491 resultados para Process model


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Our objective was to determine the factors that lead users to continue working with process modeling grammars after their initial adoption. We examined the explanatory power of three theoretical models of IT usage by applying them to two popular process modeling grammars. We found that a hybrid model of technology acceptance and expectation-confirmation best explained user intentions to continue using the grammars. We examined differences in the model results, and used them to provide three contributions. First, the study confirmed the applicability of IT usage models to the domain of process modeling. Second, we discovered that differences in continued usage intentions depended on the grammar type instead of the user characteristics. Third, we suggest implications and practice.

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This paper takes Kent and Taylor’s (2002) call to develop a dialogic theory of public relations and suggests that a necessary first step is the modelling of the process of dialogic communication in public relations. In order to achieve this, extant literature from a range of fields is reviewed, seeking to develop a definition of dialogic communication that is meaningful to the practice of contemporary public relations. A simple transmission model of communication is used as a starting point. This is synthesised with concepts relating specifically to dialogue, taken here in its broadest sense rather than defined as any one particular outcome. The definition that emerges from this review leads to the conclusion that dialogic communication in public relations involves the interaction of three roles – those of sender, receiver, and responder. These three roles are shown to be adopted at different times by both participants involved in dialogic communication. It is further suggested that variations occur in how these roles are conducted: the sender and receiver roles can be approached in a passive or an active way, while the responder role can be classified as being either resistant or responsive to the information received in dialogic communication. The final modelling of the definition derived provides a framework which can be tested in the field to determine whether variations in the conduct of the roles in dialogic communication actually exist, and if so, whether they can be linked to the different types of outcome from dialogic communication identified previously in the literature.

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Purpose – In recent years, knowledge-based urban development (KBUD) has introduced as a new strategic development approach for the regeneration of industrial cities. It aims to create a knowledge city consists of planning strategies, IT networks and infrastructures that achieved through supporting the continuous creation, sharing, evaluation, renewal and update of knowledge. Improving urban amenities and ecosystem services by creating sustainable urban environment is one of the fundamental components for KBUD. In this context, environmental assessment plays an important role in adjusting urban environment and economic development towards a sustainable way. The purpose of this paper is to present the role of assessment tools for environmental decision making process of knowledge cities. Design/methodology/approach – The paper proposes a new assessment tool to figure a template of a decision support system which will enable to evaluate the possible environmental impacts in an existing and future urban context. The paper presents the methodology of the proposed model named ‘ASSURE’ which consists of four main phases. Originality/value –The proposed model provides a useful guidance to evaluate the urban development and its environmental impacts to achieve sustainable knowledge-based urban futures. Practical implications – The proposed model will be an innovative approach to provide the resilience and function of urban natural systems secure against the environmental changes while maintaining the economic development of cities.

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Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

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Authorised users (insiders) are behind the majority of security incidents with high financial impacts. Because authorisation is the process of controlling users’ access to resources, improving authorisation techniques may mitigate the insider threat. Current approaches to authorisation suffer from the assumption that users will (can) not depart from the expected behaviour implicit in the authorisation policy. In reality however, users can and do depart from the canonical behaviour. This paper argues that the conflict of interest between insiders and authorisation mechanisms is analogous to the subset of problems formally studied in the field of game theory. It proposes a game theoretic authorisation model that can ensure users’ potential misuse of a resource is explicitly considered while making an authorisation decision. The resulting authorisation model is dynamic in the sense that its access decisions vary according to the changes in explicit factors that influence the cost of misuse for both the authorisation mechanism and the insider.

