Dynamic Optimization Algorithms for Baseload Power Plant Cycling under Variable Renewable Energy

The growing deployment of variable renewable energy (VRE) sources, such as wind and solar, is mainly due to the decline in the cost of renewable technologies and the increase of societal and cultural pressures. Solar and wind power generation are also known to have zero marginal costs and fuel emissions during dispatch. Thereby, the VRE from these sources should be prioritized when available. However, the rapid deployment of VRE has heightened concerns regarding the challenges in the integration between fossil-fueled and renewable energy systems. The high variability introduced by the VRE as well as the limited alignment between demand and wind/solar power generation led to the increased need of dispatchable energy sources such as baseload natural gas- and coal-fired power plants to cycle their power outputs more often to reliably supply the net load. The increasing power plant cycling can introduce unexpected inefficiencies into the system that potentially incur higher costs, emissions, and wear-and-tear, as the power plants are no longer operating at their optimal design points. In this dissertation, dynamic optimization algorithms are developed and implemented for baseload power plant cycling under VRE penetration. Specifically, two different dynamic optimization strategies are developed for the minute and hourly time scales of grid operation. The minute-level strategy is based on a mixed-integer linear programming (MILP) formulation for dynamic dispatch of energy systems, such as natural gas- and coal-fired power plants and sodium sulfur batteries, under VRE while considering power plant equipment health-related constraints. The hourly-level strategy is based on a Nonlinear Multi-objective dynamic real-time Predictive Optimization (NMPO) implemented in a supercritical pulverized coal-fired (SCPC) power plant with a postcombustion carbon capture system (CCS), considering economic and environmental objectives. Different strategies are employed and explored to improve computational tractability, such as mathematical reformulations, automatic differentiation (AD), and parallelization of a metaheuristic particle swarm optimization (PSO) component. The MILP-based dynamic dispatch framework is used to simulate case studies considering different loads and renewable penetration levels for a suite of energy systems. The results show that grid flexibility is mostly provided by the natural gas power plant, while the batteries are used sparingly. Additionally, considering the post-optimization equivalent carbon analysis, the environmental performance is intrinsically connected to grid flexibility and the level of VRE penetration. The stress results reinforce the necessity of further considering and including equipment health-related constraints during dispatch. The results of the NMPO successfully implemented for a large-scale SCPC-CCS show that the optimal compromise is automatically chosen from the Pareto front according to a set of weights for the objectives with minimal interaction between the framework and the decision maker. They also indicate that to setup the optimization thresholds and constraints, knowledge of the power system operations is essential. Finally, the market and carbon policies have an impact on the optimal compromise between the economic and environmental objectives

SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art

Applications of a Forward-Looking Interferometer for the On-board Detection of Aviation Weather Hazards

The Forward-Looking Interferometer (FLI) is a new instrument concept for obtaining measurements of potential weather hazards to alert flight crews. The FLI concept is based on high-resolution Infrared (IR) Fourier Transform Spectrometry (FTS) technologies that have been developed for satellite remote sensing, and which have also been applied to the detection of aerosols and gases for other purposes. It is being evaluated for multiple hazards including clear air turbulence (CAT), volcanic ash, wake vortices, low slant range visibility, dry wind shear, and icing, during all phases of flight. Previous sensitivity and characterization studies addressed the phenomenology that supports detection and mitigation by the FLI. Techniques for determining the range, and hence warning time, were demonstrated for several of the hazards, and a table of research instrument parameters was developed for investigating all of the hazards discussed above. This work supports the feasibility of detecting multiple hazards with an FLI multi-hazard airborne sensor, and for producing enhanced IR images in reduced visibility conditions; however, further research must be performed to develop a means to estimate the intensities of the hazards posed to an aircraft and to develop robust algorithms to relate sensor measurables to hazard levels. In addition, validation tests need to be performed with a prototype system

Pulse Height Spectra Analysis of a Neutron Energy Tuning Assembly

An energy tuning assembly (ETA) was previously designed and built for the purpose of irradiating samples with a combination of a thermonuclear and a prompt fission neutron spectrum. Initial research was performed to characterize the performance of the ETA at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron using 33 MeV deuteron breakup on tantalum as the neutron source. This research analyzes detector responses collected from three EJ-309 detectors used to characterize the ETA generated neutron field. A signal processing chain was developed to reduce the full waveform data into a pulse height spectrum. The primary goal was to develop a processing chain that optimized pulse shape discrimination performance to improve the discrimination between neutrons and gammas. It was found that the processing chain developed allowed for greater flexibility in determining the PSD parameters, which allowed for a greater degree of particle discrimination at low pulse heights

