• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 17
  • 10
  • 5
  • 2
  • 1
  • 1
  • 1
  • Tagged with
  • 43
  • 43
  • 12
  • 6
  • 6
  • 5
  • 5
  • 5
  • 5
  • 4
  • 4
  • 4
  • 4
  • 4
  • 3
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

An investigation into the professional development of teachers at higher education institutions in the United Arab Emirates

Abu Nasra, Juma A. January 2000 (has links)
No description available.
2

Evolution Management in NoSQL Document Databases / Evolution Management in NoSQL Document Databases

Vavrek, Michal January 2018 (has links)
NoSQL databases are widely used for many applications as a technology for data storage, and their usage and popularity rises. The first aim of the thesis is to research the existing approaches and technologies for schema evolution in NoSQL databases. Next, we introduce an approach for schema evolution in multi-model databases with a unified interface for the most common data models. The proposed approach is easy to use and covers the common migration scenarios. We have also implemented a prototype, optimized its read/write operations, and demonstrated its properties on real-world data. 1
3

Multi-perspective, Multi-modal Image Registration and Fusion

Belkhouche, Mohammed Yassine 08 1900 (has links)
Multi-modal image fusion is an active research area with many civilian and military applications. Fusion is defined as strategic combination of information collected by various sensors from different locations or different types in order to obtain a better understanding of an observed scene or situation. Fusion of multi-modal images cannot be completed unless these two modalities are spatially aligned. In this research, I consider two important problems. Multi-modal, multi-perspective image registration and decision level fusion of multi-modal images. In particular, LiDAR and visual imagery. Multi-modal image registration is a difficult task due to the different semantic interpretation of features extracted from each modality. This problem is decoupled into three sub-problems. The first step is identification and extraction of common features. The second step is the determination of corresponding points. The third step consists of determining the registration transformation parameters. Traditional registration methods use low level features such as lines and corners. Using these features require an extensive optimization search in order to determine the corresponding points. Many methods use global positioning systems (GPS), and a calibrated camera in order to obtain an initial estimate of the camera parameters. The advantages of our work over the previous works are the following. First, I used high level-features, which significantly reduce the search space for the optimization process. Second, the determination of corresponding points is modeled as an assignment problem between a small numbers of objects. On the other side, fusing LiDAR and visual images is beneficial, due to the different and rich characteristics of both modalities. LiDAR data contain 3D information, while images contain visual information. Developing a fusion technique that uses the characteristics of both modalities is very important. I establish a decision-level fusion technique using manifold models.
4

Aprendizado por reforço em ambientes não-estacionários

Silva, Bruno Castro da January 2007 (has links)
Neste trabalho apresentamos o RL-CD (Reinforcement Learning with Context Detection), um método desenvolvido a fim de lidar com o problema do aprendizado por reforço (RL) em ambientes não-estacionários. Embora os métodos existentes de RL consigam, muitas vezes, superar a não-estacionariedade, o fazem sob o inconveniente de terem de reaprender políticas que já haviam sido calculadas, o que implica perda de desempenho durante os períodos de readaptação. O método proposto baseia-se em um mecanismo geral através do qual são criados, atualizados e selecionados um dentre vários modelos e políticas parciais. Os modelos parciais do ambiente são incrementalmente construídos de acordo com a capacidade do sistema de fazer predições eficazes. A determinação de tal medida de eficácia baseia-se no cálculo de qualidades globais para cada modelo, as quais refletem o ajuste total necessário para tornar cada modelo coerente com as experimentações reais. Depois de apresentadas as bases teóricas necessárias para fundamentar o RL-CD e suas equações, são propostos e discutidos um conjunto de experimentos que demonstram sua eficiência, tanto em relação a estratégias clássicas de RL quanto em comparação a algoritmos especialmente projetados para lidar com cenários não-estacionários. O RL-CD é comparado com métodos reconhecidos na área de aprendizado por reforço e também com estratégias RL multi-modelo. Os resultados obtidos sugerem que o RLCD constitui uma abordagem eficiente para lidar com uma subclasse de ambientes nãoestacionários, especificamente aquela formada por ambientes cuja dinâmica é corretamente representada por um conjunto finito de Modelos de Markov estacionários. Por fim, apresentamos a análise teórica de um dos parâmetros mais importantes do RL-CD, possibilitada pela aproximação empírica de distribuições de probabilidades via métodos de Monte Carlo. Essa análise permite que os valores ideais de tal parâmetro sejam calculados, tornando assim seu ajuste independente da aplicação específica sendo estudada. / In this work we introduce RL-CD (Reinforcement Learning with Context Detection), a novel method for solving reinforcement learning (RL) problems in non-stationary environments. In face of non-stationary scenarios, standard RL methods need to continually readapt themselves to the changing dynamics of the environment. This causes a performance drop during the readjustment phase and implies the need for relearning policies even for dynamics which have already been experienced. RL-CD overcomes these problems by implementing a mechanism for creating, updating and selecting one among several partial models of the environment. The partial models are incrementally built according to the system’s capability of making predictions regarding a given sequence of observations. First, we present the motivations and the theorical basis needed to develop the conceptual framework of RL-CD. Afterwards, we propose, formalize and show the efficiency of RL-CD both in a simple non-stationary environment and in a noisy scenarios. We show that RL-CD performs better than two standard reinforcement learning algorithms and that it has advantages over methods specifically designed to cope with non-stationarity. Finally, we present the theoretical examination of one of RL-CD’s most important parameters, made possible by means of the analysis of probability distributions obtained via Monte Carlo methods. This analysis makes it possible for us to calculate the optimum values for this parameter, so that its adjustment can be performed independently of the scenario being studied.
5