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Physical infrastructure assets are important components of our society and our economy. They are usually designed to last for many years, are expected to be heavily used during their lifetime, carry considerable load, and are exposed to the natural environment. They are also normally major structures, and therefore present a heavy investment, requiring constant management over their life cycle to ensure that they perform as required by their owners and users. Given a complex and varied infrastructure life cycle, constraints on available resources, and continuing requirements for effectiveness and efficiency, good management of infrastructure is important. While there is often no one best management approach, the choice of options is improved by better identification and analysis of the issues, by the ability to prioritise objectives, and by a scientific approach to the analysis process. The abilities to better understand the effect of inputs in the infrastructure life cycle on results, to minimise uncertainty, and to better evaluate the effect of decisions in a complex environment, are important in allocating scarce resources and making sound decisions. Through the development of an infrastructure management modelling and analysis methodology, this thesis provides a process that assists the infrastructure manager in the analysis, prioritisation and decision making process. This is achieved through the use of practical, relatively simple tools, integrated in a modular flexible framework that aims to provide an understanding of the interactions and issues in the infrastructure management process. The methodology uses a combination of flowcharting and analysis techniques. It first charts the infrastructure management process and its underlying infrastructure life cycle through the time interaction diagram, a graphical flowcharting methodology that is an extension of methodologies for modelling data flows in information systems. This process divides the infrastructure management process over time into self contained modules that are based on a particular set of activities, the information flows between which are defined by the interfaces and relationships between them. The modular approach also permits more detailed analysis, or aggregation, as the case may be. It also forms the basis of ext~nding the infrastructure modelling and analysis process to infrastructure networks, through using individual infrastructure assets and their related projects as the basis of the network analysis process. It is recognised that the infrastructure manager is required to meet, and balance, a number of different objectives, and therefore a number of high level outcome goals for the infrastructure management process have been developed, based on common purpose or measurement scales. These goals form the basis of classifYing the larger set of multiple objectives for analysis purposes. A two stage approach that rationalises then weights objectives, using a paired comparison process, ensures that the objectives required to be met are both kept to the minimum number required and are fairly weighted. Qualitative variables are incorporated into the weighting and scoring process, utility functions being proposed where there is risk, or a trade-off situation applies. Variability is considered important in the infrastructure life cycle, the approach used being based on analytical principles but incorporating randomness in variables where required. The modular design of the process permits alternative processes to be used within particular modules, if this is considered a more appropriate way of analysis, provided boundary conditions and requirements for linkages to other modules, are met. Development and use of the methodology has highlighted a number of infrastructure life cycle issues, including data and information aspects, and consequences of change over the life cycle, as well as variability and the other matters discussed above. It has also highlighted the requirement to use judgment where required, and for organisations that own and manage infrastructure to retain intellectual knowledge regarding that infrastructure. It is considered that the methodology discussed in this thesis, which to the author's knowledge has not been developed elsewhere, may be used for the analysis of alternatives, planning, prioritisation of a number of projects, and identification of the principal issues in the infrastructure life cycle.

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There has been a worldwide trend to increase axle loads and train speeds. This means that railway track degradation will be accelerated, and track maintenance costs will be increased significantly. There is a need to investigate the consequences of increasing traffic load. The aim of the research is to develop a model for the analysis of physical degradation of railway tracks in response to changes in traffic parameters, especially increased axle loads and train speeds. This research has developed an integrated track degradation model (ITDM) by integrating several models into a comprehensive framework. Mechanistic relationships for track degradation hav~ ?een used wherever possible in each of the models contained in ITDM. This overcc:mes the deficiency of the traditional statistical track models which rely heavily on historical degradation data, which is generally not available in many railway systems. In addition statistical models lack the flexibility of incorporating future changes in traffic patterns or maintenance practices. The research starts with reviewing railway track related studies both in Australia and overseas to develop a comprehensive understanding of track performance under various traffic conditions. Existing railway related models are then examined for their suitability for track degradation analysis for Australian situations. The ITDM model is subsequently developed by modifying suitable existing models, and developing new models where necessary. The ITDM model contains four interrelated submodels for rails, sleepers, ballast and subgrade, and track modulus. The rail submodel is for rail wear analysis and is developed from a theoretical concept. The sleeper submodel is for timber sleepers damage prediction. The submodel is developed by modifying and extending an existing model developed elsewhere. The submodel has also incorporated an analysis for the likelihood of concrete sleeper cracking. The ballast and subgrade submodel is evolved from a concept developed in the USA. Substantial modifications and improvements have been made. The track modulus submodel is developed from a conceptual method. Corrections for more global track conditions have been made. The integration of these submodels into one comprehensive package has enabled the interaction between individual track components to be taken into account. This is done by calculating wheel load distribution with time and updating track conditions periodically in the process of track degradation simulation. A Windows-based computer program ~ssociated with ITDM has also been developed. The program enables the user to carry out analysis of degradation of individual track components and to investigate the inter relationships between these track components and their deterioration. The successful implementation of this research has provided essential information for prediction of increased maintenance as a consequence of railway trackdegradation. The model, having been presented at various conferences and seminars, has attracted wide interest. It is anticipated that the model will be put into practical use among Australian railways, enabling track maintenance planning to be optimized and potentially saving Australian railway systems millions of dollars in operating costs.

