Big Data, Big Knowledge: Big Data for Personalized Healthcare.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority

Прогнозування космічної погоди з використанням NARMAX моделей

Актуальність теми У даний час кількість факторів, на які космічна погода має великий вплив швидко зростає. Цей ріст обумовлено, як збільшенням кількості електронного обладнання, що виводиться на навколоземну орбіту, так і зростаючою кількістю чутливого до електромагнітних коливань наземного обладнання, включаючи навіть звичайні електромережі. На даний момент методи, що використовуються для передбачення космічної погоди мають велику кількість обмежень пов’язаних з невисокою точністю та обмеженим горизонтом прогнозу. Мета і задачі дослідження Структурно-параметрична ідентифікація NARMAX моделі. Розробка алгоритму прогнозування геомагнітного Dst індексу. Створення функціональної структури програмного забезпечення для автоматизації процесу ідентифікації та її реалізація. Об'єкт дослідження – NARMAX моделі для прогнозування космічної погоди. Предмет дослідження – космічна погода. Методи дослідження Математичне моделювання, оптимізація та порівняльний аналіз. Наукова новизна одержаних результатів Запропоновано новий метод структурно-параметричної ідентифікації NARMAX моделей. Він дозволяє покращити точність прогнозу через зменшення ступеню поліному моделі. Також використання цього методу дозволяє збільшити горизонт прогнозу до 5-6 годин. Розроблена NARMAX модель типу «вхід-вихід», що базується на описаному методі та включає у себе прогнозування Dst індексу з використанням даних про швидкість сонячного вітру та південної компоненти магнітного поля. Обґрунтована структура програмного забезпечення для автоматичної структурно- параметричної ідентифікації моделі прогнозування космічної погоди на основі викладених методів. За цією структурою було розроблено програмне забезпечення на основі Python з використанням бібліотек mathplotlib та numpy. Практична цінність одержаних результатів полягає в тому, що результуюча NARMAX модель дозволяє отримувати більш точний прогноз на більшому відрізку часу. Структура та обсяг дисертації. Магістерська дисертація складається з чотирьох розділів. Для вирішення поставленої задачі у першому розділі атестаційної роботи викладено сфери застосування результатів передбачення космічної погоди. Проведено аналіз сучасних способів ідентифікації моделей та існуючих способів прогнозування. Окреслено принципи роботи досліджуваних методів прогнозування та їх ефективність. У другому розділі роботи визначено основні математичні та алгоритмічні засоби, на яких базується викладений метод структурно-параметричної ідентифікації, визначено існуючі джерела експериментальних даних. У третьому розділі обґрунтовуються засоби розробки та розглядаються особливості їх використання. Також описується функціональна структура розробленого програмного забезпечення. Описана логіка його взаємодії з джерелами експериментальних даних та його інтерфейс для отримання результатів роботи. У четвертому розділі подані результуючі NARMAX моделі та порівняльні результати їх роботи. Подане порівняння по ефективності прогнозування з стандартними методами ідентифікації моделі.Actuality of theme Currently, the number of factors that are greatly influenced by space weather is growing rapidly. This growth is due to both the increase in the number of electronic equipment launched into orbit and the growing number of sensitive to electromagnetic oscillations ground equipment, including even conventional power grids. Currently, the methods used to predict space weather have a number of limitations due to low accuracy and limited forecast horizon. The purpose and objectives of the study Structural and parametric identification of the NARMAX model. Development of a geomagnetic Dst index prediction algorithm. Creating a functional structure of software to automate the identification process and its implementation. The object of study - NARMAX models for forecasting space weather. The subject of research - space weather. Research methods Mathematical modeling, optimization and comparative analysis. Scientific novelty of the obtained results A new method of structural-parametric identification of NARMAX models is proposed. It improves the prediction accuracy by reducing the degree of the polynomial model. Also, the use of this method allows you to increase the forecast horizon to 5-6 hours. An NARMAX input-output model has been developed based on the described method and includes Dst index prediction using data on the solar wind speed and the southern component of the magnetic field. The structure of the software for automatic structural-parametric identification of the model of space weather forecasting on the basis of the stated methods is substantiated. Python-based software using the mathplotlib and numpy libraries was developed using this structure. The practical value of the obtained results is that the resulting NARMAX model allows to obtain a more accurate forecast over a longer period of time. The structure and scope of the dissertation. The master's dissertation consists of four sections. To solve this problem in the first section of the certification work outlines the scope of application of the results of space weather forecasting. The analysis of modern methods of model identification and existing methods of forecasting is carried out. The principles of operation of the studied forecasting methods and their efficiency are outlined. In the second section of the work the basic mathematical and algorithmic means on which the stated method of structural-parametric identification is based are defined, the existing sources of experimental data are defined. The third section substantiates the means of development and considers the features of their use. The functional structure of the developed software is also described. The logic of its interaction with experimental data sources and its interface for obtaining work results are described. The fourth section presents the resulting NARMAX models and comparative results of their work. A comparison of forecasting efficiency with standard model identification methods is presented