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Mass spectrometry data mining for cancer detection

Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.Mathematics, Department o

The magmatic crust of Vesta

Les astéroïdes Cérès et Vesta ont motivé la mission spatiale Dawn parce qu'ils représentent deux embryons planétaires différents restés relativement intacts depuis leur formation. Vesta est large- ment considéré comme le corps parent des météorites HED témoins d'une activité magmatique probablement due à la présence de l'isotope radioactif 26Al qui était suffisamment abondant pour permettre la fusion interne des corps rocheux primitifs. La composition d'une surface planétaire peut être mesurée grâce à l'analyse des rayons gammas qu'elle produit. Pour la sonde Dawn cela est rendu possible par l'instrument GRaND et la scintillation d'un cristal de BGO. Cette thèse présente l'analyse des spectres gammas de Vesta par deux outils de séparation aveugle de source: l'analyse en composantes indépendantes (ICA) et la factorisation en matrice non-négative (NMF). Ces méthodes sont aussi appliquées à un jeu de données lunaire comparable et déjà bien interprété. Des spectres synthétiques lunaires permettent de tester ICA et NMF. La séparation de spectres élémentaires s'avère délicate même si on peut distinguer les éléments K, Th et Fe en raison des propriétés statistiques de leur signaux sources plus favorables. On mesure la sensibilité d'ICA-NMF à la variabilité chimique de la surface pour des Lunes artificielles, ce qui permet d'expliquer l'absence de séparation d'un signal élémentaire clair dans le cas de Vesta. Malgré les observations de la sonde Dawn et le nombre important d'informations fournies par les HED, il n'y a pas de consensus sur la formation des HED. On met souvent en avant l'existence d'un océan magmatique global sur Vesta, alors que la migration de la principale source chaleur, contenue dans le premier minéral fondu, le plagioclase, ne permet pas la fusion totale. On met en oeuvre un modèle de migration des magmas, basé sur les équations de la compaction. On adapte ce modèle en utilisant un diagramme d'équilibre de phase olivine-anorthite-quartz. Cela permet de calculer l'évolution de la minéralogie en fonction du temps et de la profondeur. Les résultats montrent que les eucrites et les diogénites pourraient être une caractéristique commune des gros corps accrétés tôt dans l'histoire du système solaire.Asteroids Vesta and Ceres motivated the space mission Dawn because they represent two different planetary embryos that remained relatively intact since their formation. Vesta is broadly considered as the parent body of the HED meteorites suite that are witnesses of a magmatic activity probably due to the presence of the radioactive isotope 26 Al which was present in significant amount to cause internal melting of primitive rocky bodies. The composition of a planetary surface can be quantified through the analysis of the gamma rays it produces. This is made possible for the Dawn spacecraft by the instrument GRaND and the scintillation of a BGO crystal. This thesis presents the analysis of gamma ray spectra from Vesta by two blind source separation methods: the independent component analysis and the non negative matrix factorization. These methods are also applied to an equivalent lunar dataset already well interpreted. Lunar synthetic spectra are used to test ICA and NMF. The separation of elementary spectra is delicate although K, Th and Fe can be discriminated due to the more favorable statistical properties of their source signals. The sensitivity of separation to the chemical variability is assessed based on artificial lunar spectra, which allows to explain the lack of separation of a clear elemental signal in the case of Vesta. Despite the observations of Dawn and the important collection of HED data, there is no consensus on the conditions of the vestan magmatism. A global magma ocean is often put forward, whereas the migration of the heat source, contained in the easiest mineral to melt, plagioclase, does not allow it. A model of melt migration is implemented, based on two-phase flow equations. This model is combined with the olivine-anorthite-quartz equilibrium phase diagram. This allows to predict the mineralogy as a function of depth and time. Results obtained show that eucrites and diogenites may be a common feature of large bodies accreted early in solar system history

