This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified. / <p>QC 20170420</p>

<p>Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. </p> <p><br></p> <p>The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. </p> <p><br></p> <p>In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. </p> <p><br></p> <p>The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages. </p>

This document is a Master Thesis report in financial mathematics for KTH. This Master thesis is the product of an internship conducted at Nexialog Consulting, in Paris. This document is about the innovative use of Constrained Gaussian process regression in order to build an arbitrage free swaption cube. The methodology introduced in the document is used on a data set of European Swaptions Out of the Money. / Det här dokumentet är en magisteruppsats i finansiel matematik på KTH. Detta examensarbete är resultatet av en praktik som ufördes på Nexialog Consulting i Paris.Detta dokument handlar om den innovativa användningen av regression för gaussiska processer med bivillkor för att bygga en arbitragefri swaption kub. Den metodik som introduceras i dokumentet används på en datamängd av europeiska swaptions som är "Out of the Money".

<p>Injection to the subcutaneous (SC) tissue is one of the preferred methods for drug delivery of pharmaceuticals, from small molecules to monoclonal antibodies. Delivery to SC has become widely popular in part thanks to the low cost, ease of use, and effectiveness of drug delivery through the use of auto-injector devices. However, injection physiology, from initial plume formation to the eventual uptake of the drug in the lymphatics, is highly dependent on SC mechanics, poroelastic properties in particular. Yet, the poroelastic properties of SC have been understudied. In this thesis, I present a two-pronged approach to understanding the poroelastic properties of SC. Experimentally, mechanical and fluid transport properties of SC were measured with confined compression experiments and compared against gelatin hydrogels used as SC-phantoms. It was found that SC tissue is a highly non-linear material that has viscoelastic and porohyperelastic dissipation mechanisms. Gelatin hydrogels showed a similar, albeit more linear response, suggesting a micromechanical mechanism may underline the nonlinear behavior. The second part of the thesis focuses on the multiscale modeling of SC to gain a fundamental understanding of how geometry and material properties of the microstructure drive the macroscale response. SC is composed of adipocytes (fat cells) embedded in a collagen network. The geometry can be characterized with Voroni-like tessellations. Adipocytes are fluid-packed, highly deformable and capable of volume change through fluid transport. Collagen is highly nonlinear and nearly incompressible. Representative volume element (RVE) simulations with different Voroni tesselations shows that the different materials, coupled with the geometry of the packing, can contribute to different material response under the different kinds of loading. Further investigation of the effect of geometry showed that cell packing density nonlinearly contributes to the macroscale response. The RVE models can be homogenized to obtain macroscale models useful in large scale finite element simulations of injection physiology. Two types of homogenization were explored: fitting to analytical constitutive models, namely the Blatz-Ko material model, or use of Gaussian process surrogates, a data-driven non-parametric approach to interpolate the macroscale response.</p>

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With advanced technology and information explosion, the data of interest often have complex structures, with the large size and dimensions in the form of continuous or discrete features. There is an emerging need for data reduction, efficient modeling, and model inference. For example, data can contain millions of observations with thousands of features. Traditional methods, such as linear regression or LASSO regression, cannot effectively deal with such a large dataset directly. This dissertation aims to develop several techniques to effectively analyze large datasets with complex structures in the observational, experimental and time series data. In Chapter 2, I focus on the data reduction for model estimation of sparse regression. The commonly-used subdata selection method often considers sampling or feature screening. Un- der the case of data with both large number of observation and predictors, we proposed a filtering approach for model estimation (FAME) to reduce both the size of data points and features. The proposed algorithm can be easily extended for data with discrete response or discrete predictors. Through simulations and case studies, the proposed method provides a good performance for parameter estimation with efficient computation. In Chapter 3, I focus on modeling the experimental data with quantitative-sequence (QS) factor. Here the QS factor concerns both quantities and sequence orders of several compo- nents in the experiment. Existing methods usually can only focus on the sequence orders or quantities of the multiple components. To fill this gap, we propose a QS transformation to transform the QS factor to a generalized permutation matrix, and consequently develop a simple Gaussian process approach to model the experimental data with QS factors. In Chapter 4, I focus on forecasting multivariate time series data by leveraging the au- toregression and clustering. Existing time series forecasting method treat each series data independently and ignore their inherent correlation. To fill this gap, I proposed a clustering based on autoregression and control the sparsity of the transition matrix estimation by adap- tive lasso and clustering coefficient. The clustering-based cross prediction can outperforms the conventional time series forecasting methods. Moreover, the the clustering result can also enhance the forecasting accuracy of other forecasting methods. The proposed method can be applied on practical data, such as stock forecasting, topic trend detection. / Doctor of Philosophy / This dissertation focuses on three projects that are related to data reduction and statistical modeling of data with complex structures. In chapter 2, we propose a filtering approach of data for parameter estimation of sparse regression. Given data with thousands of ob- servations and predictors or even more, large storage and computation spaces is need to handle these data. It is challenging to computational power and takes long time in terms of computational cost. So we come up with an algorithm (FAME) that can reduce both the number of observations and predictors. After data reduction, this subdata selected by FAME keeps most information of the original dataset in terms of parameter estimation. Compare with existing methods, the dimension of the subdata generated by the proposed algorithm is smaller while the computational time does not increase. In chapter 3, we use quantitative-sequence (QS) factor to describe experimental data. One simple example of experimental data is milk tea. Adding 1 cup of milk first or adding 2 cup of tea first will influence the flavor. And this case can be extended to cases when there are thousands of ingredients need to be input into the experiment. Then the order and amount of ingredients will generate different experimental results. We use QS factor to describe this kind of order and amount. Then by transforming the QS factor to a matrix containing continuous value and set this matrix as input, we model the experimental results with a simple Gaussian process. In chapter 4, we propose an autoregression-based clustering and forecasting method of multi- variate time series data. Existing research works often treat each time series independently. Our approach incorporates the inherent correlation of data and cluster related series into one group. The forecasting is built based on each cluster and data within one cluster can cross predict each other. One application of this method is on topic trending detection. With thousands of topics, it is unfeasible to apply one model for forecasting all time series. Considering the similarity of trends among related topics, the proposed method can cluster topics based on their similarity, and then perform forecasting in autoregression model based on historical data within each cluster.