Maximising Oil Production Through Data Modelling, Simulation and Optimisation.

The research work presented on this thesis provides an alternative tool for characterising oil fields under fluid injection by analysing historical production/injection rates. In particular polynomial and radial basis Non Linear Autoregressive with Exogenous Input Model (NARX) models were developed; these models were capable of capturing the dynamics of an operating field in the North Sea. A Greedy Randomised Adaptive Search Procedure (GRASP) heuristic optimisation method was applied for estimating a future injection strategy. This approach is combined with a risk analysis methodology, a popular approach in financial mathematics. As a result, it is possible to estimate how likely it is to reach a production goal. According to the simulations, it is possible to increase oil production by 10% in one year by implementing a smart injection strategy with low statistical uncertainty. Resulting from this research project, a computational tool was developed. It is now possible to estimate NARX models from any field under fluid injection as well as finding the best future injection scenario

The design of periodic excitations for dynamic system identification

System identification techniques are developed for modelling linear and nonlinear systems. The main results of the work are concerned with the design and utilisation of periodic perturbation signals in general areas of time- and frequency-domain system identification. A design strategy is given for a new class of perturbation signals, together with examples of their use in system identification applications. Signal processing procedures are developed for the practical treatment of drift disturbances and transient effects, and also for the detection of nonlinear contributions to the measurement data. The techniques rely completely on the periodicity of the excitation, and so the advantageous properties of periodic input signals are considered in detail. The use of periodic excitations in discrete- and continuous-time nonlinear system identification is also reported, with the identification methods illustrating the worth of frequency-domain measurements in this area. An automatic tuning procedure for PID controllers is also developed, which illustrates an application of system identification techniques to control problems

Modelos polinomiais narx obtidos através de metaheurísticas com codificação binária