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

peak picking und map alignment

We study two fundamental processing steps in mass spectrometric data analysis from a theoretical and practical point of view. For the detection and extraction of mass spectral peaks we developed an efficient peak picking algorithm that is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In two optional stages, highly overlapping peaks can be separated or all peak parameters can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques, our algorithm is able to separate overlapping peaks of multiply charged peptides in LC-ESI-MS data of low resolution. Furthermore, applied to high-quality MALDI-TOF spectra it yields a high degree of accuracy and precision and compares very favorably with the algorithms supplied by the vendor of the mass spectrometers. On the high-resolution MALDI spectra as well as on the low-resolution LC-MS data set, our algorithm achieves a fast runtime of only a few seconds. Another important processing step that can be found in every typical protocol for labelfree quantification is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a multiple alignment of the LC-MS maps needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments. For the first time we formally define the multiple LC-MS raw and feature map alignment problem using our own distance function for LC-MS maps. Furthermore, we present a solution to this problem. Our novel algorithm aligns LC-MS samples and matches corresponding ion species across samples. In a first step, it uses an adapted pose clustering approach to efficiently superimpose raw maps as well as feature maps. This is done in a star-wise manner, where the elements of all maps are transformed onto the coordinate system of a reference map. To detect and combine corresponding features in multiple feature maps into a so-called consensus map, we developed an additional step based on techniques from computational geometry. We show that our alignment approach is fast and reliable as compared to five other alignment approaches. Furthermore, we prove its robustness in the presence of noise and its ability to accurately align samples with only few common ion species.Im Rahmen dieser Arbeit beschäftigen wir uns mit peak picking und map alignment; zwei fundamentalen Prozessierungsschritten bei der Analyse massenspektrometrischer Signale. Im Gegensatz zu vielen anderen peak picking Ansätzen haben wir einen Algorithmus entwickelt, der alle relevanten Informationen aus den massenspektrometrischen Peaks extrahiert und unabhängig von der analytischen Fragestellung und dem MS Instrument ist. Im ersten Teil dieser Arbeit stellen wir diesen generischen peak picking Algorithmus vor. Für die Detektion der Peaks nutzen wir die Multiskalen-Natur von MS Messungen und erlauben mit einem Wavelet-basierten Ansatz auch das Prozessieren von stark verrauschten und Baseline-behafteten Massenspektren. Neben der exakten m/z Position und dem FWHM Wert eines Peaks werden seine maximale Intensität sowie seine Gesamtintensität bestimmt. Mithilfe des Fits einer analytischen Peakfunktion extrahieren wir außerdem zusätzliche Informationen über die Peakform. Zwei weiterere optionale Schritte ermöglichen zum einen die Trennung stark überlappender Peaks sowie die Optimierung der berechneten Peakparameter. Anhand eines niedrig aufgelösten LC-ESI-MS Datensatzes sowie eines hoch aufgelösten MALDI-MS Datensatzes zeigen wir die Effizienz unseres generischen Algorithmus sowie seine schnelle Laufzeit im Vergleich mit kommerziellen peak picking Algorithmen. Ein direkter quantitativer Vergleich mehrer LC-MS Messungen setzt voraus, dass Signale des gleichen Peptids innerhalb unterschiedlicher Maps die gleichen RT und m/z Positionen besitzen. Aufgrund experimenteller Unsicherheiten sind beide Dimension verzerrt. Unabhängig vom Prozessierungsstand der LC-MS Maps müssen die Verzerrungen vor einem Vergleich der Maps korrigiert werden. Mithilfe eines eigens entwickelten Ähnlichkeitsmaßes für LC-MS Maps entwickeln wir die erste formale Definition des multiplen LC-MS Roh- und Featuremap Alignment Problems. Weiterhin stellen wir unseren geometrischen Ansatz zur Lösung des Problems vor. Durch die Betrachtung der LC-MS Maps als zwei-dimensionale Punktmengen ist unser Algorithmus unabhängig vom Prozessierungsgrad der Maps. Wir verfolgen einen sternförmigen Alignmentansatz, bei dem alle Maps auf eine Referenzmap abgebildet werden. Die Überlagerung der Maps erfolgt hierbei mithilfe eines pose clustering basierten Algorithmus. Diese Überlagerung der Maps löst bereits das Rohmap Alignment Problem. Zur Lösung des multiplen Featuremap Alignment Problems implementieren wir einen zusätzlichen, effizienten Gruppierungsschritt, der zusammengehörige Peptidsignale in unterschiedlichen Maps einander zuordnet. Wir zeigen die Effizienz und Robustheit unseres Ansatzes auf zwei realen sowie auf drei künstlichen Datensätzen. Wir vergleichen hierbei die Güte sowie die Laufzeit unseres Algorithmus mit fünf weiteren frei verfügbaren Featuremap-Alignmentmethoden. In allen Experimenten überzeugte unser Algorithmus mit einer schnellen Laufzeit und den besten recall Werten