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Failures in cyber physical systems can be costly in terms of money and lives. The marsclimate orbiter alone had a mission cost of 327.6 million USD which was almostcompletely wasted do to an uncaught design flaw. This shows the importance of beingable to define formal requirements as well as being able to test the design against theserequirements. One way to define requirements is in Metric Temporal Logic (MTL), whichallows for constraints that also have a time component. MTL can also have a distancemetric defined that allows for the calculation of how close the MTL constraint is to beingfalsified. This is termed robustness.Being able to calculate MTL robustness quickly can help reduce development time andcosts for a cyber physical system. In this thesis, improvements to the current method ofcomputing MTL robustness are proposed. These improvements lower the timecomplexity, allows parallel processing to be used, and lowers the memory foot print forMTL robustness calculation. These improvements will hopefully increase the likelihood ofMTL robustness being used in systems that were previously inaccessible do to timeconstraints, data resolution or real time systems that need results quickly. Theseimprovements will also open the possibility of using MTL in systems that operate for alarge amount of time and produce a large amount of signal data

Memory is a complex and multifaceted entity. Cognitive psychology has adopted terminology to help simplify the study of memory. For example, one can consider the cognitive process the brain is engaged in, such as encoding versus retrieval. Similarly, one can consider the content of information, such as words, faces, or scenes. Content and process can also interact such as with instructions to view a face that happens to be situated next to a house (item memory) versus instructions to evaluate whether the face 'belongs' in the house (relational memory). Although neuropsychology, animal lesion studies, and cognitive neuroscience have identified brain structures that are consistently associated with memory performance, such as the medial temporal lobes (MTL) and prefrontal cortex (PFC), the specifics of when and why such regions participate in memory is still largely unexplored. Theoretical standpoints are often at odds about whether regions such as the MTL operate as a functional unit, supporting memory in general, or whether subregions within the MTL support specific types of memory (e.g. item versus relational memory). To investigate how memory processes might recruit unique and common brain regions, three functional magnetic resonance imaging (fMRI) studies were conducted. Each study involved comparisons of successful encoding (trials later remembered versus forgotten) and successful retrieval (hits versus misses). Experiment 1, using semantic and perceptual word pairs, found unique contributions for subregions in the MTL and PFC, dependent on memory phase and stimulus class. One region in the left hippocampus was associated with memory success, regardless of either memory phase or stimulus class. Experiment 2, using faces and scenes, found unique contributions for 'stimulus sensitive' subregions of the fusiform gyrus and parahippocampal gyrus, as well as for the PFC, and MTL that were dependent on content-process interactions, or independent of content and process. Experiment 3, using faces, scenes, and face-scene pairings, found unique contributions for subregions of the MTL and PFC based on item versus relational processing and memory phase. Together, the results of the three experiments provide support for dichotomies in brain structures based on specific processes, specific content, or process-content interactions. / Dissertation

Presented work covers one of the most important areas of OMG’s model driven architecture (MDA) – problems of object model transformations. Based on research of OMG specifications and other sources, author analyzes transformation process, states importance of modeling and metamodeling for designing of UML like modeling languages. Research work describes designed PIM ant PSM metamodels of aggregate systems. Structure and syntax of ATL and MTL model transformation languages was introduced. Author gives a short overview of model editors and graphical representation for these languages, created using EMF framework tools. Transformation languages have been compared. A result of experimental transformations was summarized by their qualitative and quantitative criteria. Advantages of hybrid ATL transformation language versus imperative MTL language were found. Experimental transformations were successfully produced and executed using ATL and MTL model transformation languages.

