Design of a General-Purpose MIMO Predictor with Neural Networks

A new multi-step predictor for multiple-input, multiple-output (MIMO) systems is proposed. The output prediction of such a system is represented as a mapping from its historical data and future inputs to future outputs. A neural network is designed to learn the mapping without re quiring a priori knowledge of the parameters and structure of the system. The major problem in de veloping such a predictor is how to train the neural network. In case of the back propagation algorithm, the network is trained by using the network's output error which is not known due to the unknown predicted future system outputs. To overcome this problem, the concept of updating, in stead of training, a neural network is introduced and verified with simulations. The predictor then uses only the system's historical data to update the configuration of the neural network and always works in a closed loop. If each node can only handle scalar operations, emulation of an MIMO mapping requires the neural network to be excessively large, and it is difficult to specify some known coupling effects of the predicted system. So, we propose a vector-structured, multilayer perceptron for the predictor design. MIMO linear, nonlinear, time-invariant, and time-varying systems are tested via simulation, and all showed very promising performances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68861/2/10.1177_1045389X9400500206.pd

Designing artificial neural networks for band structures computations in photonic crystals

We modeled Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks (ANNs) for computing band structures (BSTs) and photonic band gaps (PBGs) of 2D and 3D photonic crystals (PhCs). We aim at providing fast ANN models which might boost the computations of BDs and PBGs regarding electromagnetic solvers. The case studies considered 2D and 3D PhCs with different lattices, geometries, and materials. Datasets for ANN training were built by varying the geometric shapes' dimensions and the dielectric constants of the case-study PhCs. We demonstrate simple and fast-training ANNs capable of providing accurate BSTs and PGBs through speedy computations10912SPIE OPTO - Physics and Simulation of Optoelectronic Devices XXVI

Feature Driven Learning Techniques for 3D Shape Segmentation

Segmentation is a fundamental problem in 3D shape analysis and machine learning. The abil-ity to partition a 3D shape into meaningful or functional parts is a vital ingredient of many down stream applications like shape matching, classification and retrieval. Early segmentation methods were based on approaches like fitting primitive shapes to parts or extracting segmen-tations from feature points. However, such methods had limited success on shapes with more complex geometry. Observing this, research began using geometric features to aid the segmen-tation, as certain features (e.g. Shape Diameter Function (SDF)) are less sensitive to complex geometry. This trend was also incorporated in the shift to set-wide segmentations, called co-segmentation, which provides a consistent segmentation throughout a shape dataset, meaning similar parts have the same segment identifier. The idea of co-segmentation is that a set of same class shapes (i.e. chairs) contain more information about the class than a single shape would, which could lead to an overall improvement to the segmentation of the individual shapes. Over the past decade many different approaches of co-segmentation have been explored covering supervised, unsupervised and even user-driven active learning. In each of the areas, there has been widely adopted use of geometric features to aid proposed segmentation algorithms, with each method typically using different combinations of features. The aim of this thesis is to ex-plore these different areas of 3D shape segmentation, perform an analysis of the effectiveness of geometric features in these areas and tackle core issues that currently exist in the literature.Initially, we explore the area of unsupervised segmentation, specifically looking at co-segmentation, and perform an analysis of several different geometric features. Our analysis is intended to compare the different features in a single unsupervised pipeline to evaluate their usefulness and determine their strengths and weaknesses. Our analysis also includes several features that have not yet been explored in unsupervised segmentation but have been shown effective in other areas.Later, with the ever increasing popularity of deep learning, we explore the area of super-vised segmentation and investigate the current state of Neural Network (NN) driven techniques. We specifically observe limitations in the current state-of-the-art and propose a novel Convolu-tional Neural Network (CNN) based method which operates on multi-scale geometric features to gain more information about the shapes being segmented. We also perform an evaluation of several different supervised segmentation methods using the same input features, but with vary-ing complexity of model design. This is intended to see if the more complex models provide a significant performance increase.Lastly, we explore the user-driven area of active learning, to tackle the large amounts of inconsistencies in current ground truth segmentation, which are vital for most segmentation methods. Active learning has been used to great effect for ground truth generation in the past, so we present a novel active learning framework using deep learning and geometric features to assist the user in co-segmentation of a dataset. Our method emphasises segmentation accu-racy while minimising user effort, providing an interactive visualisation for co-segmentation analysis and the application of automated optimisation tools.In this thesis we explore the effectiveness of different geometric features across varying segmentation tasks, providing an in-depth analysis and comparison of state-of-the-art methods

Object Shape Classification Utilizing Magnetic Field Disturbance and Supervised Machine Learning

Various narrow artificial intelligence architectures are on the rise due to the development of Graphics Processing Units and, thus, computational capabilities. Massive number multiplication capabilities of GPUs enabled researches to create more complicated and advanced algorithms. Initially, a gaming hardware became a base for modern time Industrial Revolution. Machine learning, once a forgotten branch of computer science, attracts huge investments and interest. In 2014, Google acquired an UK-based start-up Deep Mind for over £400M. In 2016 Volkswagen invested $680M in autonomous vehicle and cyber security start-ups (1). Same year Microsoft announced a newly created AI fund (2) and in May this year it resulted in investment of$7.6M in Bonsai, an AI start-ups that hopes to help companies to integrate machine learning in the infrastructure (3). It seems that almost never-ending pockets of investors are motivated by a promise of automation of difficult tasks, which, until now, have never been performed by humans. This thesis explores various supervised machine learning algorithms, beginning with the simplest k-Nearest Neighbours and Multi-layer Perceptron, to the state of the art architecture created by the industry experts (Deep Residual Network from Microsoft Research), and prominent academic figures (i.e. GG from Oxford). Furthermore, the author of the thesis proposes two additional network structures, named Deep Inception and Stacked Artificial Residual Architecture, inspired by previously mentioned research

Uma abordagem baseada em redes neurais artificiais para computação de propriedades ópticas de cristais fotônicos

Orientadores: Hugo Enrique Hernández-Figueroa, Gilliard Nardel Malheiros SilveiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese aborda o emprego de processos baseados em redes neurais artificiais para computação de relações de dispersão e banda fotônica proibida de cristais fotônicos. A proposta objetiva prover um modelo de computação alternativo capaz de calcular rapidamente estas propriedades ópticas em relação às simulações eletromagnéticas convencionais. O modelo é baseado nas redes neurais artificiais Perceptron de Múltiplas Camadas e Máquinas de Aprendizado Extremo, que são projetadas para processarem dados geométricos e de materiais de cristais fotônicos e assim predizerem estas propriedades ópticas. Uma arquitetura simples de rede neural é proposta para permitir processos rápidos de treinamento. O modelo é testado em uma variedade de cristais fotônicos bi- and tri-dimensionais com arranjos, geometrias, e materiais diferentes, e sua capacidade de predição e desempenho de computação são avaliados em relação a um simulador eletromagnético bem estabelecido na comunidade de fotônicaAbstract: This thesis addresses the employment of Artificial Neural Network-based processes for computing dispersion relations and photonic bandgaps of photonic crystals. The proposal aims to provide an alternative computing model able to fastly calculate these optical properties regarding conventional electromagnetic simulations. The model is based on Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks, which are designed to process the geometric and material data of photonic crystals in order to predict such optical properties. A simple neural-network architecture is proposed for allowing fast training processes. The model is tested on a variety of bi- and tri-dimensional photonic crystals with different lattices, geometries, and materials, and its predicting capability and computing performance are evaluated in regard to a well-established electromagnetic simulator in photonic communityDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE