824 resultados para Probabilistic decision process model
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This paper presents practical vision-based collision avoidance for objects approximating a single point feature. Using a spherical camera model, a visual predictive control scheme guides the aircraft around the object along a conical spiral trajectory. Visibility, state and control constraints are considered explicitly in the controller design by combining image and vehicle dynamics in the process model, and solving the nonlinear optimization problem over the resulting state space. Importantly, range is not required. Instead, the principles of conical spiral motion are used to design an objective function that simultaneously guides the aircraft along the avoidance trajectory, whilst providing an indication of the appropriate point to stop the spiral behaviour. Our approach is aimed at providing a potential solution to the See and Avoid problem for unmanned aircraft and is demonstrated through a series.
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The market of building retrofits is increasingly more intensified as existing buildings are aging. The building retrofit projects involve existing buildings which impose constraints on stakeholders throughout the project process. They are also risky, complex, less predictable and difficult to be well planned 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, but they often do not have adequate resources to deal with the specific technical challenges and project risks related to waste. This paper first discusses the requirements of waste management in building retrofit projects considering specific project characteristics and work natures, and highlights the importance of involving SMEs in waste planning and management through an appropriate way. By utilizing semi-structured interviews, this research develops a process model for SMEs to be applied in waste management. A collaboration scenario is also developed for collaborative waste planning and management by SMEs as subcontractors and large companies as main contractors. Findings from the paper will promote coordination of project delivery and waste management in building retrofit projects, and improve the involvement and performance of SMEs in dealing with waste problems.
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Product Lifecycle Management (PLM) systems are widely used in the manufacturing industry. A core feature of such systems is to provide support for versioning of product data. As workflow functionality is increasingly used in PLM systems, the possibility emerges that the versioning transitions for product objects as encapsulated in process models do not comply with the valid version control policies mandated in the objects’ actual lifecycles. In this paper we propose a solution to tackle the (non-)compliance issues between processes and object version control policies. We formally define the notion of compliance between these two artifacts in product lifecycle management and then develop a compliance checking method which employs a well-established workflow analysis technique. This forms the basis of a tool which offers automated support to the proposed approach. By applying the approach to a collection of real-life specifications in a main PLM system, we demonstrate the practical applicability of our solution to the field.
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This paper demonstrates that project management is a developing field of academic study in management, of considerable diversity and richness, which can make a valuable contribution to the development of management knowledge, as well as being of considerable economic importance. The paper reviews the substantial progress and trends of research in the subject, which has been grouped into nine major schools of thought: optimization, modelling, governance, behaviour, success, decision, process, contingency, and marketing. The paper addresses interactions between the different schools and with other related management fields, and provides insights into current and potential research in each and across these schools.
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Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation. In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.
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Identification of behavioural contradictions is an important aspect of software engineering, in particular for checking the consistency between a business process model used as system specification and a corresponding workflow model used as implementation. In this paper, we propose causal behavioural profiles as the basis for a consistency notion, which capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities. Existing notions of behavioural equivalence, such as bisimulation and trace equivalence, might also be applied as consistency notions. Still, they are exponential in computation. Our novel concept of causal behavioural profiles provides a weaker behavioural consistency notion that can be computed efficiently using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets.
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Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is process model as a brain-storming tool. This approach heavily relies on the intuition of the participants and misses a clear description of the quality requirements. Against this background, we identify four generic quality categories of business process quality, and populate them with quality requirements from related research. We refer to the resulting framework as the Quality of Business Process (QoBP) framework. Furthermore, we present the findings from applying the QoBP framework in a case study with a major Australian bank, showing that it helps to systematically fill the white space between as-is and to-be process modeling.
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Robots currently recognise and use objects through algorithms that are hand-coded or specifically trained. Such robots can operate in known, structured environments but cannot learn to recognise or use novel objects as they appear. This thesis demonstrates that a robot can develop meaningful object representations by learning the fundamental relationship between action and change in sensory state; the robot learns sensorimotor coordination. Methods based on Markov Decision Processes are experimentally validated on a mobile robot capable of gripping objects, and it is found that object recognition and manipulation can be learnt as an emergent property of sensorimotor coordination.
