Proefschrift--Amsterdam. / Summary in Dutch. Bibliography: p. [101]-102.

In the context of the development of renewable energy sources in the U.K., and of the increase in anthropogenic atmospheric CO2, it is important to develop alternative ways of providing energy to the community. The shift to renewable sources of electricity comes to a cost: variable generation. At present, an important part of the renewable electricity capacity is being curtailed during low demand periods. One way to ensure that electricity supply matches demand is to store excess energy when it is available and deliver it when demand cannot be met by primary generation alone. Compressed Air Energy Storage (CAES) allows this storage. The aim of this project is to build upon existing knowledge on CAES using porous rocks (PM-CAES) to assess the technical feasibility for this storage technology to be developed offshore of the UK. The focus is on inter-seasonal storage. This assessment is undertaken by developing geological and power plant models to calculate the storage potential of offshore UK formations. Modelling of a conceptual aquifer air store enables approximations of the subsurface pressure response to CAES operations. These pressure changes are coupled with surface facilities models to provide estimates of both load/generation capacity and roundtrip efficiencies. Algebraic predictive models can be developed from the results of a sensitivity analysis of the store and plant idealised models. Screening of the CO2 Stored database, containing data on geological formations offshore of the UK (initially developed for CO2 storage), was then performed to estimate PM-CAES potential using the predictive models. The results suggest that there is substantial PM-CAES potential in the UK. Results indicate an energy storage potential in the range of 77-96 TWh, which can be released over 60 days. A geographic information system (GIS) study was then performed to identify the portion of the identified storage potential colocated with offshore windfarm. 19 TWh of the storage potential identified is colocated with windfarm and would be achievable at an average levelised cost of electricity of 0.70 £/kWh.

Comprender los fenómenos de nuestro planeta es esencial en diversos problemas de estimación y predicción, tales como minería, hidrología y extracción de petróleo. El principal inconveniente para resolver problemas inversos en geoestadística es la falta de datos, lo que imposibilita la generación de modelos estadísticos confiables. Debido a esto, es necesario incorporar información adicional para estimar las variables de interés en locaciones no medidas, como por ejemplo utilizando imágenes de entrenamiento. Esta Tesis aborda el problema de interpolación espacial de estructuras de canal basada en teorías de representación sparse de señales y estadísticos multipuntos. El trabajo se inspira en la teoría de Compressed Sensing (CS), la cual ofrece un nuevo paradigma de adquisición y reconstrucción de señales, y simulación multipunto (MPS), técnica que provee realizaciones realistas de diversas estructuras geológicas. Esta Tesis se motiva por estos dos enfoques, explorando la fusión de ambas fuentes de información, tanto geológica-estructural como la descomposición de dicha estructura en un dominio transformado. La principal contribución de este trabajo es el uso de algoritmos MPS para incorporar información a priori al algoritmo de reconstrucción, convirtiendo información geológica en información de señal. El algoritmo MPS es utilizado para estimar el soporte de la estructura subyacente, identificando las posiciones de los coeficientes transformados significativos y generando un ranking para los elementos de la base DCT (Discrete cosine transform). Este ranking es usado para la creación de una matriz de pesos, la cual impone una particular estructura directamente en el algoritmo de reconstrucción. Esta metodología es validada mediante el estudio de tres modelos de canal. Respecto a los resultados, primero se estudian diversas definiciones de la matriz de pesos para determinar la mejor configuración. Segundo, se estudia un enfoque multiescala de regularización sparse con el propósito de mejorar los desempeños clásicos de minimización en norma l1-ponderada. Con ello, se valida el uso de varias reconstrucciones a distintos niveles de escala para reducir los artefactos inducidos en la reconstrucción a imagen completa. Finalmente, el método es comparado con diversas técnicas de interpolación. De este análisis, se observa que el método propuesto supera a las técnicas convencionales de regularización en norma l1, tanto con pesos como sin ponderadores, al igual que el algoritmo multipunto utilizado. Esto valida la hipótesis sobre la complementariedad de las informaciones de patrones estadísticos y estructuras de señal. Para cada modelo, el método es capaz de inferir la estructura predominante de canal, incluso en un escenario inferior al 1% de datos adquiridos. Finalmente, se han identificado algunas posibles áreas de investigación futura. Algunas de estas posibles aristas son: CS binario, dada la naturaleza binaria de los modelos estudiados; algoritmos greedy para la extensión al análisis 3D; y métodos adaptativos de CS para resolver simultáneamente el problema de localización de sondajes y reconstrucción de señales.

