Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising.

Objectives: The aim of the two exploratory studies presented here, was to investigate expert-novice cognitive performance in the field of dietetic counseling. More specifically, the purpose was to characterize the knowledge used and the cognitive reasoning strategies of expert, intermediate and novice dietitians during their assessment of clinical vignettes of simulated dyslipidemia cases. Background: Since no studies have been conducted on the expert-novice differences in knowledge utilization and reasoning in the field of dietetics, literature from various domains looking at expert-novice decision-making was used to guide the studies presented here. Previous expert-novice research in aspects of health such as counseling and diagnostic reasoning among physicians and nurses has found differences between in the way experts extract and apply knowledge during reasoning. In addition, various studies illustrate an intermediate effect, where generalist performance is somewhat poorer than that of experts and novices. Methods: The verbal protocols of expert (n=4), generalist (n=4), and novice (n=4) dietitians were analyzed, using propositional analysis. Semantic networks were generated, and used to compare reasoning processes to a reference model developed from an existing Dyslipidemia care map by Brauer et al, (2007, 2009). Detailed analysis was conducted on individual networks in an effort to obtain better understanding of cue utilization, concept usage, and overall cohesiveness during reasoning. Results: The results of the first study indicate no statistical differences in reasoning between novices, generalist and experts with regards to recalls and inferences. Interesting findings in the study also suggest that discussions of the terms “dietary fat” and “cholesterol” by individuals in each level of expertise had qualitative differences. This may be reflective of the information provided in the case scenearios to each participating dietitian. Furthermore, contrary to previous studies in expert-novice reasoning, an intermediate effect was not evident. The results of the second study show a statistical difference in data driven (forward) reasoning between experts and novices. There was no statistical difference in hypothesis driven (backward) reasoning between groups. The reasoning networks of experts appear to reveal more concise explanations of important aspects related to dyslipidemia counseling. Reasoning patterns of the expert dietitians appear more coherent, although there was no statistical difference in the length or number of reasoning chains between groups. With previous research focusing on diagnostic reasoning rather than counseling, this finding may be a result of the nature of the underlying task. Conclusion: The studies presented here serve as a basis for future expert-novice research in the field of dietetics. The exploration of individual verbal protocols to identify characteristics of dietitians of various levels of expertise, can provide insight into the way knowledge is used and applied during diet counseling. Subsequent research can focus on randomized sample selection, with case scenarios as a constant, in order to obtain results that can be generalized to the greater dietitian population.

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This project presents a Materials Informatics Workbench that resolves the challenges confronting materials scientists in the aspects of materials science data assimilation and dissemination. It adopts an approach that has ingeniously combined and extended the technologies of the Semantic Web, Web Service Business Process Execution Language (WSBPEL) and Open Archive Initiative Object Reuse and Exchange (OAI-ORE). These technologies enable the development of novel user interfaces and innovative algorithms and techniques behind the major components of the proposed workbench. In recent years, materials scientists have been struggling with the challenge of dealing with the ever-increasing amount of complex materials science data that are available from online sources and generated by the high-throughput laboratory instruments and data-intensive software tools, respectively. Meanwhile, the funding organizations have encouraged, and even mandated, the sponsored researchers across many domains to make the scientifically-valuable data, together with the traditional scholarly publications, available to the public. This open access requirement provides the opportunity for materials scientists who are able to exploit the available data to expedite the discovery of novel compound materials. However, it also poses challenges for them. The materials scientists raise concerns about the difficulties of precisely locating and processing diverse, but related, data from different data sources and of effectively managing laboratory information and data. In addition, they also lack the simple tools for data access and publication, and require measures for Intellectual Property protection and standards for data sharing, exchange and reuse. The following paragraphs describe how the major workbench components resolve these challenges. First, the materials science ontology, represented in the Web Ontology Language (OWL), enables, (1) the mapping between and the integration of the disparate materials science databases, (2) the modelling of experimental provenance information acquired in the physical and digital domains and, (3) the inferencing and extraction of new knowledge within the materials science domain. Next, the federated search interface based on the materials science ontology enables the materials scientists to search, retrieve, correlate and integrate diverse, but related, materials science data and information across disparate databases. Then, a workflow management system underpinning the WSBPEL engine is not only able to manage the scientific investigation process that incorporates multidisciplinary scientists distributed over a wide geographic region and self-contained computational services, but also systematically acquire the experimental data and information generated by the process. Finally, the provenance-aware scientific compound-object publishing system provides the scientists with a view of the highly complex scientific workflow at multiple-grained levels. Thus, they can easily comprehend the science of the workflow, access