Aprendizado por reforço em ambientes não-estacionários

Silva, Bruno Castro da January 2007 (has links)
Neste trabalho apresentamos o RL-CD (Reinforcement Learning with Context Detection), um método desenvolvido a fim de lidar com o problema do aprendizado por reforço (RL) em ambientes não-estacionários. Embora os métodos existentes de RL consigam, muitas vezes, superar a não-estacionariedade, o fazem sob o inconveniente de terem de reaprender políticas que já haviam sido calculadas, o que implica perda de desempenho durante os períodos de readaptação. O método proposto baseia-se em um mecanismo geral através do qual são criados, atualizados e selecionados um dentre vários modelos e políticas parciais. Os modelos parciais do ambiente são incrementalmente construídos de acordo com a capacidade do sistema de fazer predições eficazes. A determinação de tal medida de eficácia baseia-se no cálculo de qualidades globais para cada modelo, as quais refletem o ajuste total necessário para tornar cada modelo coerente com as experimentações reais. Depois de apresentadas as bases teóricas necessárias para fundamentar o RL-CD e suas equações, são propostos e discutidos um conjunto de experimentos que demonstram sua eficiência, tanto em relação a estratégias clássicas de RL quanto em comparação a algoritmos especialmente projetados para lidar com cenários não-estacionários. O RL-CD é comparado com métodos reconhecidos na área de aprendizado por reforço e também com estratégias RL multi-modelo. Os resultados obtidos sugerem que o RLCD constitui uma abordagem eficiente para lidar com uma subclasse de ambientes nãoestacionários, especificamente aquela formada por ambientes cuja dinâmica é corretamente representada por um conjunto finito de Modelos de Markov estacionários. Por fim, apresentamos a análise teórica de um dos parâmetros mais importantes do RL-CD, possibilitada pela aproximação empírica de distribuições de probabilidades via métodos de Monte Carlo. Essa análise permite que os valores ideais de tal parâmetro sejam calculados, tornando assim seu ajuste independente da aplicação específica sendo estudada. / In this work we introduce RL-CD (Reinforcement Learning with Context Detection), a novel method for solving reinforcement learning (RL) problems in non-stationary environments. In face of non-stationary scenarios, standard RL methods need to continually readapt themselves to the changing dynamics of the environment. This causes a performance drop during the readjustment phase and implies the need for relearning policies even for dynamics which have already been experienced. RL-CD overcomes these problems by implementing a mechanism for creating, updating and selecting one among several partial models of the environment. The partial models are incrementally built according to the system’s capability of making predictions regarding a given sequence of observations. First, we present the motivations and the theorical basis needed to develop the conceptual framework of RL-CD. Afterwards, we propose, formalize and show the efficiency of RL-CD both in a simple non-stationary environment and in a noisy scenarios. We show that RL-CD performs better than two standard reinforcement learning algorithms and that it has advantages over methods specifically designed to cope with non-stationarity. Finally, we present the theoretical examination of one of RL-CD’s most important parameters, made possible by means of the analysis of probability distributions obtained via Monte Carlo methods. This analysis makes it possible for us to calculate the optimum values for this parameter, so that its adjustment can be performed independently of the scenario being studied.
6

