Capsule networks: a new approach for brain imaging

Nel campo delle reti neurali per il riconoscimento immagini, una delle più recenti e promettenti innovazioni è l’utilizzo delle Capsule Networks (CapsNet). Lo scopo di questo lavoro di tesi è studiare l'approccio CapsNet per l'analisi di immagini, in particolare per quelle neuroanatomiche. Le odierne tecniche di microscopia ottica, infatti, hanno posto sfide significative in termini di analisi dati, per l'elevata quantità di immagini disponibili e per la loro risoluzione sempre più fine. Con l'obiettivo di ottenere informazioni strutturali sulla corteccia cerebrale, nuove proposte di segmentazione possono rivelarsi molto utili. Fino a questo momento, gli approcci più utilizzati in questo campo sono basati sulla Convolutional Neural Network (CNN), architettura che raggiunge le performance migliori rappresentando lo stato dell'arte dei risultati di Deep Learning. Ci proponiamo, con questo studio, di aprire la strada ad un nuovo approccio che possa superare i limiti delle CNNs come, ad esempio, il numero di parametri utilizzati e l'accuratezza del risultato. L’applicazione in neuroscienze delle CapsNets, basate sull’idea di emulare il funzionamento della visione e dell’elaborazione immagini nel cervello umano, concretizza un paradigma di ricerca stimolante volto a superare i limiti della conoscenza della natura e i limiti della natura stessa

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi

On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

A machine learning based material homogenization technique for masonry structures

