Many animals are social foragers. Foraging with others may confer a number of advantages, but is also likely to present a number of challenges that are not encountered by solitary foragers. For instance, whilst feeding in a group may interfere with an animal's ability to learn new foraging skills or the location of new foraging patches by itself, it may simultaneously provide it with the opportunity to acquire new skills or knowledge by means of social learning. This thesis addresses a number of questions relating to the interaction between social foraging and social learning using small groups of captive budgerigars, Melopsittacus undulatus, as a test species. In particular, it investigates the spread of novel foraging behaviour through groups of birds under conditions that either permit or restrict a high degree of ‘scrounging' (food stealing) by naïve birds from skilled ‘producers' in their group (Chapter Three). Scrounging is found to inhibit naïve budgerigars' performance of new foraging skills, but appears to facilitate their underlying acquisition, or motivation to acquire these skills, when the need arises – for instance, when producers are lost from their group. In addition, the thesis assesses the importance of a number of different individual-level characteristics, such as age, sex, and competitive rank, in predicting birds' propensity to behave as producers rather than scroungers when foraging in a group (Chapter Four). The thesis also examines budgerigars' relative use of social and personal information when selecting foraging locations (Chapter Five), and assesses the importance of group social networks in predicting individual birds' order and latency to arrive at foraging patches (Chapter Six). Budgerigars are found to rely on social information when they lack any personal information about foraging locations. When equipped with both social information and personal information, some, but not all birds appear still to utilise social information. Birds' social networks appear to have little bearing on individuals' foraging patch visitation times.

The effectiveness of e-mail writing has been exhaustively studied and reported on, especially in Taiwan. However, there has not been any research carried out on the topics that mainland Chinese university students enjoy writing about when corresponding with their Western epals, nor does the literature report research on writing e-mails to two groups of epals simultaneously. This study explores what issues concerned the participants when they exchanged e-mails with their Western epals and how they viewed their cross-cultural learning experience. The participants were 28 mainland Chinese second-year English majors who voluntarily corresponded with 28 American high school pupils and 28 Western adult epals for about two months in Autumn 2006. The data of this exploratory interpretative research was mainly collected from their e-mails, ‘final reports’, the mid-project questionnaire, and semi-structured interviews. The study found that the topics the participants enjoyed writing about actually depended on with whom they were corresponding. With the younger school pupils, they tended to look for friendship by talking about pastimes, their own high school experience, etc. To the more sophisticated adult epals though, they wrote largely about personal matters, on which they seemed to be covertly seeking advice. However, some topics were common to both groups and were equally popular – for example, school and daily life. The data also reveals that the majority of the participants enjoyed the experience and overall had positive views about it. These fall into three broad categories of learning: language, cultural, and communication. However, some experienced minor difficulties and problems in these areas, particularly regarding the communication aspect. Meanwhile, in the process of the participants multiediting their ‘final reports’, learning seems to have occurred between their first and final drafts – perhaps as a result of responding to the researcher’s written feedback, which seemed to make a significant difference. The implications arising from the study suggest that the students’ interest in it stimulated their engagement with learning - though the findings are tentative. Some recommendations for further research are also given.

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.

One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].

This thesis presents the development of a Neural Network Based Controller (NNBC) for chain grate stoker fired boilers. The objective of the controller was to increase combustion efficiency and maintain pollutant emissions below future medium term stringent legislation. Artificial Neural Networks (ANNs) were used to estimate future emissions from and control the combustion process. Initial tests at Casella CRE Ltd demonstrated the ability of ANNs to characterise the complex functional relationships which subsisted in the data set, and utilised previously gained knowledge to deliver predictions up to three minutes into the future. This technique was then built into a carefully designed control strategy that fundamentally mimicked the actions of an expert boiler operator, to control an industrial chain grate stoker at HM Prison Garth, Lancashire. Test results demonstrated that the developed novel NNBC was able to control the industrial stoker boiler plant to deliver the load demand whilst keeping the excess air level to a minimum. As a result the NNBC also managed to maintain the pollutant emissions within probable future limits for this size of boiler. This prototype controller would thus offer the industrial coal user with a means to improve the combustion efficiency on chain grate stokers as well as meeting medium term legislation limits on pollutant emissions that could be imposed by the European Commission.

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.

