2,352 research outputs found
Deep ensemble model-based moving object detection and classification using SAR images
In recent decades, image processing and computer vision models have played a vital role in moving object detection on the synthetic aperture radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this study, a new automated model for detecting moving objects is proposed using SAR images. The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu-based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. When using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by gray-level co-occurrence matrix (GLCM), median binary patterns (MBPs), and additive harmonic mean estimated local Gabor binary pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing the ensemble deep learning technique, combining the models like the bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), and improved deep belief network (IDBN), which is trained with the features extracted previously. The combined models increase the accuracy of the results significantly. Furthermore, ensemble modeling reduces the variance and modeling method bias, which decreases the chances of overfitting. Compared to a single contributing model, ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean-case scenario, the proposed ensemble model has a minimum error value of 0.032, which is better related to other models. In both median- and best-case scenario studies, the ensemble model has a lower error value of 0.029 and 0.015
Multi-epoch machine learning for galaxy formation
In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models.
In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network
The use of telematic data on car insurance: How to use Machine Learning Models to implement telematic data on car insurance models
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis work focuses on the use of Telematic Car Data in insurance pricing. Nowadays, since the data to do this kind of research is not easily accessible due to the amount of sensitive personal information, we do not know much about the impact of this kind of data on insurance pricing. In this work, we used real data using an NDA. We used different feature selection techniques to assess the importance of Telematic Features compared to conventional insurance data and different Machine Learning algorithms to evaluate how the Telematic Data influences the prediction power of a claim happening. Towards the end of our work, we could see that using Telematic Data in insurance pricing increases our algorithms' prediction power and opens plenty of doors
on the the use of Telematic Data for the future of insurance pricing
Performance and Competitiveness of Tree-Based Pipeline Optimization Tool
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceAutomated machine learning (AutoML) is the process of automating the entire machine learn-ing workflow when applied to real-world problems. AutoML can increase data science produc-tivity while keeping the same performance and accuracy, allowing non-experts to use complex machine learning methods. Tree-based Pipeline Optimization Tool (TPOT) was one of the first AutoML methods created by data scientists and is targeted to optimize machine learning pipe-lines using genetic programming. While still under active development, TPOT is a very prom-ising AutoML tool. This Thesis aims to explore the algorithm and analyse its performance using real word data. Results show that evolution-based optimization is at least as accurate as TPOT initialization. The effectiveness of genetic operators, however, depends on the nature of the test case
Using personalised cardiovascular models to identify new diagnostic predictors for pre-eclampsia
Haemodynamic adaptations play a crucial role in uteroplacental perfusion during pregnancy. In particular, modifications of the utero-ovarian arterial network cause a significant increase in blood volume distributed to the placenta and foetus. Failure to make these cardiovascular modifications results in complicated pregnancies caused by different disorders such as hypertension, pre-eclampsia, intrauterine growth restriction (IUGR), and placental insufficiency. In pre-eclampsia, the modifications of the utero-ovarian arterial network are unsuccessful and cause less blood volume to be distributed to the placenta and foetus. Pre-eclampsia is a hypertensive disorder that is still not fully understood, and clinicians still fail at identifying pre-eclamptic women during controls, especially at differentiating between hypertensive women and pre-eclamptic women. One reason for this is that clinicians rely heavily on blood pressure when diagnosing pre-eclampsia, and this biomarker has similar readings for both pre-eclampsia and hypertension. As part of the diagnosis of pre-eclampsia, proteinuria is used. In order to improve the diagnosis of pre-eclampsia, other biomarkers are being researched. A