Super learner implementation in corrosion rate prediction

This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material\u27s environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models’ ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models’ predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis)

Electric vehicle X driving range predition – EV X DRP

Projeto Final para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresThe electric vehicle use as a reliable and eco-friendly means of transportation has in creased rapidly over the past few years. When choosing an electric vehicle, its driving range capacity is a decisive factor to be taken into account as it minimizes driver’s anxiety while driving. An electric vehicle driving range depends on multiple factors that must be taken into account when attempting its prediction. Machine learning has become a widely used approach for highly complex problems, in which eRange prediction, being one of them, provides benefits such as becoming more accurate, the more the user drives his vehicle. This thesis compares, through standard metrics, implementations of machine learn ing based regression models (Linear regression and Ensemble Stacked Generalization) when training with publicly available datasets. The results of this work show the effects of different training sample sizes on machine learning model’s accuracy and training time, presenting more favorable results for the Linear Regression algorithm, as the algorithm was more resistant to overfitting for commonly trained data. The results can be replicated with the implemented Python application, allowing for future testing and study of the topic.A utilização veículos elétricos como um meio de transporte confiável e ecológico tem vindo a aumentar nos últimos anos. Ao escolher um veículo elétrico, o range elétrico de condução é um fator decisivo a ser levado em consideração, pois minimiza a ansiedade do utilizador durante a condução. A autonomia de um veículo elétrico depende de vários fatores que devem ser conside rados ao estimar a sua previsão. A aprendizagem automática tem sido uma abordagem amplamente utilizada para problemas altamente complexos, dos quais a previsão da autonomia do veículo, é beneficial para o consumidor ao tornar-se mais preciso quanto mais o veículo é utilizado. Esta tese compara, através de métricas padrão e validação cruzada, implementações de modelos de regressão aprendizagem automática (Linear Regression e Ensemble Stacked Generalization) ao treinar com conjuntos de dados disponíveis publicamente. Os resultados desta tese demonstram as alterações da qualidade de previsão e de tempo de treino que os modelos de aprendizagem automática sofrem quando são usa das configurações dos dados diferentes e demonstrando resultados mais favoráveis para o algoritmo de Linear Regression, pois este demonstra melhor resistência a sobrea justar aos dados mais comuns presentes no conjunto de treino. Utilizando a aplicação desenvolvida em Python, é possível a replicação resultados, promovendo estudos fu turos no tema.info:eu-repo/semantics/publishedVersio

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Matrix Completion under Interval Uncertainty

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes

Journal of Telecommunications and Information Technology, 2009, nr 3

kwartalni

Compression effects, perceptual asymmetries, and the grammar of timing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 272-279).This dissertation reports the results of two English experiments on timing and perception. The first experiment demonstrates asymmetries in timing between consonants and vowels, which depend on the manner of the consonant. The second experiment shows that these asymmetries in speech production are mirrored by perceptual asymmetries among consonants with different manner features. We argue that these phenomena are best described in terms of auditory rather than articulatory representations. A formal analysis is developed using weighted, gradiently-violable constraints on segment and syllable duration. Because the constraints make reference to the auditory features of segments, the analysis can derive the relationship between asymmetries in speech production and asymmetries in speech perception. The patterns of timing discovered here appear to interact in limited ways with systems of phonological contrast. We incorporate the duration constraints proposed here into a phonetically-driven model of phonology, examining the predictions that such an approach makes about phonological typology.by Jonah Katz.Ph.D

Structural and functional alterations of cortical neurons in Alzheimer’s disease transgenic mice assessed by two-photon in vivo imaging

Alzheimer’s disease (AD), the most common form of dementia, has been proposed to result from the degeneration of synapses, putatively caused by assemblies of the amyloid-β peptide (Aβ). The spatiotemporal dynamics of this synaptopathy, its potential reversibility as well as its consequences on the function of single neurons and neuronal circuits, however, are not fully understood to date. In order to address these questions, I assessed structural and functional alterations of neurons in the neocortex in a transgenic mouse model of Alzheimer’s disease, namely APP/PS1 (APPswe, PS1L166P) mice, using in vivo two-photon imaging. Chronic imaging of dendrites and axons over the course of four weeks revealed not only a reduction in dendritic spine density close to amyloid plaques (proteinaceous extracellular deposits typical of AD), but I also identified synaptic instability as a main aspect contributing to AD pathology. Importantly, while synapse loss was confined to the immediate plaque vicinity (up to 15µm from the histological plaque border), synaptic instability was evident in a much larger region surrounding plaques (50 µm) and affected both, pre- and postsynaptic compartments. As the prevailing hypothesis in AD holds that Aβ conveys these detrimental effects on synapses one therapeutic approach is based on the pharmacological inhibition of Aβ generation. I thus assessed the impact of a novel selective γ-secretase inhibitor (GSI), a compound that prevents the last cleavage step necessary for the release of Aβ from the longer transmembrane amyloid precursor protein (APP). Notably, the GSI used here primarily interferes with the processing of APP and still allows for processing of other γ-secretase substrates, and hence should largely reduce side effects seen with earlier generations of GSIs before. Daily treatment with the GSI reduced the deposition of Aβ as evidenced by the initial reduction in the number of new plaques and a sustained decrease in the growth of these newly deposited plaques. Importantly, it also ameliorated the plaque-associated synaptic instability, without displaying overt adverse effects on dendritic spines in WT mice. These data represent the first in vivo evidence that selective pharmacological inhibition of the γ-secretase mediated APP cleavage can have beneficial effects on synaptic pathology in AD. Given the widespread impact of Aβ assemblies on neuronal structures, I then asked to which extent these structural alterations affect the function of neurons. To address this question, I recorded neuronal response properties in the primary visual cortex of behaving APP/PS1 mice, employing in vivo two-photon calcium imaging using the genetically encoded calcium indicator GCaMP6m. In order to probe the impact of AD related pathology on specific aspects of information processing, which rely on multiple neuronal circuits, I characterized visually driven and motor-related activity, as well as signals based on mismatches between actual and expected visual input. My data reveal a massive reduction in responsiveness under almost all conditions tested, which is line with the profound impact on neuronal structure. Stimulus selectivity, like orientation or direction tuning, were not altered in APP/PS1 mice, indicating that the main effect is caused by a change in response gain. Along with the massive decrease in feedforward signals, I observed an increase in spontaneous, hence uncorrelated neuronal activity in AD transgenic mice. Both features jointly affected the coding accuracy of the network, and I propose that this combination may represent a common characteristic leading to impaired information processing in AD. Surprisingly, I found that responses elicited after a discordance of actual and expected visual flow during running, i.e. a visuomotor mismatch, were selectively spared in APP/PS1 mice, suggesting a particular resilience of this very signal. Together, both studies demonstrate that global widespread structural changes of neurons in the AD brain are accompanied by a severe impact on information processing, most prominently seen in a strong reduction of feedforward signals. My data, thus, provide a correlate of impaired cognition in AD at the level of single neurons and neural circuits