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This thesis addresses the contemporary issue of the control, restoration and potential for reuse of State Government-owned heritage properties with commercial potential. It attempts to reconcile the sometimes competing interests of the range of stakeholders in such properties, particularly those seeking to maximise economic performance and return on one hand and community expectations for heritage preservation and exhibition on the other. The matters are approached principally from the Government's position as asset owner/manager. It includes research into a number of key elements - including statutory, physical and economic parameters and an analysis of the legitimate requirements of all stakeholders. The thesis also recognises the need for innovation in approach and for the careful structuring and pre-planning of proposals on a project-by-project basis. On the matter of innovation, four case studies are included in the thesis to exhibit some approaches and techniques that have already been employed in addressing these issues. From this research base, a series of deductions at both a macro and micro level are established and a model for a rational decision-making process for dealing with such projects is developed as a major outcome of the work. Finally, the general model is applied to a specific project, the currently unused Port Office heritage site in the Brisbane Central Business District.

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This study develops a model (i.e., secondary values selection process - 2VS) to describe how values shared by individuals (i.e., secondary values) contribute to the creation of meaning and interpretation in organisations. Elements of the model are identified through exploration of two bodies of literature (a) cultural approaches to organisational studies, and (b) theories of evolution. Incorporated within the model are observable elements that support analysis and evaluation of the 2VS. Outcomes of the study are (a) development of a more complete understanding of the Selection Process in organising and (b) creation of a mechanism for cultural analysis of organisational settings.

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A review of the main rolling models is conducted to assess their suitability for modelling the foil rolling process. Two such models are Fleck and Johnson's Hertzian model and Fleck, Johnson, Mear and Zhang's Influence Function model. Both of these models are approximated through the use of perturbation methods. Decrease in the computation time resulted when compared with the numerical solution. The Hertzian model was approximated using the ratio of the yield stress of the strip to the plane-strain Young's Modulus of the rolls as the small perturbation parameter. The Influence Function model approximation takes advantage of the solution of the well-known Aerofoil Integral Equation to gain an insight into how the choice of interior boundary points affects the stability of numerical solution of the model's equations. These approximations require less computation than their full models and, in the case of the Hertzian approximation, only introduces a small error in the predictions of roll force roll torque. Hence the Hertzian approximate method is suitable for on-line control. The predictions from the Influence Function approximation underestimates the predictions from the numerical results. Better approximation of the pressure in the plastic reduction regions is the main source of this error.

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The present paper focuses on some interesting classes of process-control games, where winning essentially means successfully controlling the process. A master for one of these games is an agent who plays a winning strategy. In this paper we investigate situations in which even a complete model (given by a program) of a particular game does not provide enough information to synthesize—even incrementally—a winning strategy. However, if in addition to getting a program, a machine may also watch masters play winning strategies, then the machine is able to incrementally learn a winning strategy for the given game. Studied are successful learning from arbitrary masters and from pedagogically useful selected masters. It is shown that selected masters are strictly more helpful for learning than are arbitrary masters. Both for learning from arbitrary masters and for learning from selected masters, though, there are cases where one can learn programs for winning strategies from masters but not if one is required to learn a program for the master's strategy itself. Both for learning from arbitrary masters and for learning from selected masters, one can learn strictly more by watching m+1 masters than one can learn by watching only m. Last, a simulation result is presented where the presence of a selected master reduces the complexity from infinitely many semantic mind changes to finitely many syntactic ones.