dataset of 21 patients was used to find novel biomarkers that can classify pre-eclampsia. The dataset is divided into two groups: uncomplicated pregnancies with hypertensive women and complicated pregnancies with pre-eclampsia. A computational model of the cardiovascular system is used to simulate blood and pressure solutions based on patient-specific observations in order to develop a new biomarker. The model employs 1D modelling which incorporates a wave intensity analysis that models forward and backward waves to provide more precise predictions of wave propagation across the artery system, particularly in the utero-ovarian system. The proposed biomarkers will include dimensionless terms formed by global maternal parameters such as systolic blood pressure, stroke volume, pulse wave velocity, etc., or local uterine parameters such as pressure and velocity in specific vessels of the uterine system. Afterwards, their ability as a classifier of pre-eclampsia will be investigated. Besides this, a case study of the prone position in pregnancy and its effects on cardiovascular changes will be carried out. To do this, the computational model will be used to study what happens when a pregnant woman is positioned in the prone position and how vital metrics like blood pressure and cardiac output are altered. It was found that the biomarkers based on the radial and arcuate arteries have a better classification ability for pre-eclampsia, even higher than the Doppler-measured Resistance Index (RI) and Pulsatility Index (PI). The novelty of this work is the introduction of new biomarkers through the use of a computational model, as well as the demonstration of the dependability and use of 1D modelling in pregnancy. The model demonstrated how biomarkers that could not be measured clinically may be easily calculated using 1D modelling and provide critical information about the utero-ovarian circulation. Future work should concentrate on changing the existing solver into a much faster and simpler solver, as well as validating the biomarkers in a larger dataset
Less is More -- Towards parsimonious multi-task models using structured sparsity
Model sparsification in deep learning promotes simpler, more interpretable
models with fewer parameters. This not only reduces the model's memory
footprint and computational needs but also shortens inference time. This work
focuses on creating sparse models optimized for multiple tasks with fewer
parameters. These parsimonious models also possess the potential to match or
outperform dense models in terms of performance. In this work, we introduce
channel-wise l1/l2 group sparsity in the shared convolutional layers parameters
(or weights) of the multi-task learning model. This approach facilitates the
removal of extraneous groups i.e., channels (due to l1 regularization) and also
imposes a penalty on the weights, further enhancing the learning efficiency for
all tasks (due to l2 regularization). We analyzed the results of group sparsity
in both single-task and multi-task settings on two widely-used Multi-Task
Learning (MTL) datasets: NYU-v2 and CelebAMask-HQ. On both datasets, which
consist of three different computer vision tasks each, multi-task models with
approximately 70% sparsity outperform their dense equivalents. We also
investigate how changing the degree of sparsification influences the model's
performance, the overall sparsity percentage, the patterns of sparsity, and the
inference time.Comment: Under revie
Efficient Deep Learning for Real-time Classification of Astronomical Transients
A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential.
The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy
Nanofluids with optimised thermal properties based on metal chalcogenides with different morphology
Over the last decades, the interest around renewable energies has increased considerably because of the growing energy demand and the environmental problems derived from fossil fuels combustion. In this scenario, concentrating solar power (CSP) is a renewable energy with a high potential to cover the global energy demand. However, improving the efficiency and reducing the cost of technologies based on this type of energy to make it more competitive is still a work in progress.
One of the current lines of research is the replacement of the heat transfer fluid used in the absorber tube of parabolic trough collectors with nano-colloidal suspensions of nanomaterials in a base fluid, typically named nanofluids. Nanofluids are considered as a new generation of heat transfer fluids since they exhibit thermophysical properties improvements compared with conventional heat transfer fluids. But there are still some barriers to overcome for the implementation of nanofluids. For example, obtaining nanofluids with high stability is a priority challenge for this kind of system. Also ensuring that nanoparticles will not clog pipes or cause pressure drops.