Flaring of associated gas, found during petroleum exploration and production in Nigeria, results in substantial environmental degradation, which endangers sustainable development and exposes the population to health hazards. In addition, it results in significant economic losses, especially from the opportunity cost of the disposed natural gas (NG). As part of the many initiatives to abate flaring and harness NG resources, the Nigerian government proposed the use of compressed natural gas (CNG) as an automotive fuel in 1997, but progress has been slow. This study investigates the barriers to use of CNG as an automotive fuel in Nigeria and how these can be overcome. It identified, validated, prioritized and built consensus on 29 barriers and 25 policy recommendations, using a combination of case study of selected countries, semi-structured interviews and a Delphi survey among participants who are key stakeholders in the energy and transportation sectors. Major hindrances identified include the absence of market coordination; lack of transparency and accountability; inexperience of the population with gas usage; lack of public awareness on the benefits of NG; artificial distortion of the economic benefits of CNG due to the subsidy on gasoline; focus on export market development to the detriment of the domestic market; absence of regulatory standards; poor infrastructure; and an old and dilapidated national vehicle fleet. There was no convergence on the impact of insecurity of human and material resources caused by militancy and pipeline vandalism in the oil producing areas, despite widespread views of the negative effect on the oil and gas industry generally. Based on the consensus built among study participants, the study recommends 12 policy interventions, which might stimulate growth in the use of CNG as automotive fuel; these comprise specific energy market reforms, fiscal and operational incentives, transportation sector reforms and the creation/building of public awareness.

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2016

abstract: Compressed sensing (CS) is a novel approach to collecting and analyzing data of all types. By exploiting prior knowledge of the compressibility of many naturally-occurring signals, specially designed sensors can dramatically undersample the data of interest and still achieve high performance. However, the generated data are pseudorandomly mixed and must be processed before use. In this work, a model of a single-pixel compressive video camera is used to explore the problems of performing inference based on these undersampled measurements. Three broad types of inference from CS measurements are considered: recovery of video frames, target tracking, and object classification/detection. Potential applications include automated surveillance, autonomous navigation, and medical imaging and diagnosis. Recovery of CS video frames is far more complex than still images, which are known to be (approximately) sparse in a linear basis such as the discrete cosine transform. By combining sparsity of individual frames with an optical flow-based model of inter-frame dependence, the perceptual quality and peak signal to noise ratio (PSNR) of reconstructed frames is improved. The efficacy of this approach is demonstrated for the cases of \textit{a priori} known image motion and unknown but constant image-wide motion. Although video sequences can be reconstructed from CS measurements, the process is computationally costly. In autonomous systems, this reconstruction step is unnecessary if higher-level conclusions can be drawn directly from the CS data. A tracking algorithm is described and evaluated which can hold target vehicles at very high levels of compression where reconstruction of video frames fails. The algorithm performs tracking by detection using a particle filter with likelihood given by a maximum average correlation height (MACH) target template model. Motivated by possible improvements over the MACH filter-based likelihood estimation of the tracking algorithm, the application of deep learning models to detection and classification of compressively sensed images is explored. In tests, a Deep Boltzmann Machine trained on CS measurements outperforms a naive reconstruct-first approach. Taken together, progress in these three areas of CS inference has the potential to lower system cost and improve performance, opening up new applications of CS video cameras. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

Made available in DSpace on 2014-06-12T15:52:41Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / A busca por novas funcionalidades no que diz respeito a melhoria da confiabilidade dos sistemas eletrônicos e também a necessidade de gerir o tempo gasto durante o teste faz do mecanismo Built-in-Self-Test (BIST) um característica promissora a ser integrada no fluxo atual de desenvolvimento de Circuitos Integrados (IC). Existem vários tipos de BIST: Memories BIST, Logical BIST (LBIST) e também alguns mecanismos usados para teste as partes analógicas do circuito. O LBIST tradicional usa um hardware on-chip para gerar todos os padrões de teste com um gerador pseudo aleatório (PRPG) e analisa a assinatura de saída gerada por um registrador de assinatura de múltipla entradas (MISR). Essa abordagem requer a inserção de pontos de teste extras or armazenagem de informação fora do chip que tornará possível alcançar uma cobertura de teste > 98%. Também a geração de todos os estímulos de teste implica no sacrifício no tempo aplicação do teste, o qual pode ser aceitável para pequenos sistemas executarem auto-teste durante a inicialização do sistema mas pode tornasse um aspecto negativo quando testando System-on-chip (SOC) ICs. O fluxo corrente de desenvolvimento de um IC insere scan chains e gera automaticamente padrões de teste de scan para alcançar uma alta cobertura para o teste de manufatura. Técnicas de compressão de dados provaram ser muito úteis para reduzir o custo de teste enquanto reduzem o volume de dados e o tempo de aplicação dos testes. Esse trabalho propõe o reuso de padrões de teste comprimidos usados durante o teste de manufatura para implementar um LBIST com objetivo de testar o circuito quando ele já está em campo. O mecanismo LBIST proposto objetiva descobrir defeitos que podem ocorrer devido ao desgasto do circuito. Uma arquitetura e um fluxo de desenvolvimento semi-automático do mecanísmo LBIST baseado em padrões de teste de scan são propostos e validados usando um SoC real como caso de teste