With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, etc. These data provide a rich source of information on disease development, cell evolvement, engineering systems, and many other scientific phenomena. To achieve a clearer understanding of the underlying mechanism, one needs a fast and reliable analytical approach to extract useful information from the wealth of data. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing, and analysis of complex, high-dimensional data. It contains three parts: parameter estimation based on complex data, powerful testing of functional data, and the analysis of functional data supported on manifolds. The first part focuses on a family of parameter estimation problems in which the relationship between data and the underlying parameters cannot be explicitly specified using a likelihood function. We introduce a wavelet-based approximate Bayesian computation approach that is likelihood-free and computationally scalable. This approach will be applied to two applications: estimating mutation rates of a generalized birth-death process based on fluctuation experimental data and estimating the parameters of targets based on foliage echoes. The second part focuses on functional testing. We consider using multiple testing in basis-space via p-value guided compression. Our theoretical results demonstrate that, under regularity conditions, the Westfall-Young randomization test in basis space achieves strong control of family-wise error rate and asymptotic optimality. Furthermore, appropriate compression in basis space leads to improved power as compared to point-wise testing in data domain or basis-space testing without compression. The effectiveness of the proposed procedure is demonstrated through two applications: the detection of regions of spectral curves associated with pre-cancer using 1-dimensional fluorescence spectroscopy data and the detection of disease-related regions using 3-dimensional Alzheimer's Disease neuroimaging data. The third part focuses on analyzing data measured on the cortical surfaces of monkeys' brains during their early development, and subjects are measured on misaligned time markers. In this analysis, we examine the asymmetric patterns and increase/decrease trend in the monkeys' brains across time. / Doctor of Philosophy / With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, and biological measurements. These data provide a rich source of information on disease development, engineering systems, and many other scientific phenomena. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing, and analysis of complex, high-dimensional data. It contains three parts: parameter estimation based on complex biological and engineering data, powerful testing of high-dimensional functional data, and the analysis of functional data supported on manifolds. The first part focuses on a family of parameter estimation problems in which the relationship between data and the underlying parameters cannot be explicitly specified using a likelihood function. We introduce a computation-based statistical approach that achieves efficient parameter estimation scalable to high-dimensional functional data. The second part focuses on developing a powerful testing method for functional data that can be used to detect important regions. We will show nice properties of our approach. The effectiveness of this testing approach will be demonstrated using two applications: the detection of regions of the spectrum that are related to pre-cancer using fluorescence spectroscopy data and the detection of disease-related regions using brain image data. The third part focuses on analyzing brain cortical thickness data, measured on the cortical surfaces of monkeys’ brains during early development. Subjects are measured on misaligned time-markers. By using functional data estimation and testing approach, we are able to: (1) identify asymmetric regions between their right and left brains across time, and (2) identify spatial regions on the cortical surface that reflect increase or decrease in cortical measurements over time.

Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas. / Ph. D. / Heterogeneous data with different characteristics are ubiquitous in the modern digital world. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation focuses on modeling and analysis of data with heterogeneous structures. Particularly, four types of heterogeneous data are analyzed and different techniques are proposed in order to nd change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.

