generators in each area. The reduced system structure is able to be identified and a kalman estimator is designed for the reduced system to estimate the inter-area modes using the synchronized phasor measurement data. The proposed method is tested on a six machine, three area test system and the obtained results show the estimation of inter-area oscillations in the system with a high accuracy.

Improving accuracy and intelligibility of decisions

Intelligible and accurate risk-based decision-making requires a complex balance of information from different sources, appropriate statistical analysis of this information and consequent intelligent inference and decisions made on the basis of these analyses. Importantly, this requires an explicit acknowledgement of uncertainty in the inputs and outputs of the statistical model. The aim of this paper is to progress a discussion of these issues in the context of several motivating problems related to the wider scope of agricultural production. These problems include biosecurity surveillance design, pest incursion, environmental monitoring and import risk assessment. The information to be integrated includes observational and experimental data, remotely sensed data and expert information. We describe our efforts in addressing these problems using Bayesian models and Bayesian networks. These approaches provide a coherent and transparent framework for modelling complex systems, combining the different information sources, and allowing for uncertainty in inputs and outputs. While the theory underlying Bayesian modelling has a long and well established history, its application is only now becoming more possible for complex problems, due to increased availability of methodological and computational tools. Of course, there are still hurdles and constraints, which we also address through sharing our endeavours and experiences.

Can the contra-lateral limb be used as a control with respect to analyses of bone remodelling?

Bone loss may result from remodelling initiated by implant stress protection. Quantifying remodelling requires bone density distributions which can be obtained from computed tomography scans. Pre-operative scans of large animals however are rarely possible. This study aimed to determine if the contra-lateral bone is a suitable control for the purpose of quantifying bone remodelling. CT scans of 8 pairs of ovine tibia were used to determine the likeness of left and right bones. The deviation between the outer surfaces of the bone pairs was used to quantify geometric similarity. The density differences were determined by dividing the bones into discrete volumes along the shaft of the tibia. Density differences were also determined for fractured and contra-lateral bone pairs to determine the magnitude of implant related remodelling. Left and right ovine tibiae were found to have a high degree of similarity with differences of less than 1.0 mm in the outer surface deviation and density difference of less than 5% in over 90% of the shaft region. The density differences (10–40%) as a result of implant related bone remodelling were greater than left-right differences. Therefore, for the purpose of quantifying bone remodelling in sheep, the contra-lateral tibia may be considered an alternative to a pre-operative control.

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.

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.

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 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.