Cutting-edge methods in the computational analysis of structures have been developed over the last decades. Such modern tools are helpful to assess the safety of existing buildings. Two main finite element (FE) modeling approaches have been developed in the field of masonry structures, i.e. micro and macro scale. While the micro modeling distinguishes between the masonry components in order to accurately represent the typical masonry damage mechanisms in the material constituents, macro modeling considers a single continuum material with smeared properties so that large scale masonry models can be analyzed. Both techniques have demonstrated their advantages in different structural applications. However, each approach comes along with some possible disadvantages. For example, the use of micro modeling is limited to small scale structures, since the computational effort becomes too expensive for large scale applications, while macro modeling cannot take into account precisely the complex interaction among masonry components (brick units and mortar joints). Multi scale techniques have been proposed to combine the accuracy of micro modeling and the computational efficiency of macro modeling. Such procedures consider linked FE analyses at both scales, and are based on the concept of a representative volume element (RVE). The analysis of a RVE takes into account the micro structural behavior of component materials, and scales it up to the macro level. In spite of being a very accurate tool for the analysis of masonry structures, multi scale techniques still exhibit high computational cost while connecting the FE analyses at the two scales. Machine learning (ML) tools have been utilized successfully to train specific models by feeding big source data from different fields, e.g. autonomous driving, face recognition, etc. This thesis proposes the use of ML to develop a novel homogenization strategy for the in-plane analysis of masonry structures, where a continuous nonlinear material law is calibrated by considering relevant data derived from micro scale analysis. The proposed method is based on a ML tool that links the macro and micro scales of the analysis, by training a macro model smeared damage constitutive law through benchmark data from numerical tests derived from RVE micro models. In this context, numerical nonlinear tests on masonry micro models executed in a virtual laboratory provide the benchmark data for feeding the ML training procedure. The adopted ML technique allows the accurate and efficient simulation of the anisotropic behavior of masonry material by means of a tensor mapping procedure. The final stage of this novel homogenization method is the definition of a calibrated continuum constitutive model for the structural application to the masonry macro scale. The developed technique is applied to the in-plane homogenization of a Flemish bond masonry wall. Evaluation examples based on the simulation of physical laboratory tests show the accuracy of the method when compared with sophisticated micro modeling of the entire structure. Finally, an application example of the novel homogenization technique is given for the pushover analysis of a masonry heritage structure.En las últimas décadas se han desarrollado diversos métodos avanzados para el análisis computacional de estructuras. Estas herramientas modernas son también útiles para evaluar la seguridad de los edificios existentes. En el campo de las estructuras de la obra de fábrica se han desarrollado principalmente dos técnicas de modelizacón por elementos finitos (FE): la modelización en escala micro y en escala macro. Mientras que en un micromodelo se distingue entre los componentes de la obra de fábrica para representar con precisión los mecanismos de daño característicos de la misma, en un macromodelo se asignan las propiedades a un único material continuo que permite analizar modelos de obra de fábrica a gran escala. Ambas técnicas han demostrado sus ventajas en diferentes aplicaciones estructurales. Sin embargo, cada enfoque viene acompañado de algunas posibles desventajas. Por ejemplo, la micromodelización se limita a estructuras de pequeña escala, puesto que el esfuerzo computacional que requieren aumenta rápidamente con el tamaño de los modelos, mientras que la macromodelización, por su parte, es un enfoque promediado que no puede por tanto tener en cuenta precisamente la interacción compleja entre los componentes de la fábrica (unidades de ladrillo y juntas de mortero). Hasta el momento, se han propuesto algunas técnicas multiescala para combinar la precisión de la micromodelización y la eficiencia computacional de la macromodelización. Estos procedimientos aplican el análisis de FE vinculado a ambas escalas y se basan en el concepto de elemento de volumen representativo (RVE). El análisis de un RVE tiene en cuenta el comportamiento microestructural de los materiales componentes y lo escala hasta el nivel macro. A pesar de ser una herramienta muy precisa para el análisis de obra de fábrica, las técnicas multiescala siguen presentando un elevado coste computacional que se produce al conectar los análisis de FE de dos escalas. Además, diversos autores han utilizado con éxito herramientas de aprendizaje automático (machine learning (ML)) para poner a punto modelos específicos alimentados con grandes fuentes de datos de diferentes campos, por ejemplo, la conducción autónoma, el reconocimiento de caras, etc. Partiendo de los anteriores conceptos, este tesis propone el uso de ML para desarrollar una novedosa estrategia de homogeneización para el análisis en plano de estructuras de mampostería, donde se calibra una ley de materiales continua no lineal considerando datos relevantes derivados del análisis a microescala. El método propuesto se basa en una herramienta de ML que vincula las escalas macro y micro del análisis mediante la puesta a punto de una ley constitutiva para el modelo macro a través de datos producidos en ensayos numéricos de un RVE micro modelo. En este contexto, los ensayos numéricos no lineales sobre micro modelos de mampostería ejecutados en un laboratorio virtual proporcionan los datos de referencia para alimentar el procedimiento de entrenamiento del ML. La técnica de ML adoptada permite la simulación precisa y eficiente del comportamiento anisotrópico del material de mampostería mediante un procedimiento de mapeo tensorial. La etapa final de este novedoso método de homogeneización es la definición de un modelo constitutivo continuo calibrado para la aplicación estructural a la macroescala de mampostería. La técnica desarrollada se aplica a la homogeneización en el plano de un muro de obra de fábrica construido con aparejo flamenco. Ejemplos de evaluación basados en la simulación de pruebas físicas de laboratorio muestran la precisión del método en comparación con una sofisticada micro modelización de toda la estructura. Por último, se ofrece un ejemplo de aplicación de la novedosa técnica de homogeneización para el análisis pushover de una estructura patrimonial de obra de fábrica.Postprint (published version

Computer vision for bird strike prevention

Collisions with birds cause damage to aircraft and in some cases can even cause air travel accidents. According to data from international organizations such as the Federal Aviation Administration (FAA), the radar-based tools currently used to address this problem do not solve it, as there is no indication of a decrease in the number of bird strikes. Early detection and notification to pilots of the presence of birds is key to trying to minimize the possibility that bird impacts can occur. The objective of this project is to improve bird detection capacity in the airport environment. To achieve this goal, this work proposes that the solution could be the use of artificial intelligence based devices and computer vision. To test this hypothesis, a model based on convolutional neural networks (CNN) is selected, trained and deployed on a device for testing. To do this, research is carried out on the different strategies used to solve problems with artificial intelligence and the performance of pre-trained classifier and detector models available. To select the computer board where the model will be deployed, a discussion of Raspberry Pi¿s market performance is made. A collection of bird images is made for training the model. The prototype will finally consist of deploying the model on a Raspberry Pi that through a script in Python programming language is able to automatically notice birds in the real world using a camera connected to the Raspberry Pi. If any detection occurs, the model is capable of making a notification that could serve to anticipate impacts and thus allow appropriate preventive measures to be taken beforehand. In conclusion, this technology shows great potential to support existing solutions today. Theoretical results with validation images show accuracy and recall parameters above 90% but experimental tests with the prototype do not allow for a conclusive judgment due to limitations regarding the training data set