experimental information and keep the confidential information from unauthorised viewers. It also enables the scientists to quickly and easily author and publish a scientific compound object that, (1) incorporates not only the internal experimental data with the provenance information from the rendered view of a scientific experimental workflow, but also external digital objects with the metadata, for example, published scholarly papers discoverable via the World Wide Web (the Web), (2) is self- contained and explanatory with IP protection and, (3) is guaranteed to be disseminated widely on the Web. The prototype systems of the major workbench components have been developed. The quality of the material science ontology has been assessed, based on Gruber’s principles for the design of ontologies used for knowledge–sharing, while its applicability has been evaluated through two of the workbench components, the ontology-based federated search interface and the provenance-aware scientific compound object publishing system. Those prototype systems have been deployed within a team of fuel cell scientists working within the Australian Institute for Bioengineering and Nanotechnology (AIBN) at the University of Queensland. Following the user evaluation, the overall feedback to date has been very positive. First, the scientists were impressed with the convenience of the ontology-based federated search interface because of the easy and quick access to the integrated databases and analytical tools. Next, they felt the surge of the relief that the complex compound synthesis process could be managed by and monitored through the WSBPEL workflow management system. They were also excited because the system is able to systematically acquire huge amounts of complex experimental data produced by self-contained computational services that is no longer handled manually with paper-based laboratory notebooks. Finally, the scientific compound object publishing system inspired them to publish their data voluntarily, because it provides them with a scientific-friendly and intuitive interface that enables scientists to, (1) intuitively access experimental data and information, (2) author self-contained and explanatory scientific compound objects that incorporate experimental data and information about research outcomes, and published scholarly papers and peer-reviewed datasets to strengthen those outcomes, (3) enforce proper measures for IP protection, (4) comply those objects with the Open Archives Initiative Protocol – Object Exchange and Reuse (OAI-ORE) to maximize its dissemination over the Web and,(5) ingest those objects into a Fedora-based digital library.

This project presents a Materials Informatics Workbench that resolves the challenges confronting materials scientists in the aspects of materials science data assimilation and dissemination. It adopts an approach that has ingeniously combined and extended the technologies of the Semantic Web, Web Service Business Process Execution Language (WSBPEL) and Open Archive Initiative Object Reuse and Exchange (OAI-ORE). These technologies enable the development of novel user interfaces and innovative algorithms and techniques behind the major components of the proposed workbench. In recent years, materials scientists have been struggling with the challenge of dealing with the ever-increasing amount of complex materials science data that are available from online sources and generated by the high-throughput laboratory instruments and data-intensive software tools, respectively. Meanwhile, the funding organizations have encouraged, and even mandated, the sponsored researchers across many domains to make the scientifically-valuable data, together with the traditional scholarly publications, available to the public. This open access requirement provides the opportunity for materials scientists who are able to exploit the available data to expedite the discovery of novel compound materials. However, it also poses challenges for them. The materials scientists raise concerns about the difficulties of precisely locating and processing diverse, but related, data from different data sources and of effectively managing laboratory information and data. In addition, they also lack the simple tools for data access and publication, and require measures for Intellectual Property protection and standards for data sharing, exchange and reuse. The following paragraphs describe how the major workbench components resolve these challenges. First, the materials science ontology, represented in the Web Ontology Language (OWL), enables, (1) the mapping between and the integration of the disparate materials science databases, (2) the modelling of experimental provenance information acquired in the physical and digital domains and, (3) the inferencing and extraction of new knowledge within the materials science domain. Next, the federated search interface based on the materials science ontology enables the materials scientists to search, retrieve, correlate and integrate diverse, but related, materials science data and information across disparate databases. Then, a workflow management system underpinning the WSBPEL engine is not only able to manage the scientific investigation process that incorporates multidisciplinary scientists distributed over a wide geographic region and self-contained computational services, but also systematically acquire the experimental data and information generated by the process. Finally, the provenance-aware scientific compound-object publishing system provides the scientists with a view of the highly complex scientific workflow at multiple-grained levels. Thus, they can easily comprehend the science of the workflow, access experimental information and keep the confidential information from unauthorised viewers. It also enables the scientists to quickly and easily author and publish a scientific compound object that, (1) incorporates not only the internal experimental data with the provenance information from the rendered view of a scientific experimental workflow, but also external digital objects with the metadata, for example, published scholarly papers discoverable via the World Wide Web (the Web), (2) is self- contained and explanatory with IP protection and, (3) is guaranteed to be disseminated widely on the Web. The prototype systems of the major workbench components have been developed. The