Aprendizado por reforço em ambientes não-estacionários

Silva, Bruno Castro da January 2007 (has links)
Neste trabalho apresentamos o RL-CD (Reinforcement Learning with Context Detection), um método desenvolvido a fim de lidar com o problema do aprendizado por reforço (RL) em ambientes não-estacionários. Embora os métodos existentes de RL consigam, muitas vezes, superar a não-estacionariedade, o fazem sob o inconveniente de terem de reaprender políticas que já haviam sido calculadas, o que implica perda de desempenho durante os períodos de readaptação. O método proposto baseia-se em um mecanismo geral através do qual são criados, atualizados e selecionados um dentre vários modelos e políticas parciais. Os modelos parciais do ambiente são incrementalmente construídos de acordo com a capacidade do sistema de fazer predições eficazes. A determinação de tal medida de eficácia baseia-se no cálculo de qualidades globais para cada modelo, as quais refletem o ajuste total necessário para tornar cada modelo coerente com as experimentações reais. Depois de apresentadas as bases teóricas necessárias para fundamentar o RL-CD e suas equações, são propostos e discutidos um conjunto de experimentos que demonstram sua eficiência, tanto em relação a estratégias clássicas de RL quanto em comparação a algoritmos especialmente projetados para lidar com cenários não-estacionários. O RL-CD é comparado com métodos reconhecidos na área de aprendizado por reforço e também com estratégias RL multi-modelo. Os resultados obtidos sugerem que o RLCD constitui uma abordagem eficiente para lidar com uma subclasse de ambientes nãoestacionários, especificamente aquela formada por ambientes cuja dinâmica é corretamente representada por um conjunto finito de Modelos de Markov estacionários. Por fim, apresentamos a análise teórica de um dos parâmetros mais importantes do RL-CD, possibilitada pela aproximação empírica de distribuições de probabilidades via métodos de Monte Carlo. Essa análise permite que os valores ideais de tal parâmetro sejam calculados, tornando assim seu ajuste independente da aplicação específica sendo estudada. / In this work we introduce RL-CD (Reinforcement Learning with Context Detection), a novel method for solving reinforcement learning (RL) problems in non-stationary environments. In face of non-stationary scenarios, standard RL methods need to continually readapt themselves to the changing dynamics of the environment. This causes a performance drop during the readjustment phase and implies the need for relearning policies even for dynamics which have already been experienced. RL-CD overcomes these problems by implementing a mechanism for creating, updating and selecting one among several partial models of the environment. The partial models are incrementally built according to the system’s capability of making predictions regarding a given sequence of observations. First, we present the motivations and the theorical basis needed to develop the conceptual framework of RL-CD. Afterwards, we propose, formalize and show the efficiency of RL-CD both in a simple non-stationary environment and in a noisy scenarios. We show that RL-CD performs better than two standard reinforcement learning algorithms and that it has advantages over methods specifically designed to cope with non-stationarity. Finally, we present the theoretical examination of one of RL-CD’s most important parameters, made possible by means of the analysis of probability distributions obtained via Monte Carlo methods. This analysis makes it possible for us to calculate the optimum values for this parameter, so that its adjustment can be performed independently of the scenario being studied.
7

A multi-model ensemble system for short-range weather prediction in South Africa

Landman, Stephanie 06 February 2012 (has links)
Predicting the location and timing of rainfall events has important social and economic impacts. It is also important to have the ability to predict the amount of rainfall accurately. In operational centres forecasters use deterministic model output data as guidance for a subjective probabilistic rainfall forecast. The aim of this research is to determine the skill in an objective multi-model, multi-institute objective probabilistic forecast system. This was done by obtaining the rainfall forecast of two high-resolution regional models operational in South Africa. The first model is the Unified Model (UM) which is operational at the South African Weather Service. The UM contributed three members which differ in physics, data assimilation techniques and horisontal resolution. The second model is the Conformal-Cubic Atmospheric Model (CCAM) which is operational at the Council for Scientific and Industrial Research which in turn contributed two members to the ensemble system differing in horisontal resolution. A single-model ensemble was constructed for the UM and CCAM models respectively with each of the individual members having equal weights. The UM and CCAM single-model ensemble prediction models have been used in turn to construct a multi-model ensemble prediction system, using simple un-weighted averaging. The multi-model system was used to predict the 24-hour rainfall totals for three austral summer half-year seasons of 2006/07 to 2008/09. The forecast of this system was rigorously tested using observed rainfall data for the same period. From the multi-model system it has been found that the probabilistic forecast has good significant skill in predicting rainfall. The multi-model system proved to have skill and shows discrimination between events and non-events. This study has shown that it is possible to make an objective probabilistic rainfall forecast by constructing a multi-model, multi-institute system with high resolution regional models currently operational in South Africa. Thus, probabilistic rainfall forecasts with usable skill can be made with the use of a multi-model short-range ensemble prediction system over the South African domain. Such a system is not currently operational in South Africa. Copyright / Dissertation (MSc)--University of Pretoria, 2012. / Geography, Geoinformatics and Meteorology / Unrestricted
8