Being able to keep track of ones assets is a very useful thing, from avoiding losing ones keys or phone to being able to find the needed equipment in a busy hospital or on a construction site. The area of localization is actively evolving to find the best ways to accurately track objects and devices in an energy efficient manner, at any range, and in any type of environment. This thesis focuses on the last aspect of maintaining accurate localization regardless of environment. For radio frequency based systems, challenging environments containing many obstacles, e.g., indoor or urban areas, have a detrimental effect on the measurements used for positioning, making them deceptive. In this work, a method for correcting range measurements is proposed for a narrowband sub-GHz radio frequency based localization system using Received Signal Strength Indicator (RSSI) and Time-of-Flight (ToF) measurements for positioning. Three different machine learning models were implemented: a linear regressor, a least squares support vector machine regressor and a gaussian process regressor. They were compared in their ability to predict the true range between devices based on raw range measurements. Achieved was a 69.96 % increase in accuracy compared to uncorrected ToF estimates and a 88.74 % increase in accuracy compared to RSSI estimates. When the corrected range estimates were used for positioning with a trilateration algorithm using least squares estimation, a 67.84 % increase in accuracy was attained compared to positioning with uncorrected range estimates. This shows that this is an effective method of improving range estimates to facilitate more accurate positioning. / Att kunna hålla reda på var ens tillgångar befinner sig kan vara mycket användbart, från att undvika att ens nycklar eller telefon tappas bort till att kunna hitta utrustningen man behöver i ett myllrande sjukhus eller på en byggarbetsplats. Området av lokalisering utvecklas aktivt för att hitta de bästa metoderna och teknologierna för att med precision kunna spåra fysiska objekt på ett energieffektivt sätt, på vilken räckvidd som helst, och i vilken miljö som helst. Detta arbete fokuserar på den sista aspekten av att uppnå precis positionering oavsett miljö. För radiofrekvensbaserade system har utmanande miljöer med många fysiska hinder som till exempel inomhus och stadsområden en negativ effekt på de mätningar som används för positionering, vilket gör dem vilseledande. I detta arbete föreslås en metod för att korrigera avståndsmätningar i ett narrowband sub-GHz radiofrekvensbaserat lokaliseringssystem som använder Received Signal Strength Indicator (RSSI)- och Time-of-Flight (ToF)-mätningar för positionering. Tre olika maskininlärningsmodeller har implementerats: en linear regressor, en least squares support vector machine regressor och en gaussian process regressor. Dessa jämfördes i sin förmåga att förutspå det sanna avståndet mellan enheter baserat på råa avståndsmätningar. De korrigerade avståndsmätningarna uppnådde 69.96 % högre nogrannhet jämfört med okorrigerade ToF-uppskattningar och 88.74 % högre nogrannhet jämfört med RSSI-uppskattningar. Avståndsuppskattningarna användes för positionering med trilateration och minsta kvadratmetoden. De korrigerade uppskattningarna gav 67.84 % mer precis positionering jämfört med de okorrigerde uppskattningarna. Detta visar att detta är en effektiv metod förbättra avståndsuppskattningarna för att i sin tur bidra till mer exakt positionering.

<p dir="ltr">For turbocharged engine simulation analysis, manufacturer-provided data are typically used to predict the mass flow and efficiency of the turbine. To create a turbine map, physical tests are performed in labs at various turbine speeds and expansion ratios. These tests can be very expensive and time-consuming. Current testing methods can have limitations that result in errors in the turbine map. As such, only a modest set of data can be generated, all of which have to be interpolated and extrapolated to create a smooth surface that can then be used for simulation analysis.</p><p><br></p><p dir="ltr">The current method used by the manufacturer is a physics-informed polynomial regression model that depends on the Blade Speed Ratio (BSR ) in the polynomial function to model the efficiency and MFP. This method is memory-consuming and provides a lower-than-desired accuracy. This model is decades old and must be updated with new state-of-the-art Machine Learning models to be more competitive. Currently, CTT is facing up to +/-2% error in most turbine maps for efficiency and MFP and the aim is to decrease the error to 0.5% while interpolating the data points in the available region. The current model also extrapolates data to regions where experimental data cannot be measured. Physical tests cannot validate this extrapolation and can only be evaluated using CFD analysis.</p><p><br></p><p dir="ltr">The thesis focuses on investigating different AI techniques to increase the accuracy of the model for interpolation and evaluating the models for extrapolation. The data was made available by CTT. The available data consisted of various turbine parameters including ER, turbine speeds, efficiency, and MFP which were considered significant in turbine modeling. The AI models developed contained the above 4 parameters where ER and turbine speeds are predictors and, efficiency and MFP are the response. Multiple supervised ML models such as SVM, GPR, LMANN, BRANN, and GBPNN were developed and evaluated. From the above 5 ML models, BRANN performed the best achieving an error of 0.5% across multiple turbines for efficiency and MFP. The same model was used to demonstrate extrapolation, where the model gave unreliable predictions. Additional data points were inputted in the training data set at the far end of the testing regions which greatly increased the overall look of the map.</p><p><br></p><p dir="ltr">An additional contribution presented here is to completely predict an expansion ratio line and evaluate with CTT test data points where the model performed with an accuracy of over 95%. Since physical testing in a lab is expensive and time-consuming, another goal of the project was to reduce the number of data points provided for ANN model training. Furthermore, strategically reducing the data points is of utmost importance as some data points play a major role in the training of ANN and can greatly affect the model's overall accuracy. Up to 50% of the data points were removed for training inputs and it was found that BRANN was able to predict a satisfactory turbine map while reducing 20% of the overall data points at various regions.</p>