Submitted by Mayara Cobacho Ortega null (mayarac.ortega@gmail.com) on 2016-10-03T14:46:10Z No. of bitstreams: 1 Dissertação_Mestrado_Mayara_Caldeira.pdf: 4776033 bytes, checksum: ecc26c1af2e4e6f23b65a5eecbd9ed03 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-10-04T18:11:06Z (GMT) No. of bitstreams: 1 caldeira_mco_me_prud.pdf: 4776033 bytes, checksum: ecc26c1af2e4e6f23b65a5eecbd9ed03 (MD5) / Made available in DSpace on 2016-10-04T18:11:06Z (GMT). No. of bitstreams: 1 caldeira_mco_me_prud.pdf: 4776033 bytes, checksum: ecc26c1af2e4e6f23b65a5eecbd9ed03 (MD5) Previous issue date: 2016-02-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Visando usufruir do potencial dos sistemas de posicionamento global existentes, novos métodos de posicionamento têm surgido e outros vêm sendo aprimorados. Uma grande tendência nos últimos anos tem sido o uso de redes de estações GNSS de referência. Mas, tanto no uso de redes como nos demais métodos, um fator importante para melhorar a qualidade do posicionamento está relacionado com a modelagem atmosférica. Especial atenção deve ser dada aos erros que ocorrem devido à ionosfera, pois ela se tornou a principal fonte de erro no posicionamento GNSS, após desativação da técnica SA. Este erro é diretamente proporcional ao Conteúdo Total de Elétrons (TEC) e inversamente proporcional ao quadrado da frequência do sinal. O TEC e, consequentemente, o erro ionosférico variam no tempo e no espaço, e sofrem diversas influências, como: ciclo solar, época do ano, hora local, localização geográfica, atividade geomagnética, entre outros. Atualmente, o os erros proporcionados pela ionosfera podem ter seus efeitos minimizados a partir de arquivos IONEX ou por meio de modelagem ionosférica. Portanto, nesta pesquisa, foram utilizados dados das estações da RBMC em diferentes regiões do Brasil no período de baixa e alta densidade de elétrons do pico solar 24 para avaliar o desempenho dos mapas ionosféricos, no posicionamento baseado em redes, disponibilizados por diversos centros (CODE, ESA, JPL, UPC e IGS), bem como os fornecidos pelo projeto MIMOSA, e também os modelos de Grade (AGUIAR, 2010) e estimativa de TEC. Para tal fim, foi adotado um sistema computacional desenvolvido na FCT/UNESP, denominado VRS-Unesp, que emprega o conceito de Estação Virtual. De acordo com os resultados obtidos, nota-se que não há um único mapa fornecido pelos centros de análise do IGS que melhor se enquadra a realidade brasileira, além disso, o desempenho do mapa depende das condições ionosféricas e, principalmente, da localização da estação. Além disso, verificou-se que a acurácia obtida pelo IONEX do projeto MIMOSA, pelo modelo de grade e de Estimativa do TEC que utilizam dados regionais e possuem maior resolução espacial e temporal, foram os que apresentaram os melhores resultados. Por fim foi avaliada a correlação entre a acurácia do posicionamento por ponto e o índice de cintilação S4, já que a ionosfera pode não apenas degradar a acurácia do posicionamento GNSS como reduzir sua disponibilidade, pois existe uma alta dependência entre perdas do sinal e irregularidades ionosféricas. Como resultado, considerando a análise de espaço-frequência em relação ao tempo pelo método coerência wavelets para avaliação da correlação da série, nota-se uma correlação no périodo do equinócio superior a 70%. / In order to take advantage from global positioning systems, new positioning methods have emerged and others have been improved. An important tendency in recent years has been the use of GNSS reference stations networks. But, using networks or other positioning methods an important factor to improve the positioning quality is related to atmospheric modeling. Special attention should be given to errors that occur due to ionosphere, it became the largest error source in GNSS positioning after disabling SA technique. Ionosphere error depends on signal frequency and Total Electron Content (TEC) in the ionospheric layer. TEC and consequently the ionospheric error varies regularly in time and space and they are affected by different sources like: sunspot number (solar cycle), season, local time, geographic position, geomagnetic activity, and others. Currently, the errors provided by the ionosphere can be minimized using IONEX files or models. Therefore, in this research, the RBMC stations data were used in different regions of Brazil in the period of low and high electron density of the cycle solar 24 to evaluate the performance of the ionospheric maps, in network-based positioning, available from several centers, as CODE , ESA, JPL, UPC and IGS, as well as those provided by the MIMOSA project, and also the Grade Models (AGUIAR, 2010) and TEC Estimates. For this, a computer system developed in FCT / UNESP has been adopted, RSV-Unesp that uses the concept of Virtual Station. According to the results, we note that there is not single map of IGS analysis centers that best fits the Brazilian reality, moreover, the map performance depends on the ionospheric conditions and, primarily, the station location. Moreover, it was found that the accuracy obtained by IONEX the MIMOSA project, the Grade Model and TEC estimation using regional data and have higher spatial and temporal resolution, showed the best results. Finally we evaluated the correlation between the accuracy of point positioning and scintillation index S4, since the ionosphere can not only degrade the accuracy of GNSS positioning as well as reduce its availability, because there is a high dependency between signal loss and ionospheric irregularities. As result, considering the space-frequency analysis with respect to time by the wavelet coherence method for evaluation of the correlation of the series, there is a correlation in the period of higher equinox to 70%. / FAPESP: 2014/03858-9