This thesis explores multi-task learning for combustion flame characterization i.e to learn different characteristics of the combustion flame. We propose a multi-task convolutional neural network for two tasks i.e. PFR (Pilot fuel ratio) and fuel type classification based on the images of stable combustion. We utilize transfer learning and adopt VGG16 to develop a multi-task convolutional neural network to jointly learn the aforementioned tasks. We also compare the performance of the individual CNN model for two tasks with multi-task CNN which learns these two tasks jointly by sharing visual knowledge among the tasks. We share the effectiveness of our proposed approach to a private company’s dataset. To the best of our knowledge, this is the first work being done for jointly learning different characteristics of the combustion flame. / <p>This wrok as done with Siemens, and we have applied for a patent which is still pending.</p>

Made available in DSpace on 2014-12-17T14:33:10Z (GMT). No. of bitstreams: 1 MarcioLFR_DISSERT.pdf: 949735 bytes, checksum: 7b0d7914f06ce721cd7df5864b67525c (MD5) Previous issue date: 2014-02-11 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / When scientists study methods, theory and standards in an inseparable form, he is facing a paradigm. Throughout the development of a determined science, paradigms can change by changing the methods, objective and standards of research. Fisheries science is changing the paradigm moving from the paradigm of maximization in the use of resources, quantified by the index of the catch, which was influenced by evolutionary concepts such as Optimal Foraging Theory, to the sustainability paradigm that seeks its foundation in the fishery ecosystem perspective. The goal of this study was to review methods, theory and the history of ecosystem indexes of fishery science that attempts to determine sustainability of fishery resources from the data capture. Ecosystems indexes by themselves may not be useful to measure the sustainability of fishing because they focus only on the environmental or ecological side of the sustainability tripod. Probably to measure the sustainability of fishing these indexes should include in the future the Payments for Ecosystem Services and Social Resilience. Thus the methods and theories are in constantly changing within science to meet the most current paradigm / Quando o cientista enxerga de forma insepar?vel os m?todos, a teoria e as normas ele est? diante de um paradigma. Ao longo do tempo o desenvolvimento de uma determinada ci?ncia o paradigma pode mudar alterando os m?todos, os objetivos e as normas da pesquisa com o passar dos anos. Ci?ncia Pesqueira transitou de paradigma da maximiza??o na utiliza??o dos recursos, quantificada pelo ?ndice da captura, que foi influenciado por conceitos evolutivos como a Teoria do Forrageamento ?timo para um paradigma da sustentabilidade que busca seu alicerce na perspectiva ecossist?mica da pesca. O objetivo desse trabalho foi realizar uma revis?o que aborda m?todos, teoria e o hist?rico dos ?ndices ecossist?micos da ci?ncia pesqueira que tentam determinar a sustentabilidade dos recursos pesqueiros a partir dos dados de captura. Os ?ndices ecossist?micos talvez n?o consiga mensurar a sustentabilidade da pesca por si s?, porque eles abordam apenas o lado ambiental ou ecol?gico do trip? da sustentabilidade. Provavelmente para se mensurar a sustentabilidade da pesca esses ?ndices devem se juntar no futuro com os Pagamentos por Servi?os Ecossist?micos e a Resili?ncia Social. Assim os m?todos e as teorias se agregam e se reformulam constantemente dentro dessa ci?ncia para atender o paradigma mais atual

Transients encountered by transformers in power stations during normal operation can have complex oscillatory overvoltages containing a large spectrum of frequency components. These transients can coincide with the natural frequencies of the transformers windings, leading to voltages that can be greater or more severe than the current factory proof tests. This may lead to insulation breakdown and catastrophic failures. Existing lumped parameter RLCG transformer models have been proven to be less accurate for very fast transient overvoltages (VFTO) with frequencies over 1 MHz. A white box model for transient analysis of transformer windings has been developed using Multiconductor Transmission Line (MTL) Theory. This model enables the simulation of natural frequencies of the transformer windings up to frequencies of several MHz, and can be used to compute voltages between turns by representing each turn as a separate transmission line. Both continuous and interleaved disk windings have been modelled and a comparison and validation of the results is presented. / May 2017