quality of the material science ontology has been assessed, based on Gruber’s principles for the design of ontologies used for knowledge–sharing, while its applicability has been evaluated through two of the workbench components, the ontology-based federated search interface and the provenance-aware scientific compound object publishing system. Those prototype systems have been deployed within a team of fuel cell scientists working within the Australian Institute for Bioengineering and Nanotechnology (AIBN) at the University of Queensland. Following the user evaluation, the overall feedback to date has been very positive. First, the scientists were impressed with the convenience of the ontology-based federated search interface because of the easy and quick access to the integrated databases and analytical tools. Next, they felt the surge of the relief that the complex compound synthesis process could be managed by and monitored through the WSBPEL workflow management system. They were also excited because the system is able to systematically acquire huge amounts of complex experimental data produced by self-contained computational services that is no longer handled manually with paper-based laboratory notebooks. Finally, the scientific compound object publishing system inspired them to publish their data voluntarily, because it provides them with a scientific-friendly and intuitive interface that enables scientists to, (1) intuitively access experimental data and information, (2) author self-contained and explanatory scientific compound objects that incorporate experimental data and information about research outcomes, and published scholarly papers and peer-reviewed datasets to strengthen those outcomes, (3) enforce proper measures for IP protection, (4) comply those objects with the Open Archives Initiative Protocol – Object Exchange and Reuse (OAI-ORE) to maximize its dissemination over the Web and,(5) ingest those objects into a Fedora-based digital library.

This dissertation primarily proposes data-driven methods to handle uncertainty in problems related to Enterprise-wide Optimization (EWO). Datadriven methods are characterized by the direct use of data (historical and/or forecast) in the construction of models for the uncertain parameters that naturally arise from real-world applications. Such uncertainty models are then incorporated into the optimization model describing the operations of an enterprise. Before addressing uncertainty in EWO problems, Chapter 2 deals with the integration of deterministic planning and scheduling operations of a network of batch plants. The main contributions of this chapter include the modeling of sequence-dependent changeovers across time periods for a unitspecific general precedence scheduling formulation, the hybrid decomposition scheme using Bilevel and Temporal Lagrangean Decomposition approaches, and the solution of subproblems in parallel. Chapters 3 to 6 propose different data analytics techniques to account for stochasticity in EWO problems. Chapter 3 deals with scenario generation via statistical property matching in the context of stochastic programming. A distribution matching problem is proposed that addresses the under-specification shortcoming of the originally proposed moment matching method. Chapter 4 deals with data-driven individual and joint chance constraints with right-hand side uncertainty. The distributions are estimated with kernel smoothing and are considered to be in a confidence set, which is also considered to contain the true, unknown distributions. The chapter proposes the calculation of the size of the confidence set based on the standard errors estimated from the smoothing process. Chapter 5 proposes the use of quantile regression to model production variability in the context of Sales & Operations Planning. The approach relies on available historical data of actual vs. planned production rates from which the deviation from plan is defined and considered a random variable. Chapter 6 addresses the combined optimal procurement contract selection and pricing problems. Different price-response models, linear and nonlinear, are considered in the latter problem. Results show that setting selling prices in the presence of uncertainty leads to the use of different purchasing contracts.

Improving robotic manipulation is critical for robots to be actively useful in realworld factories and homes. While some success has been shown in simulation and controlled environments, robots are slow, clumsy, and not general or robust enough when interacting with their environment. By contrast, humans effortlessly manipulate objects. One possible reason for this discrepancy is that, starting from birth, humans have years of experience to collect data and develop good internal models of what happens when they manipulate objects. If robots could also learn models from a large amount of real data, perhaps they, too, could become more capable manipulators. In this thesis, we propose to improve robotic manipulation by solving two problems. First, we look at how robots can collect a large amount of manipulation data without human intervention. Second, we study how to build statistical models of robotic manipulation from the collected data. These data-driven models can then be used for planning more robust manipulation actions. To solve the first problem of enabling large data collection, we perform several different robotic manipulation experiments and use these as case studies. We study bin-picking, post-grasp manipulation, pushing, tray tilting, planar grasping, and regrasping. These case studies allow us to gain insights on how robots can collect a large amount of accurate data with minimal human intervention. To solve the second problem of statistically modeling manipulation actions, we propose models for different parts of various manipulation actions. First, we look at how to model post-grasp manipulation actions by modeling the probability distribution of where an object ends up in a robot’s hand, and how this affects its success rate at various tasks such as placing or insertion. Second, we model how robots can change the pose of an object in their hand with regrasp actions. Third, we improve on the place and pick regrasp action by modeling each separately with more data. These learned data-driven models can then be used for planning more robust and accurate manipulation actions.