Identifying specific line balancing criteria for an efficient line balancing software : A case Study

Dhanpal Harinath, Shravan, Siddique, Shakeel January 2018 (has links)
For any business, surviving in a competitive market while maintaining all the operational performance indices up to mark is very crucial. There are several theories and techniques to improve the efficiency of the operational performances. Line balancing is one of those well practiced techniques used daily in most of the industries. Line balancing helps balance the assembly lines with regards to man, machine, takt times, etc. This thesis research was done with Electrolux laundry systems, Ljungby in Sweden. With the varying customer demands the case company was balancing its line manually using basic methods. As a part of lean development schemes, Electrolux Ljungby, wanted to transform the line balancing techniques from manual to a fully automated software. The purpose of this research is to determine the company-specific line balancing criteria which should be considered before performing line balancing. This research furthermore lays out a guideline to follow a smooth transition from the manual system of LB to an automated software by concluding the features the software must handle to perform the LB according to required objectives. A case study approach was utilized to collect all the required data to achieve the results. Using the data collection techniques such as interviews, observations and historical analysis we arrived at the data required to design the guidelines with regards to line balancing software features.  The findings suggest that the desired line balancing constraints which are very important in the multi model single sided straight-line balancing problems are flow of materials, assembly precedence, physical constraints, product demand, bill of materials, restricted processes, man power and desired line balancing objectives. Keeping these constraints into consideration the features which are desired in an onlooking line balancing software are the Integration of data and documents/ maximum control, mixed model and option intelligence and analysis, multiple resources, smart variant management, scenario management, yamazumi chart, constraints and reporting tabs. The findings of this thesis can be used as guidelines by any manufacturing industry while they consider buying a new software which can handle Line balancing problems. This research is one of its kind which talks purely about the constraints and desired features only in a specific line balancing scenario. Practitioners can use this as a base for conducting further research on constraints and features pertaining to it, for different line balancing scenarios.
9

Multi-Model Snowflake Schema Creation

Gruenberg, Rebecca 25 April 2022 (has links)
No description available.
10

Statistical methods for quantifying uncertainty in climate projections from ensembles of climate models

Sansom, Philip George January 2014 (has links)
Appropriate and defensible statistical frameworks are required in order to make credible inferences about future climate based on projections derived from multiple climate models. It is shown that a two-way analysis of variance framework can be used to estimate the response of the actual climate, if all the climate models in an ensemble simulate the same response. The maximum likelihood estimate of the expected response provides a set of weights for combining projections from multiple climate models. Statistical F tests are used to show that the differences between the climate response of the North Atlantic storm track simulated by a large ensemble of climate models cannot be distinguished from internal variability. When climate models simulate different responses, the differences between the re- sponses represent an additional source of uncertainty. Projections simulated by climate models that share common components cannot be considered independent. Ensemble thinning is advocated in order to obtain a subset of climate models whose outputs are judged to be exchangeable and can be modelled as a random sample. It is shown that the agreement between models on the climate response in the North Atlantic storm track is overestimated due to model dependence. Correlations between the climate responses and historical climates simulated by cli- mate models can be used to constrain projections of future climate. It is shown that the estimate of any such emergent relationship will be biased, if internal variability is large compared to the model uncertainty about the historical climate. A Bayesian hierarchical framework is proposed that is able to separate model uncertainty from internal variability, and to estimate emergent constraints without bias. Conditional cross-validation is used to show that an apparent emergent relationship in the North Atlantic storm track is not robust. The uncertain relationship between an ensemble of climate models and the actual climate can be represented by a random discrepancy. It is shown that identical inferences are obtained whether the climate models are treated as predictors for the actual climate or vice versa, provided that the discrepancy is assumed to be sym- metric. Emergent relationships are reinterpreted as constraints on the discrepancy between the expected response of the ensemble and the actual climate response, onditional on observations of the recent climate. A simple method is proposed for estimating observation uncertainty from reanalysis data. It is estimated that natural variability accounts for 30-45% of the spread in projections of the climate response in the North Atlantic storm track.

Page generated in 0.0667 seconds