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Belongingness has been linked to depression. Prior studies have been cross-sectional with few addressing distinct belongingness contexts. This study used structural equation modelling to investigate cross-lagged longitudinal relationships between general belonging, workplace belonging and depressive symptoms in a community sample of 221 working adults measured at two time points three months apart. Measures were: Sense of Belonging Instrument-Psychological (SOBI-P); Psychological Sense of Organizational Membership (PSOM); Depression Anxiety Stress Scales (DASS-21); Kessler Psychological Distress Scale (K10). General belonging was predicted more strongly by depressive symptoms than by baseline general belonging, suggesting that depressive symptoms not only linger but also influence future belongingness cognitions. Neither general nor workplace belonging longitudinally predicted depressive symptoms, however cross-sectional correlations were substantial. The concurrent path between general belongingness and depressive symptoms was strong. Results are consistent with daily process studies suggesting that reduced belongingness precipitates a rapid increase in depressive symptoms which influence longer term belongingness cognitions. Congruent with interpersonal descriptions of depression such as the social-cognitive interpersonal process model, results further suggest that belongingness cognitions are the proximal antecedent of a depressive response. Practitioners should monitor both a general sense of belonging as well as perceived relational value cues in specific contexts.
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Existing techniques for automated discovery of process models from event logs largely focus on extracting flat process models. In other words, they fail to exploit the notion of subprocess, as well as structured error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events, and loop and multi-instance markers. The technique analyzes dependencies between data attributes associated with events, in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered separately using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process model discovery techniques.
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A procurement decision-making model is developed based on a novel integration of leading-edge microeconomic theory and empirically tested in major road and health projects. The model provides a more reliable approach to identifying projects suited to Public-Private-Partnerships (PPPs) and it is expected that the model will enable government to deliver improved value-for-money from their portfolio of PPP projects.
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In large sedimentary basins with layers of different rocks, the groundwater flow between aquifers depends on the hydraulic conductivity (K) of the separating low-permeable rocks, or aquitards. Three methods were developed to evaluate K in aquitards for areas with limited field data: • Coherence and harmonic analysis: estimates the regional-scale K based on water-level fluctuations in adjacent aquifers. • Cokriging and Bayes' rule: infers K from downhole geophysical logs. • Fluvial process model: reproduces the lithology architecture of sediment formations which can be converted to K. These proposed methods enable good estimates of K and better planning of further drillholes.
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Organizations are increasingly seeking stakeholder support through engagement to demonstrate their corporate social responsibility (CSR) credentials. These credentials are in turn used to support claims of legitimacy for organizational operations. This paper uses a process model of antecedents, implementation, and consequences to study the connection between engagement and CSR. CSR reports show organizations perceive engagement in CSR as both communication and activities between organizations and their stakeholders; and as a second, meta-level of communication about that engagement with stakeholders beyond those directly involved, thereby broadening the scope of organizational claims to legitimacy. Understanding what engagement is and how and why it is carried out in CSR provides a framework for understanding engagement in public relations.
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This thesis investigated risk management and organisational reliability in airports from the perspective of complex sociotechnical systems (CSSs). Two research studies were undertaken, the first focusing on the processes by which disruptive events occur, are detected and responded to; the second exploring the presence of organisational reliability traits within airports. A key result of the studies was the development of new approach: the Critical Incident Disturbance Process model that detailed a means to understand and analyse how disruptions in complex CSSs might be influenced by vulnerability reduction and enhanced risk management. Further, this thesis identified and defined the concept of 'compartmentalised reliability' in complex sociotechnical systems, extending existing knowledge of high reliability theory.
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Objectives To address the scarcity of comprehensive, theory-based research in the Australian context, this study, using a theory of planned behaviour (TPB) framework, investigated the role of personal and social norms to identify the key predictors of adult Australians' sun-safe intentions and behaviour. Design The study used a prospective design with two waves of data collection, 1 week apart. Methods Participants were 816 adults (48.2% men) aged between 18 and 88 years recruited from urban, regional, and rural areas of Australia. At baseline, participants completed a questionnaire assessing the standard TPB predictors (attitude, subjective norm, and perceived behavioural control [PBC]), past behaviour, behavioural intention, and additional measures of group norm for the referent groups of friends and family, image norm, personal norm, personal choice/responsibility, and Australian identity. Seventy-one per cent of the participants (n = 577) reported on their sun-safe behaviour in the subsequent week. Results Via path modelling, past behaviour, attitude, group norm (friends), personal norm, and personal choice/responsibility emerged as independent predictors of intentions which, in turn, predicted sun-safe behaviour prospectively. Past behaviour, but not PBC, had direct effects on sun-safe behaviour. The model explained 61.6% and 43.9% of the variance in intention and behaviour, respectively. Conclusions This study provides support for the use of a comprehensive theoretical decision-making model to explain Australian adults' sun-safe intentions and behaviours and identifies viable targets for health-promoting messages in this high-risk context.