Orientador: Prof. Dr. Gideon Villar LeandroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 24/06/2020Inclui referências: p. 126-137Resumo: Dentro do contexto da identificação de sistemas, a etapa de seleção de estrutura representa uma tarefa complexa por se tratar de um problema de otimização combinatória do tipo binária. Para solucionar este problema, diversas técnicas vêm sendo aplicadas, dentre elas a seleção por meio de metaheurísticas. Entretanto, devido à grande diversidade de metaheurísticas existentes na literatura, a escolha daquela mais adequada para cumprir esta tarefa pode ser algo desafiador para o projetista. Neste trabalho, é realizada uma análise do comportamento de metaheurísticas aplicadas ao problema de seleção de estrutura, além de ser apresentado um novo método de codificação binária, chamado de Modulação em Ângulo Modificada (MAM), que tende a melhorar o desempenho das metaheurísticas neste tipo de problema. Normalmente as metaheurísticas atuam na seleção de estrutura originalmente manipulando soluções binárias ou através de associação com alguma técnica de codificação binária. Foram avaliados os desempenhos das metaheurísticas Algoritmo Genético, Evolução Diferencial e Algoritmo do Morcego aplicadas ao problema de seleção de estrutura de modelos não lineares autorregressivos com entrada externa (NARX, do inglês Nonlinear AutoRegressive with eXogenous input). O Algoritmo Genético já consiste em uma metaheurística projetada para manipular soluções binárias. A Evolução Diferencial e o Algoritmo do Morcego, por sua vez, tiveram suas versões binárias implementadas através das codificações Função de Transferência (TF), Prioridade de Maior Valor (GVP) e Modulação em Ângulo (AM). Além disso, a forma de associação entre as metaheurísticas e a codificação AM foi modificada, dando origem à codificação MAM. Dois estudos de caso foram conduzidos utilizando dados de um conversor buck e de um aquecedor elétrico. Os resultados das simulações mostram que as versões binárias da Evolução Diferencial obtidas com as codificações TF, GVP e MAM foram as que apresentaram melhores desempenho, superando o Algoritmo Genético e o Algoritmo do Morcego. Além disso, levando em conta a convergência das soluções e a capacidade de localizar bons modelos, em todos os cenários analisados o desempenho da Evolução Diferencial codificada com MAM melhorou substancialmente em relação à sua versão original codificada com AM. Os melhores modelos encontrados neste trabalho apresentaram bom desempenho ao serem aplicados métodos de validação (simulação livre e análise de resíduos) e ao serem comparados com modelos da literatura. Palavras-chave: Identificação de sistemas. Seleção de estrutura. Modelo NARX. Metaheurísticas. Codificação binária.Abstract: During a system identification procedure, structure selection represents a complex task because it is a binary combinatorial optimization problem. To solve this problem, several techniques have been applied, the metaheuristics is one of them. However, there is a great diversity of metaheuristics, thus choosing the most suitable one to perform the task is difficult for the designer. In this study, we performed a performance analysis of metaheuristics applied to a structure selection problem. In addition, we present a new binarization technique, called Modified Angle Modulation (MAM), which tends to improve the performance of metaheuristics. Usually, metaheuristics perform the structure selection taking binary solutions directly or through the association with a binarization technique. We evaluated the performances of three metaheuristic techniques, being the Genetic Algorithm, Differential Evolution and Bat Algorithm, all working at a structure selection problem for nonlinear autoregressive models with exogenous input (NARX). The Genetic Algorithm is originally a binary metaheuristics. Binary versions of the Differential Evolution and the Bat Algorithm were developed through the Transfer Function (TF), Great Value Priority (GVP) and Angle Modulation (AM) binarizations. In addition, the form of association between metaheuristics and AM binarization has been modified, originating the MAM binarization. We conducted two case studies using data from a buck converter and an electric heater. Binary versions of the Differential Evolution developed through TF, GVP and MAM binarizations performed better than the Genetic Algorithm and Bat Algorithm. Furthermore, considering the convergence of solutions and the ability to locate good models, the performance of the binary version of the Differential Evolution developed with MAM substantially improved in relation to its original version developed with AM. Finally, the best estimated models performed well not only during validation tests (free run simulation and statistical validation), but also when compared with other models available in the literature. Keywords: System identification. Structure selection. NARX models. Metaheuristics. Binarization

Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization

The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality. Although a large number of NCM estimation methods have been developed, most of them assume the noises to be white. However, in many real-world applications, the noises are colored (e.g., they exhibit temporal autocorrelations), resulting in suboptimal solutions. Here, we introduce a novel brain-inspired algorithm that accurately and adaptively estimates the NCM for dynamic systems subjected to colored noise. Particularly, we extend the Dynamic Expectation Maximization algorithm to perform both online noise covariance and state estimation by optimizing the free energy objective. We mathematically prove that our NCM estimator converges to the global optimum of this free energy objective. Using randomized numerical simulations, we show that our estimator outperforms nine baseline methods with minimal noise covariance estimation error under colored noise conditions. Notably, we show that our method outperforms the best baseline (Variational Bayes) in joint noise and state estimation for high colored noise. We foresee that the accuracy and the adaptive nature of our estimator make it suitable for online estimation in real-world applications.Comment: 62nd IEEE Conference on Decision and Contro