In this Doctoral Thesis, the use of transition metal dichalcogenide-based nanofluids as a heat transfer fluid in solar power plants has been investigated for the first time. Specifically, nanofluids based on one-dimensional, two-dimensional and three-dimensional MoS2 , WS2 and WSe2 nanostructures have been researched. The base fluid used in the preparation of these nanofluids is the eutectic mixture of biphenyl and diphenyl oxide typically employed as heat transfer fluid in concentrating solar power plants. Mainly two preparation methods have been explored: the liquid phase exfoliation method, and the solvothermal synthesis of the nanomaterial and its subsequent dispersion in the thermal oil by ultrasound. Experimental parameters such as surfactant concentration, time and sonication frequency for preparation of nanofluids have also been analysed. The nanofluids have been subjected to an extensive characterisation which includes the study of colloidal stability over time, characterisation of thermal properties such as isobaric specific heat or thermal conductivity, rheological properties and optical properties. The results have revealed that nanofluids based on 1D and 2D nanostructures of transition metal dichalcogenides are colloidally stable over time and exhibit improved thermal properties compared to the typical thermal fluid used in solar power plants. The most promising nanofluids are those based on MoS 2 nanosheets and those based on WSe 2 nanosheets with heat transfer coefficient improvements of 36.2% and 34.1% respectively with respect to thermal oil. Furthermore, the dramatic role of WSe2 nanosheets in enhancing optical extinction of the thermal oil suggests the use of these nanofluids in direct absorption solar collectors. In conclusion, the present work demonstrates the feasibility of using nanofluids based on transition metal dichalcogenide nanostructures as heat transfer fluids in concentrating solar power plants based on parabolic trough collectors.En las últimas décadas, el interés en torno a las energías renovables ha
aumentado considerablemente debido a la creciente demanda energética y a
los problemas medioambientales derivados de la combustión de combustibles
fósiles. En este escenario, la energía solar de concentración (CSP) es una
energía renovable con un alto potencial para cubrir la demanda energética
mundial. Sin embargo, es necesario trabajar para mejorar la eficiencia y reducir
el coste de las tecnologías basadas en este tipo de energía con el objetivo de
hacerla más competitiva.
Una de las líneas de investigación actuales es la sustitución del fluido
caloportador utilizado en el tubo absorbedor de los colectores cilindroparabólicos por suspensiones nanocoloidales de nanomateriales en un fluido
base, típicamente denominados nanofluidos. Los nanofluidos se consideran
una nueva generación de fluidos de transferencia de calor, ya que presentan
mejoras en sus propiedades termofísicas en comparación con los fluidos de
transferencia de calor convencionales. Pero aún quedan algunos obstáculos por
superar para la aplicación de los nanofluidos. Por ejemplo, obtener nanofluidos
con alta estabilidad es un reto prioritario en este tipo de sistemas. También
garantizar que las nanopartículas no obstruyan las tuberías ni provoquen caídas
de presión.
En esta Tesis Doctoral se ha investigado por primera vez el uso de nanofluidos
basados en dicalcogenuros de metales de transición como fluido caloportador
en centrales solares. En concreto, se han investigado nanofluidos basados en
nanoestructuras unidimensionales, bidimensionales y tridimensionales de
MoS2, WS2 y WSe2. El fluido base utilizado en la preparación de estos
nanofluidos es la mezcla eutéctica de bifenilo y óxido de difenilo empleada
habitualmente como fluido de transferencia de calor en las centrales de
concentración de energía solar. Se han explorado principalmente dos métodos
de preparación: el método de exfoliación en fase líquida y la síntesis
solvotermal del nanomaterial y su posterior dispersión en el aceite térmico mediante ultrasonidos. También se han analizado parámetros experimentales
como la concentración de surfactante, el tiempo y la frecuencia de sonicación
para la preparación de los nanofluidos. Los nanofluidos han sido sometidos a
una extensa caracterización que incluye el estudio de la estabilidad coloidal a
lo largo del tiempo, la caracterización de propiedades térmicas como el calor
específico isobárico o la conductividad térmica, propiedades reológicas y
propiedades ópticas. Los resultados han revelado que los nanofluidos basados
en nanoestructuras 1D y 2D de dicalcogenuros de metales de transición son
coloidalmente estables en el tiempo y presentan propiedades térmicas
mejoradas en comparación con el fluido térmico típico utilizado en las
centrales solares. Los nanofluidos más prometedores son los basados en
nanoláminas de MoS2 y los basados en nanoláminas de WSe2, con mejoras del
coeficiente de transferencia térmica del 36,2% y el 34,1%, respectivamente,
con respecto al aceite térmico. Además, el espectacular papel de las
nanoláminas de WSe2 en la mejora de la extinción óptica del aceite térmico
sugiere el uso de estos nanofluidos en colectores solares de absorción directa.
En conclusión, el presente trabajo demuestra la viabilidad del uso de
nanofluidos basados en nanoestructuras de dicalcogenuros de metales de
transición como fluidos de transferencia de calor en centrales solares de
concentración basadas en colectores cilindro-parabólicos
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