The entorhinal cortex (EC) in the medial temporal lobe plays a critical role in memory formation and is implicated in several neurological diseases including temporal lobe epilepsy and Alzheimer’s disease. Despite the known importance of this brain region, little is known about the normal bioelectrical activity patterns of the EC in awake, behaving primates. In order to develop effective therapies for diseases affecting the EC, we must first understand its normal properties. To contribute to our understanding of the EC, I monitored the activity of individual neurons and populations of neurons in the EC of rhesus macaque monkeys during free-viewing of photographs using electrophysiological techniques. The results of these experiments help to explain how primates can form memories of, and navigate through, the visual world. These experiments revealed neurons in the EC that represent visual space with triangular grid receptive fields and other neurons that prefer to fire near image borders. These properties are similar to those previously described in the rodent EC, but here the neuronal responses relate to viewing of remote space as opposed to representing the physical location of the animal. The representation of visual space may be aided by another EC neuron type that was discovered, free-viewing saccade direction cells, neurons that signaled the direction of upcoming saccades. Such a signal could be used by other cells to prepare to fire according to the future gaze location. Many of these spatially-responsive neurons also represented memory for images, suggesting that they may be useful for associating items with their locations. I also examined the neuronal circuitry of recognition memory for visual stimuli in the EC, and I found that population synchronization within the gamma-band (30-140 Hz) in superficial layers of the EC was modulated by stimulus novelty, while the strength of memory formation modulated gamma-band synchronization in the deep layers and in layer III. Furthermore, the strength of connectivity in the gamma-band between different layers was correlated with the strength of memory formation, with deep to superficial power transfer being correlated with stronger memory formation and superficial to deep transfer correlated with weaker memory formation. These findings support several previous investigations of hippocampal-entorhinal connectivity in the rodent and advance our understanding of the functional circuitry of the medial temporal lobe memory system. Finally, I explored the design of a device that could be used to investigate properties of brain tissue in vitro, potentially aiding in the development of treatments for disorders of the EC and other brain structures. We designed, fabricated, and validated a novel device for long-term maintenance of thick brain slices and 3-dimensional dissociated cell cultures on a perforated multi-electrode array. To date, most electrical recordings of thick tissue preparations have been performed by manually inserting electrode arrays. This work demonstrates a simple and effective solution to this problem by building a culture perfusion chamber around a planar perforated multi-electrode array. By making use of interstitial perfusion, the device maintained the thickness of tissue constructs and improved cellular survival as demonstrated by increased firing rates of perfused slices and 3-D cultures, compared to unperfused controls. To the best of our knowledge, this is the first thick tissue culture device to combine forced interstitial perfusion for long-term tissue maintenance and an integrated multi-electrode array for electrical recording and stimulation.

No description available.

<p>A wide range of optoelectronic applications demand semiconductors optimized for purpose.</p> <p>My research focused on data-driven identification of ABX3 Halide perovskite compositions for optimum photovoltaic absorption in solar cells.</p> <p>I trained machine learning models on previously reported datasets of halide perovskite band gaps based on first principles computations performed at different fidelities.</p> <p>Using these, I identified mixtures of candidate constituents at the A, B or X sites of the perovskite supercell which leveraged how mixed perovskite band gaps deviate from the linear interpolations predicted by Vegard's law of mixing to obtain a selection of stable perovskites with band gaps in the ideal range of 1 to 2 eV for visible light spectrum absorption.</p> <p>These models predict the perovskite band gap using the composition and inherent elemental properties as descriptors.</p> <p>This enables accurate, high fidelity prediction and screening of the much larger chemical space from which the data samples were drawn.</p> <p><br></p> <p>I utilized a recently published density functional theory (DFT) dataset of more than 1300 perovskite band gaps from four different levels of theory, added to an experimental perovskite band gap dataset of \textasciitilde{}100 points, to train random forest regression (RFR), Gaussian process regression (GPR), and Sure Independence Screening and Sparsifying Operator (SISSO) regression models, with data fidelity added as one-hot encoded features.</p> <p>I found that RFR yields the best model with a band gap root mean square error of 0.12 eV on the total dataset and 0.15 eV on the experimental points.</p> <p>SISSO provided compound features and functions for direct prediction of band gap, but errors were larger than from RFR and GPR.</p> <p>Additional insights gained from Pearson correlation and Shapley additive explanation (SHAP) analysis of learned descriptors suggest the RFR models performed best because of (a) their focus on identifying and capturing relevant feature interactions and (b) their flexibility to represent nonlinear relationships between such interactions and the band gap.</p> <p>The best model was deployed for predicting experimental band gap of 37785 hypothetical compounds.</p> <p>Based on this, we identified 1251 stable compounds with band gap predicted to be between 1 and 2 eV at experimental accuracy, successfully narrowing the candidates to about 3% of the screened compositions.</p>