This thesis deals with DEA models (Data envelopment analysis), which are models for measuring the efficiency of peer decision making units (DMUs). First part focus on basic DEA models (CCR model, BCC model) and this part is extended by new CH-CCR model, which considering that the variables (inputs or outputs) are usually strongly correlated. In second part is included description of network-based models, which are afterwards applicated on data of FRS CR (Fire Rescue Service of the Czech Republic). Using this models is solved efficiency of FRS CR through the regions.

In today's fiercely competitive environment, not only in the tourism sector, we see a rise in the need to identify the weak and strong sides of the performance of the entire branch network of a travel agency, as well as of its components, the particular branches. This problem can be approached using the process of benchmarking. This method is currently widely used to compare the similarly behaving systems and processes in all areas of human activity. Data envelopment analysis models can be used as one of the methods of benchmarking. This thesis deals first with the theoretical description of the basic models of data envelopment analysis. It then moves on to concentrate on the description of the network-based models, which are then used in the practical part of the thesis. In this last part, it compares selected travel agency branches and identifies the deficiencies in particular areas of their performance.

Due to the tough competitions that exist today, most pharmaceutical distribution companies are in a continuous effort to increase their profits and reduce their costs. Actually, both shortage and surplus of goods can lead to loss of income for these companies. One of the problems in pharmaceutical distribution organizations which deal with public health and pharmaceutical products is how to control inventory levels by means of accurate sales prediction in order to prevent costs of excessive inventory also prevent losing their customers because of drug shortage. Accurate sales prediction is certainly a valuable management tool to meet the mentioned goals since this leads to improved customer service, also, reduced lost sales and costs. However, most pharmaceutical distribution companies in Iran are still using heuristic or traditional statistical techniques to make sales prediction for their products. Thus, the purpose of this research is to apply an innovative and reliable sales prediction method for pharmaceutical distribution companies.To make sales prediction for a pharmaceutical distribution company, we needed to have past sales records of each drug. Accordingly, we gathered sales data of three years from Pakhsh Hejrat Co. which is one of the leading pharmaceutical distributors in Iran. We chose neural networks as our basic tools for sales prediction since most traditional methods like ARIMA are incapable of modeling nonlinearities that exist in most real data; also, they need forecaster’s supervision for the parameter estimation phase. In fact, neural networks are versatile tools for sale prediction since estimation with neural networks can be automatized, and they have proved very effective in order to make prediction by handling non-linear input and output variables Due to the fact that we did not have enough past sales records of drug items, we came up to a new idea of grouping drugs to find group members and make use of co-members’ sales data for each other. Thus, we did a comprehensive network based analysis in order to find clique-sets and group members. Afterwards, we built sales forecasting models with three different approaches: a) ARIMA methodology for time series forecasting, b) Hybrid neural network approach for time series forecasting by means of each drug’s past recodes, and c) Hybrid neural network approach for time series forecasting by means of each drug’s past records and its group members’ past records. Our evaluations and results indicated that our new methodology (number 3 above) was the best methodology, and the weakest one was ARIMA model. / <p>Validerat; 20130304 (marikav)</p>