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Nature Refuges encompass the second largest extent of protected area estate in Queensland. Major problems exist in the data capture, map presentation, data quality and integrity of these boundaries. The spatial accuracies/inaccuracies of the Nature Refuge administrative boundaries directly influence the ability to preserve valuable ecosystems by challenging negative environmental impacts on these properties. This research work is about supporting the Nature Refuge Programs efforts to secure Queensland’s natural and cultural values on private land by utilising GIS and its advanced functionalities. The research design organizes and enters Queensland’s Nature Refuge boundaries into a spatial environment. Survey quality data collection techniques such as the Global Positioning Systems (GPS) are investigated to capture Nature Refuge boundary information. Using the concepts of map communication GIS Cartography is utilised for the protected area plan design. New spatial datasets are generated facilitating the effectiveness of investigative data analysis. The geodatabase model developed by this study adds rich GIS behaviour providing the capability to store, query, and manipulate geographic information. It provides the ability to leverage data relationships and enforces topological integrity creating savings in customization and productivity. The final phase of the research design incorporates the advanced functions of ArcGIS. These functions facilitate building spatial system models. The geodatabase and process models developed by this research can be easily modified and the data relating to mining can be replaced by other negative environmental impacts affecting the Nature Refuges. Results of the research are presented as graphs and maps providing visual evidence supporting the usefulness of GIS as means for capturing, visualising and enhancing spatial quality and integrity of Nature Refuge boundaries.

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This paper demonstrates a model of self-regulation based on a qualitative research project with adult learners undertaking an undergraduate degree. The narrative about the participant’s life transitions, co-constructed with the researcher, yielded data about their generalised self-efficacy and resulted in a unique self-efficacy narrative for each participant. A model of self-regulation is proposed with potential applications for coaching, counselling and psychotherapy. A narrative method was employed to construct narratives about an individual’s self-efficacy in relation to their experience of learning and life transitions. The method involved a cyclical and iterative process using qualitative interviews to collect life history data from participants. In addition, research participants completed reflective homework tasks, and this data was included in the participant’s narratives. A highly collaborative method entailed narratives being co-constructed by researcher and research participants as the participants were guided in reflecting on their experience in relation to learning and life transitions; the reflection focused on behaviour, cognitions and emotions that constitute a sense of self-efficacy. The analytic process used was narrative analysis, in which life is viewed as constructed and experienced through the telling and retelling of stories and hence the analysis is the creation of a coherent and resonant story. The method of constructing self-efficacy narratives was applied to a sample of mature aged students starting an undergraduate degree. The research outcomes confirmed a three-factor model of self-efficacy, comprising three interrelated stages: initiating action, applying effort, and persistence in overcoming difficulties. Evaluation of the research process by participants suggested that they had gained an enhanced understanding of self-efficacy from their participation in the research process, and would be able to apply this understanding to their studies and other endeavours in the future. A model of self-regulation is proposed as a means for coaches, counsellors and psychotherapists working from a narrative constructivist perspective to assist clients facing life transitions by helping them generate selfefficacious cognitions, emotions and behaviour.

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At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant (p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

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This paper explores models of teaching and learning music composition in higher education. It analyses the pedagogical approaches apparent in the literature on teaching and learning composition in schools and universities, and introduces a teaching model as: learning from the masters; mastery of techniques; exploring ideas; and developing voice. It then presents a learning model developed from a qualitative study into students’ experiences of learning composition at university as: craft, process and art. The relationship between the students’ experiences and the pedagogical model is examined. Finally, the implications for composition curricula in higher education are presented.