Este trabalho trata da utilização de um método de sintonia de controladores baseado nos dados obtidos da planta. A proposta é a sintonia de controladores ressonantes para aplicação em inversores de frequência presentes em fontes ininterruptas de energia, com o intuito de seguimento de referência senoidal de tensão. Dentro deste contexto, será usado o algoritmo Virtual Reference Feedback Tuning, o qual é um método de identificação de controladores baseado em dados que não é iterativo e não necessita do modelo do sistema para identificar o controlador. A partir dos dados obtidos da planta e também da definição de um modelo de referência pelo projetista, o método estima os parâmetros de uma estrutura fixada previamente para o controlador através da minimização de uma função custo definida pelo erro entre a saída desejada e a saída real. Além disso, uma realimentação de corrente é necessária na malha de controle, onde seu ganho proporcional é definido por experimento empírico. Para demonstrar a utilização do método são apresentados resultados simulados e práticos de uma fonte ininterrupta de energia com potência de 5 kV A utilizando cargas lineares e não-lineares. É avaliado o desempenho do ponto de vista da qualidade do sinal de saída real obtido com controladores sintonizados a partir de diferentes modelos de referência, além do uso de sinais de excitação diversos para o algoritmo V RFT. Os resultados experimentais são obtidos em um inversor de frequência monofásico com uma plataforma em tempo real baseada na placa de aquisição de dados dSPACE DS1104. Os resultados mostram que, em relação as normas internacionais, o sistema de controle proposto possui bom comportamento para seguimento de referência, operando à vazio ou utilizando carga linear. / This work discusses about controller tuning methods based on plant data. The proposal is to tune resonant controllers for application to the frequency inverters found in uninterruptible power supplies, with the goal of following sinusoidal reference signals. Within this context, the Virtual Reference Feedback Tuning algorithm is used, which is a data-driven controller identification method that is not iterative and does not require a system model to identify the controller. Data obtained from the plant and also the definition of a reference model by the designer, are used by the method to estimate the parameters of a previously fixed controller structure through the minimization of a cost function, which is defined by the error between desired and actual outputs. Moreover, a current feedback is required in the control loop where the proportional gain is defined by empirical experiment. To demonstrate the method’s application, simulated and practical results of an uninterruptible power supply with capacity of the 5 kV A will be presented employing linear and nonlinear loads. Evaluates the performance in terms of system’s actual output quality, obtained with controllers tuned with different reference models. Distinct excitation signals are also used to feed the VRFT algorithm. The experimental results achieved from use of an single-phase inverter and a real-time platform based on data acquisition board dSPACE DS1104. The results show that, with respect to international standards, the proposed control system has good performance for tracking reference, operating at empty or using linear load.

The single-core performance trend predicted by Moore's law has been impeded in recent years partly due to the limitations imposed by increasing processor power demands. One way to mitigate this limitation in performance improvement is the introduction of multi-core and multi-processor computation. Another approach to increasing the performance-per-Watt metric is to utilize the processor's power more efficiently. In a single-core system, the processor cannot sustainably dissipate more than the nominal Thermal Design Power (TDP) limit determined for the processor at design time. Therefore it is important to understand and manage the power demands of the processes being executed. This principle also applies to multi-core and multi-processor environments. In a multi-processor environment, knowing the power demands of the workload, the power management unit can schedule the workload to a processor based on the state of each processor and process in the most efficient way. This is an example of the knapsack problem. Another approach, also applicable to multi-cores, could be to reduce the core's power by reducing its working voltage and frequency, leading to mitigation of the power bursts, lending more headroom to other cores, and keeping the total power under the TDP limit. The information collected from the execution of the software running on the processor (i.e. the workload) is the key to determining the actions needed with regards to power management at any given time. This work comprises two different approaches in improving the comprehension of software power demands as it executes on the processor. In the first part of this work, the effects of software data on power is analysed. It is important to be able to model the power based on the instructions it comprises, however, to the best of our knowledge, no work exists in which the effects of the values being processed has been investigated with regards to processor power. Creating a power model capable of accurately reflecting the power demands of the software at any given time is a problem addressed by previous research. The software power model can be used in processor simulation environments as well as in the processor itself to create an estimated power dissipation without the need to physically measure the power. In the first part of this research, the effects of software data on power is investigated. In order to collect the data required as part of this research, a profiler tool has been developed by the author and used in this part of the research as well as the second part. The second part of this work focuses on the development of processor power throughout time during the execution of the software. Understanding the power demands of the processor at any given time is important to maintain and manage processor power. Additionally, acquiring an insight into the future power demands of the software can help the system with scheduling planning ahead of time, in order to prepare for any high-power section of the code as well as to plan to use the available power headroom as a result of an upcoming low-power section. In this part of our work, a new hierarchical approach to software phase classification is developed. Software phase classification problem focuses on determining the behaviour of the software at any given time slice by assigning the time slice to one of pre-determined software phases. Each phase is assumed to have known behaviour which was previously measured and instrumented based on previously observed instances of the phase, or by utilizing a model capable of estimating the behaviour of each phase. Using a two-tiered hierarchical clustering approach, our proposed phase classification methodology incorporates the recent performance behaviour of the software in order to determine the power phase. We focused on determining the power phase using the performance information because the real processor power is not usually available without the need for added hardware, while there exists a large number of different performance counters available on most modern processors. Additionally, based on our observations, the relation between performance phases and power behaviour is highly predictable. This method is shown to provide robust results with a low amount of noise compared to other methods, while providing a high enough timing accuracy for the processor to act on. To the best of our knowledge, no other existing work is able to provide both timing accuracy and reduced noise compared to our work. Software phase classification can be used to control the processor power based on the software's phase at any given time, but it does not provide future insight into the progression of the workload. Finally, we developed and compared several phase prediction methodologies based on phase precursors and phase locality concepts. Phase precursor-based methods rely on detecting the precursors observed before the software enters a certain phase, while phase locality methods rely on the locality principle, which postulates a high probability for the current software behaviour to be observed in the near-future. The phase classification, as well as phase prediction methodologies was shown to be able to reduce the power bursts within a workload in order to provide a more smooth power trace. As the bursts are removed from one workload's power trace, the multi-core processor power headroom can be confidently utilized for another process. / Graduate

Este trabalho trata da utilização de um método de sintonia de controladores baseado nos dados obtidos da planta. A proposta é a sintonia de controladores ressonantes para aplicação em inversores de frequência presentes em fontes ininterruptas de energia, com o intuito de seguimento de referência senoidal de tensão. Dentro deste contexto, será usado o algoritmo Virtual Reference Feedback Tuning, o qual é um método de identificação de controladores baseado em dados que não é iterativo e não necessita do modelo do sistema para identificar o controlador. A partir dos dados obtidos da planta e também da definição de um modelo de referência pelo projetista, o método estima os parâmetros de uma estrutura fixada previamente para o controlador através da minimização de uma função custo definida pelo erro entre a saída desejada e a saída real. Além disso, uma realimentação de corrente é necessária na malha de controle, onde seu ganho proporcional é definido por experimento empírico. Para demonstrar a utilização do método são apresentados resultados simulados e práticos de uma fonte ininterrupta de energia com potência de 5 kV A utilizando cargas lineares e não-lineares. É avaliado o desempenho do ponto de vista da qualidade do sinal de saída real obtido com controladores sintonizados a partir de diferentes modelos de referência, além do uso de sinais de excitação diversos para o algoritmo V RFT. Os resultados experimentais são obtidos em um inversor de frequência monofásico com uma plataforma em tempo real baseada na placa de aquisição de dados dSPACE DS1104. Os resultados mostram que, em relação as normas internacionais, o sistema de controle proposto possui bom comportamento para seguimento de referência, operando à vazio ou utilizando carga linear. / This work discusses about controller tuning methods based on plant data. The proposal is to tune resonant controllers for application to the frequency inverters found in uninterruptible power supplies, with the goal of following sinusoidal reference signals. Within this context, the Virtual Reference Feedback Tuning algorithm is used, which is a data-driven controller identification method that is not iterative and does not require a system model to identify the controller. Data obtained from the plant and also the definition of a reference model by the designer, are used by the method to estimate the parameters of a previously fixed controller structure through the minimization of a cost function, which is defined by the error between desired and actual outputs. Moreover, a current feedback is required in the control loop where the proportional gain is defined by empirical experiment. To demonstrate the method’s application, simulated and practical results of an uninterruptible power supply with capacity of the 5 kV A will be presented employing linear and nonlinear loads. Evaluates the performance in terms of system’s actual output quality, obtained with controllers tuned with different reference models. Distinct excitation signals are also used to feed the VRFT algorithm. The experimental results achieved from use of an single-phase inverter and a real-time platform based on data acquisition board dSPACE DS1104. The results show that, with respect to international standards, the proposed control system has good performance for tracking reference, operating at empty or using linear load.