The burgeoning field of nanotechnology motivates comprehensive elucidation of nanoscale materials. This thesis addresses transmission electron microscope characterisation of nanoparticle morphology, concerning specifically the crystal- lographic status of novel intermetallic GaPd2 nanocatalysts and advancement of electron tomographic methods for high-fidelity three-dimensional analysis. Going beyond preceding analyses, high-resolution annular dark-field imaging is used to verify successful nano-sizing of the intermetallic compound GaPd2. It also reveals catalytically significant and crystallographically intriguing deviations from the bulk crystal structure. So-called ‘non-crystallographic’ five-fold twinned nanoparticles are observed, adding a new perspective in the long standing debate over how such morphologies may be achieved. The morphological complexity of the GaPd2 nanocatalysts, and many cognate nanoparticle systems, demands fully three-dimensional analysis. It is illustrated how image processing techniques applied to electron tomography reconstructions can facilitate more facile and objective quantitative analysis (‘nano-metrology’). However, the fidelity of the analysis is limited ultimately by artefacts in the tomographic reconstruction. Compressed sensing, a new sampling theory, asserts that many signals can be recovered from far fewer measurements than traditional theories dictate are necessary. Compressed sensing is applied here to electron tomographic reconstruction, and is shown to yield far higher fidelity reconstructions than conventional algorithms. Reconstruction from extremely limited data, more robust quantitative analysis and novel three-dimensional imaging are demon- strated, including the first three-dimensional imaging of localised surface plasmon resonances. Many aspects of transmission electron microscopy characterisation may be enhanced using a compressed sensing approach.

This thesis presents an investigation of the influence of turbocharging on the performance and combustion behaviour of a dual fuelled, spark-ignition engine fuelled with natural gas and gasoline. The investigation was carried out using a combination of experimental and analytical methods. The experimental data was obtained from an instrumented, four cylinder, Toyota engine mounted in a test cell. An electrically driven Roots blower was used to provide compressed air to the engine, and a restriction was placed in the exhaust pipe to simulate the effects of an exhaust-driven turbine. Cylinder pressure data were recorded and analysed using a computer routine in order to provide information on mass burning rates and burning velocities. Computer routines were also developed to simulate the compression, combustion and expansion processes in the engine. It was found that the laminar burning velocity of natural gas is 50% to 60% lower than gasoline, under engine-like conditions of temperature and pressure. Mass-burning rate analyses of measured cylinder pressure data showed that the lower burning velocity of natural gas has its greatest influence during the ignition delay period (up to 1% mass burned) and that it can cause increases in ignition delay of between 50% and 100% relative to gasoline. It was observed that the low burning velocity of natural gas also affects the main combustion period, but to a much lesser extent, increasing it by up to 10% relative to gasoline. It was concluded that the main combustion period is dominated by turbulence effects and that it is relatively unaffected by variations in fuel type, air/fuel ratio or boost pressure. Results from the engine tests and simulation program indicated that it is possible to recover the power loss experienced by an engine running on natural gas by boosting the intake pressure to 3 psig (20 kPa) above that provided when the engine is running on gasoline. This increase in boost pressure does not significantly reduce the efficiency or raise the specific fuel consumption. It was found, however, that the peak cylinder pressures attained can be as much as 20% higher on natural gas than on gasoline at the same power level. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23073号 / 医博第4700号 / 新制||医||1049(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 溝脇 尚志, 教授 黒田 知宏, 教授 増永 慎一郎 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM